Last updated: 2017-11-06
Code version: 34e4b73
Introduction Below you will find a worked example that helps you understand how to perform model building in multiple regression and analysis of covariance in R.
We are interested in the diastolic blood pressure (y) for people on two different treatments (group). Within the two treatments different dosages were given. First type in the data (or Copy-Paste):
y <- c(87,86.5,89,88.5,87.5,88,86.5,87,85,86,85,83)
dose <- c(5,6,7,8,9,10,5,6,7,8,9,10)
group <- c(0,0,0,0,0,0,1,1,1,1,1,1)
Letâ€™s take a look at a plot of the data:
interaction.plot(dose, group, y , mean, ylab = "Bloodpressure")
Use
help(interaction.plot)
to see how it works. R uses “:” to denote an interaction so group:dose is the R interaction term in a model.
It looks like there may be an interaction in the data: group 0 has higher blood pressure levels than group 1 in the higher dosages. If there is an explanatory variable in the data file that is categorical (other than 0-1), then you should tell R this by using the function factor(). So factor(group) tells R that group is not a numeric variable but that its numbers should be used as group labels. To fit an ANOVA model you can use either of the following:
model.an <- lm(y~group)
model.an <- glm(y~group, family = gaussian)
In the second statement, the
family=gaussian
part may be left out since the gaussian (normal) is the default (â€œGLMâ€ stands for generalized linear model, of which ANOVA, linear regression, and logistic regression models are special cases). The result of the ANOVA model will be stored in model.an. In the second case, this will be an object of glm-type because you used glm to create it. To see what is in it usenames(model.an)
and if you want to see something specific use for instancemodel.an$coefficients
. To get the table with the estimates:
summary(model.an)
Call:
glm(formula = y ~ group, family = gaussian)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4167 -0.5000 0.0000 0.8333 1.5833
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 87.7500 0.4930 177.989 < 2e-16 ***
group -2.3333 0.6972 -3.347 0.00741 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1.458333)
Null deviance: 30.917 on 11 degrees of freedom
Residual deviance: 14.583 on 10 degrees of freedom
AIC: 42.394
Number of Fisher Scoring iterations: 2
The function
drop1(fit, test = "F")
looks at the variables in the model â€˜fitâ€™, then leaves out the terms one by one and calculates the F-test for every term if it were to be left out. Of course, inmodel.an
there is only one variable so you just get one test. Note that the p-value of the F-test is exactly the same as the p-value of the t-test in the model summary. To fit the model without the interaction:
model.anc <- glm(y~group + dose, family = gaussian)
summary(model.anc)
Call:
glm(formula = y ~ group + dose, family = gaussian)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8809 -0.7143 0.3095 0.8036 1.2619
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.3571 1.5992 55.876 9.48e-13 ***
group -2.3333 0.6933 -3.366 0.00831 **
dose -0.2143 0.2030 -1.056 0.31858
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1.441799)
Null deviance: 30.917 on 11 degrees of freedom
Residual deviance: 12.976 on 9 degrees of freedom
AIC: 42.993
Number of Fisher Scoring iterations: 2
drop1(model.anc, test = "F")
Single term deletions
Model:
y ~ group + dose
Df Deviance AIC F value Pr(>F)
<none> 12.976 42.993
group 1 29.309 50.771 11.3284 0.008313 **
dose 1 14.583 42.394 1.1147 0.318583
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The column “Deviance” contains the residual sums of squares for different models. The first line gives the residual sums of squares if none of the terms is dropped so for the model with both group and dose in it. The second line gives the residual sum of squares for the model without group so for the model with only dose in it. The difference in these residual sums of squares gives the sum of squares for the group: 29.310-12.976 = 16.334. In the same way the sum of squares for dose can be obtained (14.583-12.976 = 1.607). For dose and group the F-values and the p-values are shown. With this information an ANOVA table could be constructed.
This somewhat elaborate method is simplified by using the Anova function in the library car:
library(car) #Note: you might have to install this library first, using Packages, Install packages
Warning: package 'car' was built under R version 3.4.2
Anova(model.anc, test.statistic = "F")
Analysis of Deviance Table (Type II tests)
Response: y
Error estimate based on Pearson residuals
SS Df F Pr(>F)
group 16.3333 1 11.3284 0.008313 **
dose 1.6071 1 1.1147 0.318583
Residuals 12.9762 9
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The model with an interaction can be fitted as: (or the exact same model can be given by:)
model.int <- glm(y~group + dose + group:dose, family=gaussian)
model.int <- glm(y~group*dose, family = gaussian)
Note that if you now use the drop1 function, only the interaction will be evaluated for possible dropping:
drop1(model.int, test = "F")
Single term deletions
Model:
y ~ group * dose
Df Deviance AIC F value Pr(>F)
<none> 6.5476 36.785
group:dose 1 12.9762 42.993 7.8545 0.02311 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(model.int, test.statistic = "F")
Analysis of Deviance Table (Type II tests)
Response: y
Error estimate based on Pearson residuals
SS Df F Pr(>F)
group 16.3333 1 19.9564 0.002091 **
dose 1.6071 1 1.9636 0.198705
group:dose 6.4286 1 7.8545 0.023105 *
Residuals 6.5476 8
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
R does this because it makes little sense to drop a main effect while the interaction is still in the model; generally, one first checks whether the interaction can be removed. The interaction term is statistically significant, so the trend in blood pressure over the dosages is different for the two treatment groups.
**this should be somewhere else, as full
does not exist yet > Checking Multicollinearity (used on day 10) The variance inflation factor can be obtained using the vif() function in the car package. The argument to the vif() function is a model you have already fit.
vif(full) temp factories population wind rainfall daysrain 3.763553 14.703175 14.340318 1.255460 3.404904 3.443932
To get the tolerance instead, you can invert the VIF: 1/vif(full)
R does not have the same type of forward, backward and stepwise selection procedures as SPSS. The add1 and drop1 can be used to examine variables and decide which variable should be added/dropped next. The actual adding and dropping is not done automatically and needs to be done by the analyst, so a new model is fitted and again checked for variables that can be added/dropped. Note that the add1 and drop1 functions both give Akaikeâ€™s Information Criterion (AIC, to be treated during Modern Methods in Data Analysis) by default; an F-test can be obtained by using the option test=“F” in the command.
This is a repeat of exercise #1, but now in R. Compare the results with those obtained in SPSS. The dataset with SO2 data from the lecture is available in the dataset so2.RData. Try to repeat the findings from the lecture notes using R. How will you do the variable selection in R?
load(amstR::fromParentDir("data/so2.RData"))
str(so2)
'data.frame': 41 obs. of 9 variables:
$ city : Factor w/ 41 levels "Albany ",..: 7 33 30 9 32 15 38 4 1 41 ...
$ SO2 : num 110 94 69 65 61 56 56 47 46 36 ...
$ temp : num 50.6 50 54.6 49.7 50.4 49.1 55.9 55 47.6 54 ...
$ factories : num 3344 343 1692 1007 347 ...
$ population: num 3369 179 1950 751 520 ...
$ wind : num 10.4 10.6 9.6 10.9 9.4 9 9.5 9.6 8.8 9 ...
$ rainfall : num 34.4 42.8 39.9 35 36.2 ...
$ daysrain : num 122 125 115 155 147 127 105 111 135 114 ...
$ region : Factor w/ 3 levels "Southeast","Northeast",..: 3 2 2 3 2 2 3 1 2 2 ...
- attr(*, "variable.labels")= Named chr "name of city" "SO2 (microgr/cubic m)" "mean yearly temp (degrees F)" "number of factories > 20 employees" ...
..- attr(*, "names")= chr "city" "SO2" "temp" "factories" ...
- attr(*, "codepage")= int 1252
First some histograms of the numeric variables
# find out which variables in so2 are numeric
num_vars <- colnames(so2)[sapply(so2, is.numeric)]
# how many are there
length(num_vars)
[1] 7
par(mfrow = c(2, 4))
for (variable in num_vars) {
hist(so2[[variable]], main = variable, xlab = variable)
}
par(mfrow = c(1, 1))
Then the pairwise scatterplots
pairs(so2)
Correlation plot of all numeric variables (this can be done nicely with the package corrplot
)
cor_matrix <- cor(so2[, num_vars])
corrplot::corrplot(cor_matrix, method = "number")
Now for the model building. We can use the step
function for variable selection. Note that it uses the Akaike Information criterion for variable selection. NB we must remove the city
variable, as this is merely a label of the observations, and is unique for each row.
model_vars <- setdiff(colnames(so2), "city") # take all colnames of so2, remove 'city' from these names
fit0 <- lm(SO2~1, data = so2[, model_vars])
fit_full <- lm(SO2~., data = so2[, model_vars]) # use ~. to include all variables except for SO2
steps_forward <- step(fit0, scope = list(lower = fit0, upper = fit_full), direction = "forward")
Start: AIC=259.76
SO2 ~ 1
Df Sum of Sq RSS AIC
+ factories 1 9161.7 12876 239.73
+ population 1 5373.2 16665 250.31
+ temp 1 4143.3 17895 253.23
+ region 2 4253.5 17784 254.97
+ daysrain 1 3009.9 19028 255.74
<none> 22038 259.76
+ wind 1 197.6 21840 261.40
+ rainfall 1 64.9 21973 261.64
Step: AIC=239.73
SO2 ~ factories
Df Sum of Sq RSS AIC
+ population 1 3759.5 9116.6 227.58
+ region 2 4158.9 8717.3 227.74
+ temp 1 2212.3 10663.8 234.00
+ daysrain 1 1816.1 11060.0 235.50
<none> 12876.2 239.73
+ rainfall 1 124.8 12751.4 241.33
+ wind 1 80.6 12795.6 241.47
Step: AIC=227.58
SO2 ~ factories + population
Df Sum of Sq RSS AIC
+ region 2 2708.58 6408.1 217.12
+ daysrain 1 684.97 8431.7 226.37
+ temp 1 577.98 8538.7 226.89
<none> 9116.6 227.58
+ rainfall 1 148.34 8968.3 228.90
+ wind 1 146.93 8969.7 228.91
Step: AIC=217.12
SO2 ~ factories + population + region
Df Sum of Sq RSS AIC
+ temp 1 472.28 5935.8 215.98
<none> 6408.1 217.12
+ wind 1 167.46 6240.6 218.04
+ daysrain 1 104.02 6304.0 218.45
+ rainfall 1 25.70 6382.3 218.96
Step: AIC=215.98
SO2 ~ factories + population + region + temp
Df Sum of Sq RSS AIC
+ wind 1<PHONE_NUMBER>.0 215.78
<none> 5935.8 215.98
+ daysrain 1 0.276 5935.5 217.98
+ rainfall 1 0.040 5935.7 217.98
Step: AIC=215.78
SO2 ~ factories + population + region + temp + wind
Df Sum of Sq RSS AIC
<none> 5626.0 215.78
+ rainfall 1 49.183 5576.8 217.43
+ daysrain 1 3.753 5622.2 217.76
steps_backward <- step(fit_full, data = so2[, model_vars], direction = "backward")
Start: AIC=219.29
SO2 ~ temp + factories + population + wind + rainfall + daysrain +
region
Df Sum of Sq RSS AIC
- daysrain 1 18.9 5576.8 217.43
- rainfall 1 64.3 5622.2 217.76
<none> 5557.9 219.29
- wind 1 377.5 5935.4 219.98
- temp 1 443.3 6001.2 220.43
- population 1 1154.9 6712.8 225.03
- region 2 1725.4 7283.3 226.37
- factories 1 3313.9 8871.8 236.46
Step: AIC=217.42
SO2 ~ temp + factories + population + wind + rainfall + region
Df Sum of Sq RSS AIC
- rainfall 1 49.2 5626.0 215.78
<none> 5576.8 217.43
- wind 1 358.9 5935.7 217.98
- temp 1 662.8 6239.6 220.03
- population 1 1160.0 6736.8 223.17
- region 2 1728.6 7305.4 224.50
- factories 1 3316.6 8893.4 234.56
Step: AIC=215.78
SO2 ~ temp + factories + population + wind + region
Df Sum of Sq RSS AIC
<none> 5626.0 215.78
- wind 1 309.8 5935.8 215.98
- temp 1 614.6 6240.6 218.04
- population 1 1247.5 6873.5 222.00
- region 2 2464.8 8090.8 226.68
- factories 1 3589.2 9215.2 234.02
steps_stepwise <- step(fit0, scope = list(upper = fit_full), data = so2[, model_vars], direction = "both")
Start: AIC=259.76
SO2 ~ 1
Df Sum of Sq RSS AIC
+ factories 1 9161.7 12876 239.73
+ population 1 5373.2 16665 250.31
+ temp 1 4143.3 17895 253.23
+ region 2 4253.5 17784 254.97
+ daysrain 1 3009.9 19028 255.74
<none> 22038 259.76
+ wind 1 197.6 21840 261.40
+ rainfall 1 64.9 21973 261.64
Step: AIC=239.73
SO2 ~ factories
Df Sum of Sq RSS AIC
+ population 1 3759.5 9116.6 227.58
+ region 2 4158.9 8717.3 227.74
+ temp 1 2212.3 10663.8 234.00
+ daysrain 1 1816.1 11060.0 235.50
<none> 12876.2 239.73
+ rainfall 1 124.8 12751.4 241.33
+ wind 1 80.6 12795.6 241.47
- factories 1 9161.7 22037.9 259.76
Step: AIC=227.58
SO2 ~ factories + population
Df Sum of Sq RSS AIC
+ region 2 2708.6 6408.1 217.12
+ daysrain 1 685.0 8431.7 226.37
+ temp 1 578.0 8538.7 226.89
<none> 9116.6 227.58
+ rainfall 1 148.3 8968.3 228.90
+ wind 1 146.9 8969.7 228.91
- population 1 3759.5 12876.2 239.73
- factories 1 7548.0 16664.7 250.31
Step: AIC=217.12
SO2 ~ factories + population + region
Df Sum of Sq RSS AIC
+ temp 1 472.3 5935.8 215.98
<none> 6408.1 217.12
+ wind 1 167.5 6240.6 218.04
+ daysrain 1 104.0 6304.0 218.45
+ rainfall 1 25.7 6382.3 218.96
- region 2 2708.6 9116.6 227.58
- population 1 2309.3 8717.3 227.74
- factories 1 5478.7 11886.7 240.45
Step: AIC=215.98
SO2 ~ factories + population + region + temp
Df Sum of Sq RSS AIC
+ wind 1 309.8 5626.0 215.78
<none> 5935.8 215.98
- temp 1 472.3 6408.1 217.12
+ daysrain 1 0.3 5935.5 217.98
+ rainfall 1 0.0 5935.7 217.98
- population 1 1347.9 7283.6 222.37
- region 2 2602.9 8538.7 226.89
- factories 1 3659.7 9595.4 233.67
Step: AIC=215.78
SO2 ~ factories + population + region + temp + wind
Df Sum of Sq RSS AIC
<none> 5626.0 215.78
- wind 1 309.8 5935.8 215.98
+ rainfall 1 49.2 5576.8 217.43
+ daysrain 1 3.8 5622.2 217.76
- temp 1 614.6 6240.6 218.04
- population 1 1247.5 6873.5 222.00
- region 2 2464.8 8090.8 226.68
- factories 1 3589.2 9215.2 234.02
The function by default prints all the steps. I do not know how to stop this behaviour
Look at the final models:
summary(steps_forward)
Call:
lm(formula = SO2 ~ factories + population + region + temp + wind,
data = so2[, model_vars])
Residuals:
Min 1Q Median 3Q Max
-33.912 -7.646 0.102 5.037 39.960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 84.66810 28.26707 2.995 0.00509 **
factories 0.06387 0.01371 4.657 4.76e-05 ***
population -0.03638 0.01325 -2.746 0.00958 **
regionNortheast 12.63719 6.92759 1.824 0.07692 .
regionMidwest/West -8.35751 5.55350 -1.505 0.14158
temp -0.71117 0.36900 -1.927 0.06234 .
wind -2.17945 1.59285 -1.368 0.18020
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.86 on 34 degrees of freedom
Multiple R-squared: 0.7447, Adjusted R-squared: 0.6997
F-statistic: 16.53 on 6 and 34 DF, p-value: 8.184e-09
summary(steps_backward)
Call:
lm(formula = SO2 ~ temp + factories + population + wind + region,
data = so2[, model_vars])
Residuals:
Min 1Q Median 3Q Max
-33.912 -7.646 0.102 5.037 39.960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 84.66810 28.26707 2.995 0.00509 **
temp -0.71117 0.36900 -1.927 0.06234 .
factories 0.06387 0.01371 4.657 4.76e-05 ***
population -0.03638 0.01325 -2.746 0.00958 **
wind -2.17945 1.59285 -1.368 0.18020
regionNortheast 12.63719 6.92759 1.824 0.07692 .
regionMidwest/West -8.35751 5.55350 -1.505 0.14158
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.86 on 34 degrees of freedom
Multiple R-squared: 0.7447, Adjusted R-squared: 0.6997
F-statistic: 16.53 on 6 and 34 DF, p-value: 8.184e-09
summary(steps_stepwise)
Call:
lm(formula = SO2 ~ factories + population + region + temp + wind,
data = so2[, model_vars])
Residuals:
Min 1Q Median 3Q Max
-33.912 -7.646 0.102 5.037 39.960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 84.66810 28.26707 2.995 0.00509 **
factories 0.06387 0.01371 4.657 4.76e-05 ***
population -0.03638 0.01325 -2.746 0.00958 **
regionNortheast 12.63719 6.92759 1.824 0.07692 .
regionMidwest/West -8.35751 5.55350 -1.505 0.14158
temp -0.71117 0.36900 -1.927 0.06234 .
wind -2.17945 1.59285 -1.368 0.18020
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.86 on 34 degrees of freedom
Multiple R-squared: 0.7447, Adjusted R-squared: 0.6997
F-statistic: 16.53 on 6 and 34 DF, p-value: 8.184e-09
This is a repeat of exercise #2, but now in R. Compare the results with those obtained in SPSS. The workspace cigarette.RData contains a dataset cigarettte with data on carbon monoxide, tar and nicotine contents and weight of 25 brands of cigarettes. We want to predict the carbon monoxide contents using the other 3 variables. a. Make a scatter plot matrix of the 4 variables, and formulate which variables you expect to predict (part of) carbon monoxide content.
NB the RData
file did not seem to contain any data, so we imported the SPSS file with package foreign
amstR::fromParentDir("data/cigarette.RData")
cigarette <- foreign::read.spss(amstR::fromParentDir("data/cigarette.sav"))
re-encoding from CP1252
cigarette <- as.data.frame(cigarette)
# save file:
# save(cigarette, file = amstR::fromParentDir("data/cigarette2.RData"))
pairs(cigarette)
Nicotine looks highly correlated with tar and carbonmonoxide. Carbonmonoxide looks highly correlated with tar too.
- Now make a correlation matrix of the 4 variables, and check your expectations from a.
corrplot::corrplot(cor(cigarette), method = "number")
- Build a regression model with all 3 predictor variables. Are all variables significant? Are regression coefficients what you would expect? Can you think of an explanation?
fit_full <- lm(carbonmonoxide~., data = cigarette)
summary(fit_full)
Call:
lm(formula = carbonmonoxide ~ ., data = cigarette)
Residuals:
Min 1Q Median 3Q Max
-2.89261 -0.78269 0.00428 0.92891 2.45082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2022 3.4618 0.925 0.365464
tar 0.9626 0.2422 3.974 0.000692 ***
nicotine -2.6317 3.9006 -0.675 0.507234
weight -0.1305 3.8853 -0.034 0.973527
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.446 on 21 degrees of freedom
Multiple R-squared: 0.9186, Adjusted R-squared: 0.907
F-statistic: 78.98 on 3 and 21 DF, p-value: 1.329e-11
Only tar is significant. If the explanatory variables were independent, we would expect that nicotine was alsa correlated with carbonmonoxide. However, due to coliniearity, the effect of nicotine vanishes when tar is included in the model.
- Using backward selection reduce the model from c until it contains only significant variables. Which variable(s) are in the final model? Which proportion of the variation in carbon monoxide content is explained by this model?
Let’s do manual backward selection
fit_full <- lm(carbonmonoxide~., data = cigarette)
summary(fit_full)
Call:
lm(formula = carbonmonoxide ~ ., data = cigarette)
Residuals:
Min 1Q Median 3Q Max
-2.89261 -0.78269 0.00428 0.92891 2.45082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2022 3.4618 0.925 0.365464
tar 0.9626 0.2422 3.974 0.000692 ***
nicotine -2.6317 3.9006 -0.675 0.507234
weight -0.1305 3.8853 -0.034 0.973527
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.446 on 21 degrees of freedom
Multiple R-squared: 0.9186, Adjusted R-squared: 0.907
F-statistic: 78.98 on 3 and 21 DF, p-value: 1.329e-11
Use drop1
to determine which variable to drop first. Remove the coefficient with the highest p-value
drop1(fit_full, test = "F")
Single term deletions
Model:
carbonmonoxide ~ tar + nicotine + weight
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 43.893 22.072
tar 1 33.001 76.894 34.089 15.7892 0.0006921 ***
nicotine 1 0.951 44.844 20.608 0.4552 0.5072343
weight 1 0.002 43.895 20.073 0.0011 0.9735268
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit_1 <- lm(carbonmonoxide~tar+nicotine, data = cigarette)
drop1(fit_1, test = "F")
Single term deletions
Model:
carbonmonoxide ~ tar + nicotine
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 43.895 20.073
tar 1 33.000 76.894 32.089 16.5393 0.0005124 ***
nicotine 1 0.974 44.869 18.622 0.4882 0.4920350
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Drop the next variable
fit_2 <- lm(carbonmonoxide ~ tar, data = cigarette)
drop1(fit_2, test = "F")
Single term deletions
Model:
carbonmonoxide ~ tar
Df Sum of Sq RSS AIC F value Pr(>F)
<none> 44.87 18.622
tar 1 494.28 539.15 78.778 253.37 6.552e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Now we cant remove anymore predictors, because ‘all’ are significant. Check assumptions of homoscedasticity and normal distribution of residuals:
plot(fit_2, which = c(1,2))
Residuals look pretty OK. Homoscedasticity is a little hard to judge, but at least there is no clear funnel shape.
- Based on the backward selection model, what is the predicted carbon monoxide content of a cigarette with tar = 13.0, nicotine = 1.0 and weight = 1.0? What is its 95% prediction interval, and how do you interpret this? (Use the R function predict to obtain predictions for new data based on the model:
new <- data.frame(tar=13.0, nicotine=1.0, weight=1.0)
predict(fit_2, newdata=new, interval="prediction", level=0.95)
fit lwr upr
1 13.15597 10.20828 16.10365
This is a repeat of exercise #4, but now in R. Compare the results with those obtained in SPSS. The variables in the study of 38 stream sites in New York state by Lovett et al. (2000) fell into two groups measured at different spatial sites â€“ watershed variables (elevation, stream length and area) and chemical variables for a site averaged across sampling dates (averaged over 3 years). We use only the chemical variables. The data are given in the data file stream.RData
STREAM name of the stream (site) from which observations were collected MAXELEV maximum elevation of stream (m above sea level) SAMPELEV site elevation (m above sea level) LENGTH length of stream AREA area of watershed NO3 concentration (mmol/L) of nitrogen oxide ions TON concentration (mmol/L) of total organic nitrogen TN concentration (mmol/L) of total nitrogen NH4 concentration (mmol/L) of ammonia ions DOC concentration (mmol/L) of dissolved oxygen SO4 concentration (mmol/L) of sulphur dioxide ions CL concentration (mmol/L) of chloride ions CA concentration (mmol/L) of calcium ions MG concentration (mmol/L) of magnesium ions H concentration (mmol/L) of hydrogen ions
Which of the chemical variables can predict the maximum elevation of the stream? Lovett et al. have used the log of the variables DOC, CL and H in their analyses.
All variables are numeric, except for STREAM
, which is the name of the site. Lets remove this variable to make our lives easier
streams <- stream$STREAM
stream$STREAM <- NULL
car::scatterplotMatrix(stream[, c(1:7)], diagonal = "histogram")
car::scatterplotMatrix(stream[, c(8:14)], diagonal = "histogram")
It looks like LENGTH
and AREA
are tightly correlated, like NO3
and TN
, also SO4
and MG
. Note that many scatterplots were not included due to the limited plot area.
We can formalize this by sorting the correlations
# create correlation matrix
cor_matrix <- cor(stream)
# to remove the uninformative diagonal, and duplicity, retain only upper triangle
cor_matrix[lower.tri(cor_matrix, diag = T)] <- NA
# to analyze this, 'melt' the data to a conveniant format
cor_melted <- data.table::melt(cor_matrix, value.name = "correlation")
# remove the NA values
cor_melted <- cor_melted[!is.na(cor_melted$correlation),]
head(cor_melted)
Var1 Var2 correlation
15 MAXELEV SAMPELEV 0.3711472
29 MAXELEV LENGTH 0.3423315
30 SAMPELEV LENGTH -0.2593533
43 MAXELEV AREA 0.3043967
44 SAMPELEV AREA -0.3109707
45 LENGTH AREA 0.9014002
# add a column with absolute correlation
cor_melted$abs_cor <- abs(cor_melted$correlation)
# sort by that column
cor_melted[order(cor_melted$abs_cor, decreasing = T), ][1:10,]
Var1 Var2 correlation abs_cor
89 NO3 TN 0.9828672 0.9828672
45 LENGTH AREA 0.9014002 0.9014002
178 SO4 MG 0.7413800 0.7413800
194 CA H -0.7189951 0.7189951
170 SAMPELEV MG -0.6456606 0.6456606
57 MAXELEV NO3 0.5896226 0.5896226
85 MAXELEV TN 0.5745820 0.5745820
142 SAMPELEV CL -0.5620794 0.5620794
179 CL MG 0.5491171 0.5491171
145 NO3 CL -0.4999713 0.4999713
Zoom in on only DOC
, CL
and H
car::scatterplotMatrix(stream[, c("MAXELEV", "DOC", "CL", "H")], diagonal = "histogram")
These 3 variables are right skewed, which is probably why they were log-transformed
Look at the transformed variables
require(dplyr)
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:car':
recode
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
stream %>%
mutate(DOC = log(DOC),
CL = log(CL),
H = log(H)) %>%
select(c(MAXELEV, DOC, CL, H)) %>%
car::scatterplotMatrix(diagonal = "histogram")
The distributions look a little nicer now. However, for linear regression, normality of the independent variables is not assumed, only a linear relation between the independent variables and the dependent variable.
To answer the question which variables predict the elevation, lets use the step
function with stepwise selection.
Of the variable pairs that were tightly correlated, in none of the cases both variables were included.
Check assumptions of the model
plot(fit_step, which = c(1,2))
Residuals are a little skewed, not too much heteroscedasticity.
sessionInfo()
R version 3.4.1 (2017-06-30)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale:
[1] LC_COLLATE=Dutch_Netherlands.1252 LC_CTYPE=Dutch_Netherlands.1252
[3] LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C
[5] LC_TIME=Dutch_Netherlands.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2 dplyr_0.7.3 car_2.1-5
loaded via a namespace (and not attached):
[1] Rcpp_0.12.12 survcomp_1.27.0 bindr_0.1
[4] plyr_1.8.4 compiler_3.4.1 nloptr_1.0.4
[7] RColorBrewer_1.1-2 git2r_0.19.0 tools_3.4.1
[10] corrplot_0.84 digest_0.6.12 lme4_1.1-14
[13] tibble_1.3.4 evaluate_0.10.1 nlme_3.1-131
[16] lattice_0.20-35 mgcv_1.8-17 pkgconfig_2.0.1
[19] rlang_0.1.2 Matrix_1.2-10 yaml_2.1.14
[22] parallel_3.4.1 SparseM_1.77 prodlim_1.6.1
[25] bootstrap_2017.2 amstR_0.1.3 stringr_1.2.0
[28] knitr_1.17 SuppDists_1.1-9.4 MatrixModels_0.4-1
[31] rprojroot_1.2 grid_3.4.1 nnet_7.3-12
[34] glue_1.1.1 data.table_1.10.4-2 R6_2.2.2
[37] survival_2.41-3 foreign_0.8-69 rmarkdown_1.6
[40] rmeta_2.16 lava_1.5.1 minqa_1.2.4
[43] reshape2_1.4.2 magrittr_1.5 survivalROC_1.0.3
[46] backports_1.1.0 htmltools_0.3.6 MASS_7.3-47
[49] splines_3.4.1 assertthat_0.2.0 pbkrtest_0.4-7
[52] quantreg_5.34 KernSmooth_2.23-15 stringi_1.1.5
This R Markdown site was created with workflowr


