| I l@ve RuBoard |
| NetGroupGetInfo |
Retrieves information about a particular global group in the security database.
data = NetGroupGetInfo(serverName, groupName, level)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the information level of the returned data. May be 0, 1, or 2.
The result is a dictionary, the contents of which are defined by one of the GROUP_INFO structures, depending on the level parameter.
| NetGroupGetUsers |
Enumerates the users in a global group.
entries, total, resume = NetGroupGetUsers(serverName, groupName, level, resume = 0, len=4096)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the level of data to be returned for each group. Only (zero) is currently valid.
A value that controls the iteration when there are a large number of servers to list. Zero should be passed the first time it's called, and while a non-zero resume result is returned from the function, it can be called again with the new resume value to obtain the rest of the servers.
A hint to the Win32 function about how much data to allocate. See the Win32 documentation for more details.
The result is a tuple with the following items:
A list of dictionaries of format GROUP_USERS_INFO_0, one for each user returned.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle that can obtain the next set of users. When resume is zero, there are no more items to read.
| NetGroupSetUsers( ) |
Sets the membership for the specified global group. Each user you specify is enrolled as a member of the global group. Users you don't specify, but who are currently members of the global group, will have their membership revoked.
NetGroupSetUsers(serverName, groupName ,level=0 , members)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the data in the members parameter. Only (zero) is currently valid.
A sequence (for example, list or tuple) of dictionaries, each of which contains data that corresponds to the GROUP_USERS_INFO_0 structure.
| NetGroupSetInfo( ) |
Sets information about a particular group account on a server.
NetGroupSetInfo(serverName, groupName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the data in the data parameter. May be 0, 1, 2, 1002, or 1005.
A dictionary with the data for the group. The data is in the format of one of the GROUP_INFO structures, depending on the level parameter.
| NetGroupAdd( ) |
Creates a new global group in the security database.
NetGroupAdd(serverName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the data in the data parameter. May be 0, 1, or 2.
A dictionary with the data for the group. The data is in the format of one of the GROUP_INFO structures, depending on the level parameter.
| NetGroupAddUser( ) |
Gives an existing user account membership in an existing global group in the security database.
NetGroupAddUser(serverName, groupName, userName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group to which the user is to be given membership.
A string specifying the name of the user to be added to the group.
| NetGroupDel( ) |
Deletes a global group from the security database.
NetGroupDel(serverName, groupName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group to be deleted.
| NetGroupDelUser( ) |
Deletes a user from the specified global group
NetGroupDelUser(serverName, groupName, userName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group from which the user is to be removed.
A string specifying the name of the user to be removed from to the group.
| NetGroupEnum |
Retrieves information about each global group in the security database.
entries, total, resume = NetGroupEnum(serverName, level, resume = 0, len=4096)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the level of the data to be returned. May be 0, 1, or 2.
A value that controls the iteration when there are a large number of servers to list. Zero should be passed the first time it's called, and while a non-zero resume result is returned from the function, it can be called again with the new resume value to obtain the rest of the servers.
A hint to the Win32 function about how much data to allocate. See the Win32 documentation for more details.
The result is a tuple with the following items:
A list of dictionaries in the format of one of the GROUP_INFO structures, depending on the level parameter, one for each group returned.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle that can obtain the next set of users. When resume is zero, there are no more items to read.
| NetLocalGroupAddMembers( ) |
Adds users to a local group.
NetLocalGroupAddMembers(serverName, groupName, level, members)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the data in the members parameter; can be or 3.
A sequence (for example, list or tuple) of dictionaries, each of which contains data that corresponds to the relevant LOCALGROUP_MEMBERS_INFO structure, depending on the level parameter.
| NetLocalGroupDelMembers( ) |
Deletes one or more users from a local group.
NetLocalGroupDelMembers(serverName, groupName, level, members)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the data in the members parameter; can be or 3.
A sequence (for example, list or tuple) of dictionaries, each of which contains data that corresponds to the relevant LOCALGROUP_MEMBERS_INFO structure, depending on the level parameter.