Host
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Vasyl Lomachenko denies he asked to be made WBC 'franchise' champion
Vasyl Lomachenko has denied he made a request to the WBC to become a ‘franchise’ champion.
The Ukrainian talent was given the title in October by the WBC, which means he is not be subject to strict mandatory challengers obligations.
It also means Lomachenko will be recognised as a WBC champion throughout his career in every fight and once he is retired he will be inducted as an ‘emeritus’ champion.
The creation of the WBC’s ‘franchise’ champions has prompted a backlash from boxing fans and fighters and Lomachenko has now insisted he never asked for the title.
He said: ‘I read an interview with [WBC president] Mauricio Sulaiman, he said we filed a request to be awarded the ‘franchise’ title.
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‘I would like to make it clear I did not file anything and no-one asked for it. When I read that, I was very surprised.’
Lomachenko’s confirmation as ‘franchise’ champion meant Devin Haney became the new WBC lightweight champion, a title he successfully defended against Alfredo Santiago earlier this month.
Middleweight superstar Canelo Alvarez was the first to be made a ‘franchise’ champion, but heavyweight WBC belt holder Deontay Wilder insists he would not accept the title.
‘Many people ask me about the ‘franchise’ belt. I don’t know much about it,’ Wilder said.
‘What I’m hearing, it allows you to stay a champion, you don’t have to fight mandatories and stuff like that. If that’s the case, I’m not interested.
‘I want the mandatories. Guys work their way up, work hard to get to number two, and then become a mandatory.
‘Why would you crown someone that doesn’t have to have mandatories?’


| Working on |
FeedMe |
Group Project -> Personal Project |
Java(Springboot), Angular, Bootstrap, JQuery, AJAX, Web Socket, Java Socket, Swift, MySQL, Amazon AWS |
| Working on |
My Shift |
Personal Project |
Node.js, Angular, Bootstrap, JQuery, AJAX, MySQL |
| Jan 2017 |
UI design to web |
Freelancer |
HTML5, CSS |
| Nov 2016 |
Back-end API implement with PHP |
Freelancer |
PHP |
| Nov 2016 |
Picture Gallery |
Freelancer |
JQuery, Flask, JS, AJAX, Bootstrap, HTML5, CSS |
| Oct 2016 |
Spring Roll |
Freelancer |
Bootstrap, HTML5, CSS |
| Sep 2016 |
Patients Visit (Single Page App) |
Code Challenge |
JQuery, Bootstrap, LESS, JS, HTML5, CSS |
| May 2016 |
Photo Post Cards |
Freelancer |
Bootstrap, HTML5, CSS |


Components are the building blocks of XFramework. A component is a part of an application that can be abstracted as an independent unit and can be reused throughout the application. Components can be nested in one another, can have customized presentations depending on the device it is used on. You can create as many device profiles as you need and customize whole application in any way for each of them.
A component separates logic from presentation, which are stored as separate files and are loaded only when the component needs to be rendered, which helps to save on load times and device resources. Though component caching mechanism allows to prefetch component parts when developer deems it necessary.
To use a component a developer places a marker where the component will be rendered. The marker is a
See an example of two nested components declared in the page markup:

This content will be shown while component is loading...