| NetLocalGroupGetMembers |
Enumerates the members in a local group.
entries, total, resume = NetLocalGroupGetMembers(serverName, groupName, level, resume = 0, len=4096)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
The name of the local group from which to enumerate the members.
An integer that specifies the level of data to be returned. May be 0, 1, 2, or 3.
A value that controls the iteration when there are many servers to list. Zero should be passed the first time it's called, and while a non-zero resume result is returned from the function, it can be called again with the new resume value to obtain the rest of the servers.
A hint to the Win32 function about how much data to allocate. See the Win32 documentation for more details.
The result is a tuple with the following items:
A list of dictionaries in the format of one of the LOCALGROUP_MEMBERS_INFO structures; depending on the level parameter, one for each user returned.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle that can obtain the next set of users. When resume is zero, there are no more items to read.
| NetLocalGroupSetMembers( ) |
Sets the members of a local group. Any existing members not listed are removed.
NetLocalGroupSetMembers(serverName, groupName, level, members)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the global group.
An integer that specifies the data in the members parameter. May be or 3.
A sequence (for example, list or tuple) of dictionaries, each of which contains data that corresponds to one of the LOCALGROUP_MEMBERS_INFO structures.
| NetMessageBufferSend( ) |
Sends a string to a registered message alias.
NetMessageBufferSend(domain, userName, fromName, message)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
The username or message alias to which the message should be sent.
A string that specifies the username or message alias from whom the message is from, or None for the logged-in user.
A string containing the message text.
| NetServerEnum |
Retrieves information about all servers of a specific type
entries, total, resume = NetServerEnum(serverName, level, serverTypes=win32netcon.SV_TYPE_ALL, resume = 0, len=4096)
The name of the server to execute on or None for the current machine.
An integer specifying the level of information requested; can be 101 or 102.
A bitmask of flags indicating the types of servers to list. A list of the common flags is presented here; please refer to the Win32 documentation for more details.
All Internet workstations.
All Internet servers.
Any server running with Microsoft SQL Server.
Primary domain controller.
Backup domain controller.
Server running the time-source service.
Server sharing print queue.
Server running dial-in service.
Windows NT (either workstation or server).
Windows NT nondomain controller server.
Server running the domain master browser.
Primary domain.
Windows 95 or later.
All servers.
A value that controls the iteration when there are many servers to list. Zero should be passed the first time it's called, and while a non-zero resume result is returned from the function, it can be called again with the new resume value to obtain the rest of the servers.
A hint to the Win32 function about how much data to allocate. See the Win32 documentation for more details.
The result is a tuple with the following items:
A list of dictionaries, each of the format define in one of the SERVER_INFO structures depending on the level parameter, one for each server returned.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle that can obtain the next set of users. When resume is zero, there are no more items to read.
| NetServerGetInfo |
Retrieves information about a particular server.
data = NetServerGetInfo(serverName, level)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the data returned by the function. May be 100, 101, or 102 on all platforms, 1 or 50 on Windows 95/98, or 402 or 403 for Windows 2000.
The result is a dictionary in the format of one of the SERVER_INFO structures, depending on the level parameter.
| NetServerSetInfo( ) |
Sets information about a particular server.
NetServerSetInfo(serverName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the data in the data parameter. May be one of 100, 101, 102, 402, or 403.
A dictionary with the data for the server. The data is in the format of one of the SERVER_INFO structures, depending on the level parameter.
| NetShareAdd( ) |
Adds a server resource.
NetShareAdd(serverName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the data in the data parameter. May be 2 or 502.
A dictionary with the data for the server resource. The data is in the format of one of the SHARE_INFO structures, depending on the level parameter.
| NetShareDel( ) |
Deletes a server resource.
NetShareDel(serverName, networkName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the resource name to be deleted.
| NetShareCheck |
Checks if server is sharing a device.
result, type = NetShareCheck(serverName, deviceName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the name of the device to check for shared access
true if the device is shared, otherwise false.
If result is true, indicates the type of device, and may be one of the following values:
The device is a shared disk drive.