Such an approach lets a developer build asynchronuous UIs and avoid loading extraneous resources.
There's a way to customize the component with the starting options from outside:

This content will be shown while component is loading...

You can use XF.setOptionsByID
method in the place you decided but before component instance was created.


University of California Irvine, B.S. Computer Science, Class of 2016
U.S Citizen, California Resident
| Sales Engineer, ID TECH Products |
January 2017 - Present |
- Lead multiple technical projects through certification and deployment by providing dedicated hardware/software integration support
- Collaborate with Sales to identify, qualify, and support key customer integration projects from implementation to production
- Scope, design, and drive critical features with product management/engineering to satisfy market requirements and customer needs
- Create product integration guides and technical resources that detail best implementation practices for customer development
- Assist Key Administrators in cryptographic key generation and injection, allowing customers to deploy secure encrypted solutions
|
| Project Manager, The Portal |
February 2015 - January 2017 |
- Utilized JIRA/Confluence to define clear user stories and drive customer features across product development lifecycle
- Supervised documentation of project requirements, customer assets, and development practices to increase engineering productivity
- Collaborated with project stakeholders to understand technical requirements, communicate project timeline, and manage scope
- Lead daily scrums with engineering teams to assess development priorities, identify difficulties, and resolve roadblocks
|
| Device Configuration Tool (ID TECH Products) - Sales Engineer |
September 2017 - Present |
- Engineer a web-based device configuration utility to easily configure and update EMV devices at points of distribution
- Defined product requirements and stakeholders, developed web tool in HTML/CSS/JavaScript to interface with web driver
|
| Production Key Injection (ID TECH Products) - Sales Engineer |
May 2017 - August 2017 |
- Troubleshoot encryption/injection issues with injection facilities/distribution, directed key transfer/loading procedures
- Validated and improved documentation for usage and commissioning hardware security modules (HSMs)
|
| Android Phone Profile Database (ID TECH Products) - Sales Engineer |
February 2017 - Present |
- Engineered a cloud solution that reduces audiojack reader connectivity overhead by automating a manual troubleshooting procedure
- Developed an administrative panel that provides android phone connectivity diagnostics and aggregates profile statistics
|
| NotaryCam (The Portal) - Project Manager |
May 2016 - January 2017 |
- Lead team of 4 engineers to implement solutions for video recording, conferencing, and applying e-signatures
- Expanded client's customer base through architecting and supervising development of an e-signing gateway solution
- Collaborated with client to implement technical requirements, give regular progress updates, and provide technical support
|
| WineKey (The Portal) - Project Manager |
February 2016 - September 2016 |
- Lead team of 5 engineers to develop an educational phone app for sommeliers, utilizing web cross platform technologies
- Coordinated with client to understand application requirements and to release application to both iTunes and Google Play
- Supervised quality assurance pipeline and application documentation through JIRA / Confluence to increase development quality
|
| Vice President, Video Game Development Club (VGDC) @ UCI |
May 2014 - June 2016 |
- Directed game projects a club of 100+ weekly participants through rapid development project prototyping/iteration
- Lead workshops covering topics such as versioning with Git, intro web development (HTML/CSS), and Unity3D
- Collaborated with game industry professionals and UCI faculty to hold tech seminars, almunni panels, and networking events
|
Issue Management: Proficient with JIRA/Confluence
Spoken Languages: English (Native), Mandarin Chinese
Version Control: Familiar with Git, SVN, Perforce
Databases: Familiar with SQL, MongoDB (noSQL)
Languages: Familiar with JavaScript, HTML/CSS, gC#, C++, Java, Python, XML
FrameWorks: Familiar with Angular.js, Express, Ionic FrameWork, Materialize
Payments: Familiar with PCI-PTS/DSS, E2E/P2PE, EMV, DUKPT


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macOS Big Sur changelog
Version 1.0.3
Updated
Dec 9, 2020
Features
fixed a small visual issue with the masks setting
Version 1.0.2
Updated
Dec 8, 2020
Features
new icons added
Version 1.0.1
Updated
Dec 6, 2020
Features
icons added, fixed the text size of the springboard calendar icon date and day
Version 1.0.0
Updated
Dec 4, 2020
Features
Initial Release


WITNESS LIST Finance Committee January 27, 1999-9:00A Bd. of Architectural Examiners On: Ellinger, Steve (Board of Architectural Examiners), Abilene Greer, John O. (Board of Architectural Examiners), Bryan Hendricks, Cathy (Board of Architectural Examiners), Austin Turner, Cleve (Board of Architectural Examiners), Amarillo Registering, but not testifying: On: Briseno-Boxe, Christene (Self), Austin Conner, Kevin (Board of Architectural Examiners), Austin Detke, Paul (Self), Austin Dorchester, E.D. (Board of Architectural Examiners), Midland Nyfeler, John (Board of Architectural Examiners), Austin Reece, Daniel (Self), Austin Solis, Heather (Self), Austin Steinbrueck, Diane (Board of Architectural Examiners), Austin Bd. of Barber Examiners On: Brown, Will K. (State Board of Barber Examiners), Austin Kukendall, William (State Board of Barber Examiners), Austin Registering, but not testifying: On: Almasov, Deborah (State Board of Barber Examiners), Austin Bd. of Plumbing Examiners On: Conrad, Doretta A. (State Board of Plumbing Examiners), Austin Morrow, Jr., Dewitt J. (State Board of Plumbing Examiners), Houston Registering, but not testifying: On: Fowler, Jim (State Board of Plumbing Examiners), Austin Bd. of Public Accountancy On: Conaway, K. Michael (State Board of Public Accountancy), Midland Registering, but not testifying: On: Davis, Jerry A. (State Board of Public Accountancy), Houston Hermanson, Alan (State Board of Public Accountancy), Austin Mason, Jimmie L. (State Board of Public Accountancy), Lubbock Treacy, William (State Board of Public Accountancy), Austin Bd. of Reg. for Prof. Engineers On: Speed, John R. (Texas Board of Professional Engineers), Austin Treat, Herb (Texas Board of Professional Engineers), Austin Registering, but not testifying: On: Jones, Joe Paul (Texas Board of Professional Engineers), Ft. Worth Warrington, Randi (Texas Board of Professional Engineers), Austin Bd. of Veterinary Medical Examiners On: Allen, Ron (Board of Veterinary Medical Examiners), Austin Registering, but not testifying: On: Alexander, Karen (Board of Veterinary Medical Examiners), Austin Matthews, Sharon (Board of Veterinary Medical Examiners), Albany Budget Overview On: Alwin, Lawrence (State Auditor's Office), Austin Fabelo, Tony (Criminal Justice Policy Council), Austin Hamilton, Billy (Comptroller of Public Accounts), Austin Keel, John (Legislative Budget Board), Austin Registering, but not testifying: On: LeBas, James (Comptroller of Public Accounts), Austin Cosmetology Commission On: Alspaugh, Delores (Texas Cosmetology Commission), Austin Black, Penny (Texas Cosmetology Commission), Austin Cottrell, Comer J. (Texas Cosmetology Commission), Austin Registering, but not testifying: On: Perkins, Larry (Texas Cosmetology Commission), Austin Office of Administrative Hearings On: Taylor, Shelia Bailey (State Office of Administrative Hearings), Austin Yeager, Lisabeth (State Office of Administrative Hearings), Austin Structural Pest Control Bd. On: Brown, Jo-Christy (Structural Pest Control Board), Austin Gillen, Gary (Structural Pest Control Board), Rosenberg Mathis, Benny (Structural Pest Control Board), Austin


Additional remarks for MailMergePending/AttachmentName
This property contains name of generated document attachment in process of Mail Merge. Note: This property is used in Mail Merge, and it is obsolete in Word Merge.


Match Play. Red trails 0-3 to 5. How should Red play 61?
pips Red=67 White=87
(CorrectAnswer= 0/0, ErrorScore= 0)
Red doesn’t have an option about paying now versus paying later so must leave a blot.
What he shouldn’t do is minimise shots now because that still leaves the very difficult 11-pt to clear. He must bite the bullet and play 11/4, leaving 16 shots.
9/8, 9/3 and 9/2 are both 1.5 blunders.
What he shouldn’t do is minimise shots now because that still leaves the very difficult 11-pt to clear. He must bite the bullet and play 11/4, leaving 16 shots.
9/8, 9/3 and 9/2 are both 1.5 blunders.
1. Cubeful 2-ply 11/4 Eq.: +0.070 0.526 0.018 0.000 - 0.474 0.027 0.000 2. Cubeful 2-ply 9/8 9/3 Eq.: -0.007 (-0.077) 0.511 0.022 0.000 - 0.489 0.049 0.000 3. Cubeful 2-ply 9/3 2/1 Eq.: -0.029 (-0.099) 0.502 0.025 0.000 - 0.498 0.052 0.001 4. Cubeful 2-ply 9/2 Eq.: -0.038 (-0.108) 0.499 0.024 0.000 - 0.501 0.054 0.001 5. Cubeful 0-ply 9/3 4/3 Eq.: -0.443 (-0.513) 0.392 0.015 0.000 - 0.608 0.139 0.002


Request for citizen status
Virtual and not recognized
Real
Country
Request
Country
{{template "signatures".}}


Visualization of directional data (3D) using arrows.
Visualization of directional data (3D) using arrows.
| kmlStr = ge_quiver3(X,Y,Z,U,V,W) |
| kmlStr = ge_quiver3(X,Y,Z,U,V,W,Parameter,Value) |
| kmlStr = ge_quiver(X,Y,Z,U,V,W) |
Places 3D Collada model arrows at locations specified by "X" (longitude), "Y" (latitude), and "Z" (elevation). The direction of each arrow is determined from its components "U" (positive in the direction of positive longitude), "V" (positive in the direction of positive latitude), and "W" (positive in the direction of positive elevation).
| kmlStr = ge_quiver(X,Y,Z,U,V,W,Parameter,Value) |
In addition to the above, this syntax also allows for specification of a number of parameters regarding the appearance of the data when viewed in Google Earth. Assigning values to the parameters can be accomplished by inclusion of an alternating sequence of parameters and their value. The table below provides an overview of the authorized options. The order in which the parameters are included does not matter. Please be aware that the options are case-sensitive. Examples are provided further down.
| Parameter | Description |
| 'altitudeMode' | Specifies which plane of reference to use. Must be one of 'absolute', 'relativeToGround' or 'clampToGround'. |
| 'arrowScale' | Scales the arrow, but allows for varying of the arrow length with fluctuations in vector components "U", "V", and "W". Default is 1. |
| 'description' | A description of objects can be included using this parameter. Its value must be passed as a character array. It will be displayed in the Google Earth Viewer within a pop-up text balloon. |
| 'fixedArrowLength' | Makes the arrow length independent from the vector components "U", "V", and "W". Used in function ge_windbarb(). Default is false. |
| 'modelLinkStr' | URL specifying which Collada model to use as arrow. See also collada models. |
| 'msgToScreen' | Defines whether verbose feedback is provided by the function when it is accessed and when it finishes. Default is false (which is equivalent to logical(0), but quicker). |
| 'name' | This character array will be used within the Google Earth Viewer 'Places' pane to identify objects. |
| 'snippet' | A short description of the feature. In Google Earth, this description is displayed in the Places panel under the name of the feature. If a Snippet is not supplied, the first two lines of the description are used |
| 'timeSpanStart' | See Dynamic visualization. |
| 'timeSpanStop' | See Dynamic visualization. |
Latitudes on the Southern hemisphere must be passed as negative values. Same for longitudes on the Western hemisphere.
Latitudes and longitudes should be passed in units of decimal degrees (also known as fractional degrees). Google Earth uses Simple Cylindrical projection (also known as Plate Carée) with a WGS84 datum. Altitudes must be passed in units of meters.
Input variables "X", "Y", and "Z" define the origin of the arrows.
See the demo file for more details.
The demo script cycles through a double for-loop. During the first loop, when m is equal to 1, variable "a" is used to calculate the heading of the Collada model ('redcone.dae'). During the second loop, when m is equal to 2, variable "a" is used to calculate the model's tilt.
If the collada model file and the kml file are located in the same directory, the above should yield the following when viewed in Google Earth:


Index Pages
Practice with WHO data
link
WHO data First test (only polygon)
WHO data Second test (with line)
base map (only polygon)
base map (with line)
Please select the map you want to display.


Wikipedia Example
Accessing the Wikipedia API is relatively straightforward. The most complicated aspect is that it generally requires many parameters to be supplied to access the page you are looking for. For example, we must tell the API that we want to access the "extracts
" property. We can also specify that we want to follow redirects
, and that we want the list of page IDs (indexpageids
) returned to be in the response, so that we can access the data:
// Function that will process the response from the API
// This function should probably check for errors
var processResponse = function(data) {
console.log(data);
}
// Function to ensure parameters used in request are encoded correctly
var encodeParameters = function(params) {
// join all key value pairs and store in an array
var strArray = [];
for(var key in params) {
if(params.hasOwnProperty(key)) {
var paramString = encodeURIComponent(key) + "=" + encodeURIComponent(params[key]);
strArray.push(paramString);
}
}
// join everything in the array together
return strArray.join("&");
}
// parameters for the API request
var query="Public Service Broadcasting (band)";
var parameters = {
format: "json",
formatversion: 2,
action: "query",
prop: "extracts",
titles: query,
indexpageids: "",
redirects: "",
callback: "processResponse"
}
// construct the url
var base_url = "https://en.wikipedia.org/w/api.php";
var query_url = base_url + '?' + encodeParameters(parameters);
console.log(query_url);
// add a new script to the page, using the query_url as the source
var script = document.createElement('script');
script.src = query_url;
document.getElementsByTagName('head')[0].appendChild(script);
IMPORTANT NOTE! This is NOT ENOUGH to pass Coursework 2 for CMT112 - you will need to add some extra functionality and presentation of data. I will not be impressed if your submission consists only of the code presented here!


../
Students of REVA University, School of Commerce went on a industrial visit organized as part of the course which was educative and to nurture the students on manufacturing and production process on 11th and 12th February 2019.
According to the schedule students of II Sem, IV Sem and VI B.Com Honors and IV Sem B.Com ‘A’ Section were accompanied by Prof. Shalini, Prof. Janaki S and Prof. Girisha.T. The visit started at 9am and reached at 11:30am, after completing the entry requirements, Students entered the industry after being provided a brief introduction about the company and the precautions that has to be taken before entering to the processing unit.
Students experienced different segments of the industry, Firstly they were acquainted with the Research and development practices followed by the organization. Secondly the students were allowed to preparation room were the materials used for developing the product was stored.
Thirdly the process of making the product was been monitored.
Later they were taken to the actual processing unit, were the students were allowed to observe the making and packing of various flavors and varieties of products produced and the involvement of employees in the entire process was appreciable. Finally, the freezing section of the product where final product was freezed before dispatching.
Each process was explained by the people who were heading the department and guided the students by explaining each process. The company guide clarified all the doubts by students and left the industry by 1.00 pm, reached University campus by 3.00pm.
-Dhamini D


hellofresh
Businessmodel of HelloFresh
Customer Segments
HelloFresh provides a subscription meal and recipe kit service to the general consumer market. The Company’s service is targeted principally at those aged between 30 and 50 years of age, largely including young professionals, and young families with working parents. Reports also suggest that around 80% of the Company’s subscribers are women. These demographics are targeted as they typically have less time to devote to meal preparation and grocery shopping, and are more likely to have enough disposable income to afford subscription services.
HelloFresh is based in Germany, which appears to remain its principal market. The Company also operates local services in the UK, the Netherlands, Belgium, Austria, Switzerland, the US, and Australia.
Value Propositions
HelloFresh provides value to its customers in the following ways:
- Its accessibility and ease of use, with the Company making its services available through various online and mobile channels, including mobile apps, with customers easily able to manage their subscriptions and accounts independently;
- The quality of its meals and ingredients, with the Company sourcing its ingredients from reliable and sustainable suppliers, and providing a varied selection of recipes spanning all tastes and dietary requirement;
- Its competitive pricing structure, with the Company providing subscriptions at competitive rates, that are comparable both to competing subscription services and to traditional grocery shopping;
- Its international reach, with the Company operating local services across Europe – including in the UK, Belgium, Switzerland, and Austria, the US, and Australia; and
- Its sustainability and food waste schemes, with the Company seeking to reduce food waste by providing only the correct amounts of each ingredient, and using only recyclable packaging. ### Channels
HelloFresh operates a website at www.hellofreshgroup.com, through which it provides information on its management team, its services, and its international reach. The Company also operates local website in each of its operating jurisdictions, which serve as online sales channels. These local websites enable customers to select subscription plans, browse menus and recipes, and manage account settings and payments. Similar functionality is available through the Company’s mobile apps, which operate on iOS and Android systems.
HelloFresh began by delivering its meal boxes to customers via third-party carriers; however, the Company has begun to develop its own in-house fleet of delivery vehicles in recent years, with a view to depending less on the services of third-party carriers. HelloFresh plans its routes each week based on the deliveries it has to make, taking the shortest route possible in order to make its deliveries more efficient and reduce its carbon emissions.
Customer Relationships
HelloFresh provides its services on a self-service basis through its online and mobile channels, with customers able to select subscription plans, manage account details, arrange delivery schedules, and make payments online without interacting with members of the Company’s sales and support staff. HelloFresh seeks to provide a reliable and efficient service to its customers, with a view to securing recurring business.
HelloFresh operates a customer care team that provides ongoing support to subscribers. Customers are able to access personal assistance with delivery issues, enquiries, and complaints by contacting the company’s support staff over the phone or online. The Company also provides a range of online resources to its customers, including FAQs and recipes.
Additionally, HelloFresh maintains an online presence through which it is able to interact directly with its customers and keep subscribers informed on company developments. This includes a company blog, as well as social media accounts with Pinterest, Facebook, Google+, Twitter, and Instagram.
Key Activities
HelloFresh is a meal plan subscription service provider. The Company sources, packages, and delivers meal boxes to subscribers, providing a varied range of recipes to customers, using pre-measured seasonal ingredients, providing in a simple and accessible meal solution. The Company offers several different meal subscription plans, with subscribers able to select different menus for each weekly delivery.
HelloFresh delivers its meals to customers on a weekly basis through its own fleet of delivery vehicles, as well as through third-party delivery companies. The Company is based in Germany, which continues to be its principal market. It also has local operating units in the UK, Austria, Belgium, the Netherlands, Switzerland, Australia, and the US.
Key Partners
HelloFresh works closely with a number of partner companies and organisations to provide quality meals and ingredients to its customers reliably and efficiently. These partners include:
- Supplier and Sourcing Partners, comprising a range of food producers, primarily local food and ingredient suppliers, that provide fresh, high-quality ingredients to the Company;
- Channel and Affiliate Partners, comprising a range of companies and websites that operate as part of the Company’s affiliate network, extending the Company’s own marketing reach in return for commissions on sales; and
- Delivery Partners, comprising third-party delivery service providers that support the operations of the Company’s own distribution and delivery fleet. HelloFresh has a number of supplier partners, comprising local operators such as Masser Potato Farms, Del Rey Avocado, To-Jo Mushrooms, Murray’s Chicken, and Love Beets. The Company also has a partnership with chef Jamie Oliver, with whom it collaborates in promoting healthy eating practices and the benefits of fresh ingredients.
Key Resources
HelloFresh’s key resources are its portfolio of recipes, its network of suppliers and supply chain, its online and mobile platforms, its It and communications infrastructure, its delivery channels and fleet, its partnerships, and its personnel.
HelloFresh owns and leases a number of properties as part of its network of offices and local operations. This includes offices in Germany, the US, the UK, Austria, the Netherlands, Switzerland, Belgium, and Australia.
Cost Structure
HelloFresh incurs costs in relation to the development of its portfolio of recipes, the management of its supply chain and suppliers, the development and maintenance of its online and mobile platforms, the maintenance of its IT and communications infrastructure, the management and operation of its delivery infrastructure, the management of its partnerships, and the retention of its personnel.
In 2016 financial statement HelloFresh recorded cost of goods sold in the amount of €127 million, including the acquisition of ingredients and supplies. The Company also incurred fulfilment expenses – comprising costs related to the receiving, packaging, and shipping of goods – in the amount of €117.6 million, marketing expenses totalling €80.5 million, and general and administrative costs totalling €14.8 million.
Revenue Streams
HelloFresh generates revenue through the provision of a meal delivery subscription service to customers across Europe, the US, and Australia. The Company provides several subscription plans: Classic Meal Plan, Veggie Meal Plan, and Family Meal Plan. Subscriptions are priced on a per-meal basis, and can cover between two and five meals per week, for two to four people. Prices in the US range from $69 per week for three meals for two people, and $129 per week for three meals for four people.
In its 2016 financial statement HelloFresh recorded an annual revenue of €291.5 million, up considerably on the €112.5 million recorded by the Company in 2015.