The device is a shared print queue.
The device is a shared communications device.
An interprocess communication device.
A special device.
| NetShareEnum |
Retrieves information about each shared resource on a server.
entries, total, resume = NetShareEnum(serverName, level, resume = 0, len=4096)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the level of the data to be returned; may be 0, 1, 2, or 502.
A value that controls the iteration when there are many servers to list. Zero should be passed the first time it's called, and while a non-zero resume result is returned from the function, it can be called again with the new resume value to obtain the rest of the servers.
A hint to the Win32 function about how much data to allocate. See the Win32 documentation for more details.
The result is a tuple with the following items:
A list of dictionaries in the format of one of the SHARE_INFO structures, depending on the level parameter, one for each resource returned.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle, that can be used to obtain the next set of users. When resume is zero, there are no more items to read.
| NetShareGetInfo |
Retrieves information about a particular share on a server.
data = NetShareGetInfo(serverName, networkName, level)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the network name of the share on which to return information.
An integer that specifies the data returned by the function. May be 0, 1, 2, 501, 502, or 1005.
The result is a dictionary in the format of one of the SHARE_INFO structures, depending on the level parameter.
| NetShareSetInfo( ) |
Sets information about a particular share on a server.
NetShareSetInfo(serverName, networkName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the network name to set.
An integer that specifies the data in the data parameter. May be 1, 2, 502, 1004, 1006, or 1501.
A dictionary with the data for the server. The data is in the format of one of the SHARE_INFO structures, depending on the level parameter.
| NetUserAdd( ) |
Creates a new user account.
NetUserAdd(serverName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
An integer that specifies the data in the data parameter. May be 1, 2, or 3.
A dictionary with the data for the server resource. The data must be in the format of the relevant USER_INFO structure, depending on the level parameter.
| NetUserChangePassword( ) |
Changes a user's password on the specified domain.
NetUserChangePassword(serverName, userName, oldPassword, newPassword)
A string specifying the name of a remote server or domain on which the account is changed. If this parameter is None, the logon domain of the caller is used.
The username for which the password is to be changed or None for the current user.
The user's old password.
The user's new password.
| NetUserEnum |
Provides information about all user accounts on a server.
entries, total, resume = NetUserEnum(serverName, level, filter=win32netcon.FILTER_ ACCOUNT_NORMAL resume = 0, len=4096)
The name of the server to execute on or None for the current machine.
An integer specifying the level of information requested. This can be 0, 1, 2, 3, 10, 11, or 20.
Specifies the account types to enumerate, which may be one of the following values:
Enumerates local user account data on a domain controller.
Enumerates global user account data on a computer.
Enumerates domain trust account data on a domain controller.
Enumerates workstation or member server account data on a domain controller.
Enumerates domain-controller account data on a domain controller.
The result is a tuple with the following items:
A list of dictionaries, one for each user returned. Each dictionary is in the format specified by the relevant USER_INFO structure, depending on the level parameter.
The total number of items left to read before making the call. Thus, total-len(entries) are the number of entries left to read after this call.
A resume handle, that can be used to obtain the next set of users. When resume is zero, there are no more items to read.
| NetUserGetInfo |
Retrieves information about a particular user account on a server.
data = NetUserGetInfo(serverName, userName, level)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the network name of the share on which to return information.
An integer that specifies the data returned by the function. May be 0, 1, 2, 3, 10, 11, or 20.
The result is a dictionary in the format of one of the USER_INFO structures, depending on the level parameter.
| NetUserSetInfo( ) |
Sets information about a particular user account on a server.
NetUserSetInfo(serverName, userName, level, data)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the username for which the information is to be set.
An integer that specifies the data in the data parameter. May be any level corresponding to a USER_INFO structure.
A dictionary with the data for the server. The data is in the format of one of the USER_INFO structures, depending on the level parameter.
| NetUserDel( ) |
Deletes a user account from a server.
NetUserDel(serverName, userName)
A string specifying the name of the remote server on which the function is to execute. If this parameter is None, the local computer is used.
A string specifying the username to be deleted.
| I l@ve RuBoard |