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cdemi's
07 January 2020 |
.NET #dotNet |
Building a self-contained game in C# under 8 kilobytes (medium.com) |
| How to shrink a self-contained C# game from 65 MB to 8 kB in 9 steps.... Read more |
DevOps |
5 predictions for Kubernetes in 2020 (opensource.com) |
| How do you track a wildly popular project like Kubernetes? How do you figure out where it’s going? If you are contributing to the project or participating in Special Interest Groups (SIGs), you might gain insight by osmosis, but for those of you with day jobs that don’t include contributing to Kubernetes, you might like a little help reading the tea leaves. With a fast-moving project like Kubernetes, the end of the year is an excellent time to take a look at the past year to gain insight into the next one.... Read more |
Cloud |
Amazon, Google, Microsoft: Who had the best year in cloud in 2019? (theregister.co.uk) Google Microsoft Aws |
| We pick one technology highlight for each of the Big 3... Read more |
Infrastructure #sysadmin |
Linux in 2020: 27.8 million lines of code in the kernel, 1.3 million in systemd (theregister.co.uk) Linux |
| Systemd? It's the proper technical solution, says kernel maintainer... Read more |
General Tech #Tech |
Sony stuns CES with an electric show car, the Vision-S (arstechnica.com) |
| The concept shows off Sony's cameras, sensors, and entertainment.... Read more |
CES Unveiled 2020: Multi-Device Wireless Chargers, Nanoleaf Hexagons, and Robots, Plus New Samsung TVs (macrumors.com) CES 2020 |
| The annual CES event is taking place this week, with thousands of companies converging in Las Vegas to show off their newest tech-related products. ...... Read more |
|
This newsletter is made with 💖 by cdemi and sponsored by moo Research. You can find this MIT Licensed open-source project on GitHub cdemi/NewsletterCurator. You can also find an archive of all sent newsletters on GitHub at https://newsletters.cdemi.io/. If you want to unsubscribe from this newsletter, please click here. If you're new and you want to subscribe to this newsletter, please click here. |


[06:24:55] Finished 'warm-up-hands' after 8.81 s
[06:24:55] Finished 'warm-up-legs' after 12 s
[06:24:55] Starting 'gangnam-style-dance'...
[06:25:00] Calling PSY to start singing
[06:25:02] Eh, sexy lady 오-오-오-오 오빤 강남스타일
[06:25:06] Strange cowboy moves
[06:24:55] Finished 'do-the-moves' after 12 s
? Would you like to stop dancing soon as the song is ending? Yes
[06:25:14] Danced 234 steps, 234 passes, 0 failures: SUCCESS
root@PSY sudo dance-gangnam-style.exe


Mileage
Enter starting mileage.
Enter final mileage.
Know Total Miles?
Total Miles Travelled
Add any relevant notes if needed.
Enter any parking fees or tolls.
Update Total Miles Travelled.
Update notes.
Update parking fees or tolls.
Enter Total Miles Travelled.


Each new version of jenkins.war
is released by the process described here.
Weekly releases
- Releases are made (roughly once per week) from the master branch, (which is built by Jenkins)
- There is an general expectation that master is should be stable; work in progress should be held in a separate branch and submitted as a pull request once ready
- If a hot fix is required (e.g. due to some fairly critical issue in the latest release), the fixes are made on master, then a new branch is made from the previous release tag (say, 1.600), and the fixes are cherry-picked, and a new release (1.601) is made from that branch. The following release will be made from the tip of the master branch as usual
This means there's generally a maximum of one week before a change in the master branch becomes available in a release.
Long-term Support (LTS) Release
There is another release line that lags behind in terms of new features, but provides more stability and a slower update cycle, called the LTS Release Line.
Background
In March 2015, it was decided to make releases directly from the master branch.
Prior to this, there was an release candidate branch which would be branched from master up to a week in advance of the release. But very few people actively tested the RC builds, and this branching model made it more complicated to release a new Jenkins version on demand, e.g. if a hot fix was required.


The Trumpeter and the Echo by Dmitri Kabalevsky performed by Da Capo
The Haunted Mouse by Nancy and Randall Faber performed by Da Capo
RCM Prep B
Jumping Jacks by Andrew Markow performed by Da CapoTo Fly Like An Eagle by Anne Crosby Gaudet performed by Da CapoCool Groove by Edwin McLean performed by Da Capo
Minuet by James Hook performed by Da CapoA Little Joke by Dmitri Kabalevsky performed by Da CapoPlayful Puppy by Linda Niamath performed by Da Capo
Arietta in C Major by Muzio Clementi performed by Da CapoOn A Greek Island by Mark Mrozinski performed by Da Capo
RCM Grade One
Relay Race by Jon George performed by Da CapoThe Snake by Renee Christopher performed by Da CapoA Porcupine Dance by Dmitri Kabalevsky performed by Da CapoRobots by Anne Crosby Gaudet performed by Luca M.
Burlesque in G Major by Anonymous performed by Da CapoMinuet in F Major by Wolfgang Amadeus Mozart performed by Da CapoMorning Greeting by Cornelius Gurlitt performed by Da Capo
Étude in C Major by Anton Diabelli performed by Da CapoThe Swinging Leprechaun by Mike Schoenmehl performed by Da CapoCelebration by Nancy Telfer performed by Luca M.


Your Time, Spent Wisely.
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Izjava o zaštiti privatnosti
Effective Date: March 24, 2019
Sites Covered: PornoZec.com
Pursuant to our Terms of Use, this document describes how we treat personal information related to your use of this website (the “Website”), including information you provide when using it. If you do not agree with these terms and do not wish to use our service, you should leave the site.
We expressly and strictly limit use of the Website to adults over 18 years of age or the age of majority in the individual’s jurisdiction, whichever is greater. Anyone under this age is strictly forbidden from using the Website. We do not knowingly seek or collect any personal information or data from persons who have not attained this age.
This Privacy Policy can provide privacy details on how we manage your personal information.
Please take a moment to familiarize yourself with our privacy practices and let us know if you have any questions.
I. Collection of Information
We collect, process and retain personal data to the extent it is necessary to provide users with our service, especially for the following purposes, e.g. keeping search and video preferences, keeping the likes of users, displaying advertisements, keeping statistics, etc.
We do not collect, process or retain personal data unless allowed under statutory laws, required by a judicial or governmental order. We collect all personal data by ourselves and by our means.The only personal data we collect from users are their Geo-location (Country), Browser language, Operating System and Referer sites the users came from.
WE DO NOT STORE PERSONAL DATA!
All collected information is only used to customize content and advertising and prepare aggregate statistics about the overall activity on the site. Such aggregate statistics include the countries visitors came from and the types of electronic device used; they do not contain any personal information.
We do not track every click or video watched by users and associate that with the particular user. We collect only agrregated data of total clicks and views that were made for every piece of content without data about users that did that clicks.
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We disclose your information for certain purposes to third parties, as described below:
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All that information may be collected by Advertising-Related Service Providers (ARSP) using standard data scripts and can be used to optimize banner advertising shown on our site. We do not collect or store such data and cooperate with reliable and trusted ARSP only:
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- - Exoclick Ltd. operates under the following terms:
We repeat: WE DO NOT COLLECT OR STORE USER PERSONAL DATA!
We get info to customize search results, give you a cookie with needed parameters and then delete all personal data.
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Cookies are text files that are placed by the browser in the memory of a user's device when this site is visited. While working we use session cookies and persistent cookies:
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We work hard to protect our users from unauthorized access to or unauthorized alteration, disclosure or destruction of information we hold. In particular:
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This example shows how to make selections on visualization programmatically.


--- title: "Mental Health Redux" author: "Drew Tyre" date: '2019-02-18' output: word_document draft: no slug: mental-health-redux summary: About a year ago I disclosed my depression to the interwebs. It seems timely to revisit the topic. tags_include: - Higher Ed - mental health categories: Personal ---
About a year ago I disclosed my depression to the interwebs. It seems timely to revisit the topic.
On the whole, telling people about my depression was a positive experience. My colleagues were very supportive. I made new friends as people reached out to have lunch and talk about mental health, science fiction, and cooking. I didn’t hear any official reaction, but through the grapevine came rumbles of my blog post triggering discussions in various student support groups throughout the university.
Despite my positive experiences, disclosure isn’t the right move for everyone. I am so lucky to be surrounded by nice people. But someone else may not be in such a safe place. I am also at a point in my career where I can remain successful even if some people choose not to work with me because of my mental health issues, or my public stance on those issues. I may have lost research opportunities because of my previous post, and this one won’t help. I’m OK with that; I’ve got enough to do, and I need to say “No” a lot more.
One thing I didn’t anticipate was the number of people who wanted to talk to me about their mental health. I listened attentively, encouraged them to seek professional help, and worked my butt off trying not to get attached to their outcomes. I have a much greater appreciation for the work of therapists and counselors now. After every meeting I wonder about what I said, what should I have said, did I do enough? I’ll still say yes every time I’m asked.
One of the recommendations for improving mental health in Academia is to create a “culture of access”. There are several components to this, but one piece is organizing events that discuss mental disabilities as an expected and important area of faculty and student support. So when the Natural Resources Diversity Initiative invited me to lead a discussion on mental health during fall semester, I thought it was great idea. I was feeling so much better, and it seemed doable. It turned out great, but the week before I was an anxious wreck. Normally when I speak in public I am an expert in the topic. My only qualification was personal experience.
I asked for suggestions on Twitter – that turned out to be a mixed benefit. On the one hand, lots of supportive replies to just tell my story, and it’ll be great. On the other were outright critical replies telling me to turn it over to a mental health professional, or worrying that graduate student complaints would dilute the lived experience of genuinely ill people. I left Twitter alone for the better part of a month after that.
The thing is, we’ve had presentations from “the proper authorities.” This was my colleagues saying “we want even more discussion!” I reached out to our university Counseling and Psychological Services about this discussion and got no reply – they are swamped. And at the meeting a few genuinely ill people spoke up and shared their experience. People get it. This isn’t about graduate student whining. This is about realizing that mental health is as important as physical health. I adapted some material I found on the CAPS website to a “myth or fact” activity. I told my story, did the exercise, and let the discussion flow after that. It went really well.
During the discussion one of my colleagues asked a great question to which I had no answer. Paraphrased, “What do we do when a student suffers depression, and is unable to complete the work required by the funding agency?” I’ve since realized the question itself reflects the stigma attached to mental health. The answer is, the same thing you’d do if the student was diagnosed with cancer,or became pregnant! Mental disability isn’t any different from a physical illness or condition that leaves a student unable to complete the work. In fact, with proper support and treatment, a mentally ill student may well be able to complete the work. The “proper support” is where mental health professionals play a key role.
There is a lot of work left to do on improving the environment at UNL and academia generally. I will keep trying to do my part.
Last summer a colleague was diagnosed with cancer. Joe Dauer and I took over the Intro ecology course to allow our colleague to focus on healing and recovery. I was doing well and it seemed like a doable thing, co-teaching ecology and organizing the ecology lab at the same time. I was super-excited to teach with Joe, one of the educational research faculty at UNL. I expected to learn a lot. I wasn’t disappointed! It was an awesome experience. Unfortunately it was also a little too much.
I’d also agreed to organize the ecology lab for the spring semester on top of my regular teaching. It was a good idea, really. We need to bring the fall and spring versions into greater alignment, and having me take over the spring semester as well would facilitate that. I was self-aware enough to recognize that a second semester of double teaching would do me in, so I had started working with John, my boss, to delegate and transfer some other responsibilities. I made it through the fall semester, but once grades were in, the pressure came off, and my mood slipped day by day.
The random crying jags struck about four days after I turned in my grades. On a whim, I stopped in to see John and update him on my plans for the spring semester. What I didn’t plan on was bursting into tears in his office. I left with strict orders that I would not be teaching ecology lab or doing several other things this spring.
I arranged an emergency visit with my counselor. It was only five minutes into the conversation when he asked if I would check myself in to the psychiatric ward at our local hospital. That really drove home how bad I was. More discussion of that option revealed I wasn’t actively suicidal, so not needed, but ouch. That was a wake up call for sure.
Next stop, back to my GP to see about medication. I sat there, twitching1, rubbing my hands together anxiously, barely holding back tears while describing the situation to the nurse. My GP took one look and started me on a low dose of an atypical anti-psychotic2.  Unfortunately I was tired and foggy all the time. I started working with a psychiatrist to review and evaluate all my medication, hopeful that an effective combination with few side effects can be found. I was really tired of the merry-go-round.
One clear positive note in all this: counselling helps. One of the questions to diagnose depression is something to the effect of “Do you have thoughts of worthlessness?” When the nurses asked me this, I had to stop and think. I was amazed to realize the answer was no, none. Not one. That’s the counselling. Unreasonable sadness, check. Suicidal ideation, check. But I don’t think I’m worthless. Medication plus counseling. It just works.
I’m experiencing some fairly immediate improvements from a couple of medication changes. But I’ve often had short term gains followed by long term mediocrity, so the jury is still out. The changes are based on a genetic test which I’ll write about elsewhere. But I’m kind of excited.
My counselor reminds me often that thoughts create reality. A friend reminded me that words are thoughts pinned in space-time by sound or text. I want to create a reality of health, so here’s what that looks like. My “quietly confident”3 mind generates new ideas and readily produces clear descriptions of those ideas. I get through an 8 hour day with energy and motivation to spare. I use that excess energy to spend quality time with friends and family, and on activities that refresh me. I have the willpower to exercise daily. I manage my time effectively, helping others, but also making time to do the things that advance my interests.
Wellness is a process. I’m struggling now, but on an upward path. I’ve surrounded myself with living things4, and my family is close at hand. I am hopeful5.
These are sharp, sudden spasms of a majority of muscles in my upper body. They are particularly likely when I bump into something or someone touches me, but happened all the time. Involuntary twitching is normal right before sleep, or occasionally when awake, but frequent twitches are a sign something is off.↩
My GP took pains to point out that I wasn’t psychotic!↩
This phrase comes from the Headspace app I use for mindfulness meditation.↩
Mushrooms, yeast, and lactic acid bacteria are alive! Our windows block too much light to grow plants indoors. I plan to start some outdoor herb planters as the weather warms up. And there is always this critter.↩
Kelly Helm-Smith and Peter Allman read early drafts of this post and it is immeasurably better as a result.↩


Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging, Dependency Parsing and Named Entity Recognition for more than 50 languages
We are currently working on training our models using the UD Corpus. Once this is done, we will provide plenty of examples, based on our experience with this dataset.


Ploror688.github.io
7 Ways to Make Homework Fun! - Red Tricycle. More advice about teaching ESL students (FAQ) Who is this FAQ for? What's the most important thing I should know about the ESL students I teach.
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Alex Scott Easy Fun. Get everyone moving in the living room with a Super Movers challenge. Do your moves match those of Alex Scott and Gunnersaurus. Make Free and Engaging Puzzles - Not Boring edHelper.com. Answers.com is the place to go to get the answers you need and to ask the questions More advice about teaching ESL students (FAQ). Fun definition and meaning Collins English Dictionary.
Pay for Homework Pay Someone to Do Homework 5Homework.com. Computing Homework :: Computing at Key Stage. How to make the 2018 Iowa State Fair a fun experience for kids and families. Your most important tradition, of course, should be just to make happy memories.


Hi,
My name is Ibrahim Majdi.I have a bachelor degree in graphic design and I'm happy that I'll be learning
coding.
Top 10 sites to visit in Jordan
- Ajloun
- Petra
- Al-Maghtas
- Amman
- Jerash
- Wadi Rum
- Dead Sea
- Aqaba
- Mujib Nature Reserve
- Dana Nature Reserve
- Madaba
My work and education experience
- I have a bachelor degree in graphic design.
- I studied at Yarmouk university-Irbid.
- I worked as graphic designer for 2 years.


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/nat.2Esqrt.20in.20lean.html ---
hi, i'm trying to understand the nat.sqrt function i know what is does but cant' figure out how the algorithm actually works. can someone explain it to me please? here is the code in the library
theorem sqrt_aux_dec {b} (h : b ≠ 0) : shiftr b 2 < b := begin simp [shiftr_eq_div_pow], apply (nat.div_lt_iff_lt_mul' (dec_trivial : 0 < 4)).2, have := nat.mul_lt_mul_of_pos_left (dec_trivial : 1 < 4) (nat.pos_of_ne_zero h), rwa mul_one at this end def sqrt_aux : ℕ → ℕ → ℕ → ℕ | b r n := if b0 : b = 0 then r else let b' := shiftr b 2 in have b' < b, from sqrt_aux_dec b0, match (n - (r + b : ℕ) : ℤ) with | (n' : ℕ) := sqrt_aux b' (div2 r + b) n' | _ := sqrt_aux b' (div2 r) n end /-- `sqrt n` is the square root of a natural number `n`. If `n` is not a perfect square, it returns the largest `k:ℕ` such that `k*k ≤ n`. -/ def sqrt (n : ℕ) : ℕ := match size n with | 0 := 0 | succ s := sqrt_aux (shiftl 1 (bit0 (div2 s))) 0 n end
thank you!
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Match Play. Red leads 4-1 to 7. How should Red play 62?
pips Red=97 White=155
(CorrectAnswer= 0/0, ErrorScore= 0)
This is the follow up to Position 215 after Red doubled and White took.
For money 11/3* is correct but not at this score. If things go badly for Red the cube will be coming back very quickly and a gammon will cost him the match. Red should play with only one blot and move 12/6, 7/5.
Red will be hoping that White will have to crash his home board. Once that happens, he can be more aggressive. This position demonstrates why Red was wrong to double in Position 215. That active cube on 2 could come back to haunt him.
If Red had left the cube in the centre, it would have been a toss-up between 11/3* and 12/6 7/5 for this roll of 62.
For money 11/3* is correct but not at this score. If things go badly for Red the cube will be coming back very quickly and a gammon will cost him the match. Red should play with only one blot and move 12/6, 7/5.
Red will be hoping that White will have to crash his home board. Once that happens, he can be more aggressive. This position demonstrates why Red was wrong to double in Position 215. That active cube on 2 could come back to haunt him.
If Red had left the cube in the centre, it would have been a toss-up between 11/3* and 12/6 7/5 for this roll of 62.
1. Cubeful 2-ply 12/6 7/5 Eq.: +0.230 0.663 0.271 0.026 - 0.337 0.039 0.001 2. Cubeful 2-ply 11/5 7/5 Eq.: +0.214 (-0.016) 0.658 0.271 0.025 - 0.342 0.040 0.001 3. Cubeful 2-ply 11/3* Eq.: +0.187 (-0.042) 0.663 0.287 0.028 - 0.337 0.059 0.002 4. Cubeful 2-ply 12/6 5/3* Eq.: +0.169 (-0.061) 0.657 0.285 0.028 - 0.343 0.059 0.002 5. Cubeful 2-ply 12/10 11/5 Eq.: +0.138 (-0.092) 0.642 0.276 0.026 - 0.358 0.050 0.001


screen
${currentplayer.name}
- Please press continue on remote.
doesnt have to be always visible just when awaiting nextPlayer
team: ${team.name}
points: ${team.points}
players:
${player.name}
${timeleftMMSS}
${team.name}
×
${textShownMsg}
doesnt have to be always visible just when awaiting nextPlayer
team: ${team.name}
points: ${team.points}
players:
${player.name}


This supply chain tool implements blockchain metadata storage for its records. Any user can upload a file to be viewed by any number of private access groups. Only members of a given access group, specified by their ownership of that group's private encryption key, will be able to access files uploaded for that group. All actions are logged on the blockchain, such that they are permanent and cannot be edited.


History¶
0.0.18¶
- Fixed windowed writing to require tiled output raster (pull #66)
- Write data array attributes using rio.to_raster (issue #64)
- Write variable name to descriptions if possible in rio.to_raster (issue #64)
- Add mask_and_scale option to rioxarray.open_rasterio() (issue #67)
- Hide NotGeoreferencedWarning warning when subdatasets are present using open_rasterio (issue #65)
- Add support for loading in 1D variables in xarray.open_rasterio() (issue #43)
- Load in netCDF metadata on the variable level (pull #73)
0.0.17¶
- Renamed descriptions to long_name when opening with open_rasterio() (pull #63)
- Make units & long_name scalar if they exist in rasterio attributes (pull #63)
0.0.16¶
- Add support for netcdf/hdf groups with different shapes (pull #62)
0.0.15¶
- Added variable and group kwargs to rioxarray.open_rasterio() to allow filtering of subdatasets (pull #57)
- Added default_name kwarg to rioxarray.open_rasterio() for backup when the original does not exist (pull #59)
- Added recalc_transform kwarg to rio.to_raster() (pull #56)
0.0.14¶
- Added windowed kwarg to rio.to_raster() to write to raster using windowed writing (pull #54)
- Added add rio.isel_window() to allow selection using a rasterio.windows.Window (pull #54)
0.0.13¶
- Improve CRS searching for xarray.Dataset & use default grid mapping name (pull #51)
0.0.12¶
- Use xarray.open_rasterio() for rioxarray.open_rasterio() with xarray<0.12.3 (pull #40)
0.0.11¶
- Added open_kwargs to pass into rasterio.open() when using rioxarray.open_rasterio() (pull #48)
- Added example opening Cloud Optimized GeoTiff (issue #45)
0.0.10¶
- Add support for opening netcdf/hdf files with rioxarray.open_rasterio (issue #32)
- Added support for custom CRS with wkt attribute for datacube CRS support (issue #35)
- Added rio.set_nodata(), rio.write_nodata(), rio.set_attrs(), rio.update_attrs() (issue #37)
0.0.9¶
- Add rioxarray.open_rasterio (issue #7)
0.0.8¶
- Fix setting nodata in _add_attrs_proj (pull #30)
0.0.7¶
- Add option to do an inverted clip (pull #29)
0.0.6¶
- Add support for scalar coordinates in reproject (issue #15)
- Updated writing encoding for FutureWarning (issue #18)
- Use input raster profile for defaults to write output raster profile if opened with xarray.open_rasterio (issue #19)
- Preserve None nodata if opened with xarray.open_rasterio (issue #20)
- Added drop argument for clip() (issue #25)
- Fix order of CRS for reprojecting geometries in clip() (pull #24)
- Added set_spatial_dims() method for datasets when dimensions not found (issue #27)
0.0.5¶
- Find nodata and nodatavals in ‘nodata’ property (pull #12)
- Added ‘encoded_nodata’ property to DataArray (pull #12)
- Write the raster with encoded_nodata instead of NaN for nodata (pull #12)
- Added methods to set and write CRS (issue #5)
0.0.4¶
- Added ability to export data array to raster (pull #8)


Thanks for trying out NetJournal.
It is in it's rough beginning stages, but we'd like to get it in a more user-friendly form sometime soon.
The point of NetJournal is pretty simple -- it's like a website blocker / productivity booster, but instead of entirely blocking websites, it just asks you to write down some thoughts before continuing to the website. The thoughts is, we go to certain websites (the ones that some people block when it feels they can't control themselves), for a reason. Instead of using an extension to block a site for us, hopefully we can examine our thought processes to see why we keep going there.
It's good to find opportunities to be forgiving of ourselves and try to understand ourselves better.
This first version of NetJournal is built on a a fork of Einar Egilsson's Redirector. Redirector is a robust opensource browser extension that lets you create any type of redirect under the sun (as far as I can tell). In this project, we only use a couple of the features built into Redirector. If you are curious about any of it, though, please take a look below at the Redirector Table of Contents.
If you're interested in improving this project (either by fixing a typo, giving it some new features, fixing a bug, or improving its overall structure), please check it out on github. I can't put a lot of time towards it currently, but I would like to keep working on it since I've found that it's useful for me and I think it might be helpful for other people, too. It feels like so many things on the internet make money by commodifying our attention, and so I think it's important to check in with ourselves to see how we feel about that.


Make a new Plan
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Complete Path {{FM.curFolderPath + FM.newFolderName + '/'}}
Upload to {{FM.curFolderPath + FM.uploadFile.name}}


Charles Schwab Gobbles TD Ameritrade for $26 Billion
by ASSOCIATED PRESSNEW YORK (AP) — Charles Schwab is buying rival TD Ameritrade for $26 billion, a blockbuster deal accelerated by massive disruption in the online brokerage industry.
Competitive pressure has forced brokerages to make it free for customers to trade U.S. stocks online, and Schwab’s proposal would combine two of the biggest players in the industry.
The merger would create a company so big that it may draw sharp scrutiny from antitrust regulators. Together the companies would have more than $5 trillion in client assets under management.
By itself, Schwab may control close to half the market for acting as a custodian for money managed by registered investment advisers, while TD Ameritrade may control 15% to 20%, according to Kyle Voigt, an analyst with Keefe, Bruyette & Woods, investment bankers.
The rewards for passing regulatory muster would be lucrative. A combined company “makes strong strategic sense,” would be able to cut costs and could bump up Schwab’s earnings per share by more than 25% over the long term, Voigt said.
The deal could also herald more mergers across the industry.
“If this deal occurs, smaller players may get squeezed further on pricing,” said Michael Taiano, senior director at Fitch Ratings.
Schwab last rocked the industry less than two months ago, when it said it would do away with commissions for online trading of U.S. stocks and exchange-traded funds, fees that have long fueled the industry. All major brokerages have followed suit.
Fees have been dropping across the investment industry for years, not just for buying a stock but also for holding a mutual fund or ETF.
Beyond players like Schwab, TD Ameritrade, Fidelity and E-Trade Financial, apps like robinhood.com out of Palo Alto, California, have entered the fray to help customers get invested in the market.
That has increased the pressure on Schwab Corp. and TD Ameritrade Holding Corp., of Omaha, the biggest publicly traded brokerages. Schwab had $3.85 trillion in client assets at the start of November, while TD Ameritrade had $1.3 trillion at the end of September.
Copyright © 2019 courthousenews.com


This Calendar is daily used in life. Actually it is a Gregorian
Calendar and it's comes in INDIA. When the Gregorian calendar was adopted
Britain in 1752, in this a correction of an 11-day discrepancy was made; the day
after September 2, 1752, became September 14. The British also adopted January 1
as the day when a new year begins. We are use Gregorian Calendar at this time. In my Calendar
all the changes are available and in this calendar a century years divisible
evenly by 400 should be leap years and that all other century years should be
common years. Thus, 1600 was a leap year, but 1700 and 1800 were common years.
This calendar is a Hindu Calendar. Actually it is called
desi calendar and my calendar project show this calendar . Month of Vikrami era
are Caitra (March-April) , Vaisakha (April-May), Jyaistha (May-June) ,
Asadha (June-July),Sravana (July-Aug) , Bhadrapada (Aug-Sept), Asvina
(Sept-Oct), Karttika (Oct-Nov), Margasirsa (Nov-Dec), Pausa (Dec-Jan), Magha
(Jan-Feb) ,Phalguna (Feb-March). In my calendar all the adoptions are available
and Vikrami calendar is show with Christian era dates.
This calendar is also a Hindu Calendar. Actually it is called desi calendar and my calendar project show this calendar . Month of Shak era are Caitra (March-April) , Vaisakha (April-May), Jyaistha (May-June) , Asadha (June-July),Sravana (July-Aug) , Bhadrapada (Aug-Sept), Asvina (Sept-Oct), Karttika (Oct-Nov), Margasirsa (Nov-Dec), Pausa (Dec-Jan), Magha (Jan-Feb) ,Phalguna (Feb-March). In my calendar all the adoptions are available and Shak calendar is show with Christian era dates.


by default overflow is visable
overflow scroll always show scrollbar
overflow auto show bar when neccessary


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| currency-converter |
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100% | 11/11 | 50% | 1/2 | 100% | 1/1 | 100% | 11/11 |
| currency-converter/config |
|
87.1% | 27/31 | 25% | 1/4 | 100% | 5/5 | 87.1% | 27/31 |
| currency-converter/server |
|
100% | 1/1 | 100% | 0/0 | 100% | 0/0 | 100% | 1/1 |
| currency-converter/server/controllers |
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100% | 20/20 | 100% | 4/4 | 100% | 2/2 | 100% | 19/19 |
| currency-converter/server/graphql/resolvers |
|
100% | 23/23 | 100% | 4/4 | 100% | 2/2 | 100% | 23/23 |
| currency-converter/server/graphql/schema |
|
100% | 5/5 | 100% | 0/0 | 100% | 0/0 | 100% | 5/5 |
| currency-converter/server/router |
|
100% | 4/4 | 100% | 0/0 | 100% | 0/0 | 100% | 4/4 |
| currency-converter/server/service |
|
100% | 37/37 | 100% | 0/0 | 100% | 10/10 | 100% | 28/28 |
| currency-converter/server/utils |
|
100% | 8/8 | 100% | 2/2 | 100% | 2/2 | 100% | 6/6 |


strength, power, and character of the One who overcame death when you are looking to overcome things in your own life. Thank God that He offered us His Son—and His name!
Pray About It
Jesus, thank You for offering us Your name, for wanting us to know You so well that we’re on a first-name basis with You in prayer. Help us to feel so comfortable with You that we share our deepest desires, needs, hurts, and joys. We’re grateful that You didn’t ask us to say some ritualistic formula in prayer; You ask so much more. You ask for all of us, surrendered to You.  In Jesus’ name we pray. Amen.
Act on It
Continue using the litmus test of lordship, character, and love with your prayers this week. If something does not pass the test, ask the Holy Spirit how to pray about this concern in light of God’s character and will.