2 Kings 18:13
Now in the fourteenth year of king Hezekiah did Sennacherib king of Assyria come up against all the fenced cities of Judah, and took them.


Psalms 141:7
Our bones are scattered at the grave's mouth, as when one cutteth and cleaveth wood upon the earth.


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Please is a cross-language build system with an emphasis on high performance, extensibility and reproduceability. It supports a number of popular languages and can automate nearly any aspect of your build process.
Please is designed to parallelise builds & tests to make the most of your CPU. It's written in Go so it's fast & efficient itself, and it can build just the required parts of your code on each invocation.
All your code & dependencies get packed into a single file which can easily be deployed & run on a server. This helps deploy languages like Python which don't do this naturally.
Because Please can build many languages already (and you can easily write rules to teach it about others) you always invoke one tool to build any language or run any test, simplifying your continuous integration and development processes.
We've developed Please in tandem with our internal integration tools, so whenever any of our engineers merges a pull request, we invoke it to make sure any affected tests are passing before merging it in. Because Please allows fine-grained dependencies and knows them for every test, we can run just the affected ones to keep this step fast & lightweight.
Got integration tests that need to spin up servers, write files, and find ports? No problem, by setting a flag on the test they can be run inside a Docker container to keep them from clashing with one another.


Tools for sorting and comparing documents.
Static Functions
,
|
Function
getDocSort
(
sspec
,
) {
Creates a complex sort function closing on the provided specification document. Documents are compared according to the specification.
Arguments
Returns
|
Function
A Function suitable for use with standard sorting
.
,
|
Function
getLeafSort
(
sspec
,
) {
Creates a simple sort function closing on the provided direction. Documents are compared according to the MongoDB sorting spec.
Arguments
Returns
|
Function
A Function suitable for use with standard sorting
.


The First Message Between Computers: "LO"
The internet started in 1969 with a group of programmers lead by Leonard Kleinrock. They were commissioned by ARPA, part of the US government, to enable computers in different locations to talk to each other.
The biggest problem was that large messages took too long to send and blocked the pipe. To solve this, the team came up with the idea of packet switching. All messages would be broken up into small packets that could be sent without blocking the pipe for too long. The packets would then be reassembled on the other side. Disassembling and reassembling these messages into and from packets was done with a machine called the Internet Message Processor
The first message was sent from Standford to UCLA. It was a remote login attempt that was tracked on both ends through researchers connected through a separate phone call. To log in, the letters L-O-G would need to be sent. Lo and behold, only the first two letters were sent before the system crashed. Thus we have the first message sent from the internet: "LO."
2 of 3 isn't bad, right?


Psalms 95:1
O come, let us sing unto the LORD: let us make a joyful noise to the rock of our salvation.


--- title: The significance of the sector on the salary in Sweden, a comparison between different occupational groups, part 3 author: Mikael Lundqvist date: '2020-02-29' slug: the-significance-of-the-sector-on-the-salary-in-sweden-a-comparison-between-different-occupational-groups-part-3 categories: - R tags: - plot - R Markdown - regression ---
To complete the analysis on the significance of the sector on the salary for different occupational groups in Sweden I will in this post examine the correlation between salary and sector using statistics for education.
The F-value from the Anova table is used as the single value to discriminate how much the region and salary correlates. For exploratory analysis, the Anova value seems good enough.
First, define libraries and functions.
library (tidyverse)
## -- Attaching packages --------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library (broom)
library (car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
library (sjPlot)
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
## Learn more about sjPlot with 'browseVignettes("sjPlot")'.
readfile <- function (file1){read_csv (file1, col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>%
gather (starts_with("19"), starts_with("20"), key = "year", value = salary) %>%
drop_na() %>%
mutate (year_n = parse_number (year))
}
The data table is downloaded from Statistics Sweden. It is saved as a comma-delimited file without heading, 000000CY.csv, http://www.statistikdatabasen.scb.se/pxweb/en/ssd/.
I have renamed the file to 000000CY_sector.csv because the filename 000000CY.csv was used in a previous post.
The table: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by sector, occupational group (SSYK 2012), sex and educational level (SUN). Year 2014 - 2018 Monthly salary 1-3 public sector 4-5 private sector
In the plot and tables, you can also find information on how the increase in salaries per year for each occupational group is affected when the interactions are taken into account.
tb <- readfile("000000CY_sector.csv") %>%
mutate(edulevel = `level of education`)
numedulevel <- read.csv("edulevel.csv")
numedulevel %>%
knitr::kable(
booktabs = TRUE,
caption = 'Initial approach, length of education')
| level.of.education | eduyears |
|---|---|
| primary and secondary education 9-10 years (ISCED97 2) | 9 |
| upper secondary education, 2 years or less (ISCED97 3C) | 11 |
| upper secondary education 3 years (ISCED97 3A) | 12 |
| post-secondary education, less than 3 years (ISCED97 4+5B) | 14 |
| post-secondary education 3 years or more (ISCED97 5A) | 15 |
| post-graduate education (ISCED97 6) | 19 |
| no information about level of educational attainment | NA |
tbnum <- tb %>%
right_join(numedulevel, by = c("level of education" = "level.of.education")) %>%
filter(!is.na(eduyears)) %>%
mutate(eduyears = factor(eduyears))
## Warning: Column `level of education`/`level.of.education` joining character
## vector and factor, coercing into character vector
summary_table = vector()
anova_table = vector()
for (i in unique(tbnum$`occuptional (SSYK 2012)`)){
temp <- filter(tbnum, `occuptional (SSYK 2012)` == i)
if (dim(temp)[1] > 90){
model <- lm(log(salary) ~ edulevel + sex + year_n + sector, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "none"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "none"))
model <- lm(log(salary) ~ edulevel * sector + sex + year_n, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and edulevel"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and edulevel"))
model <- lm(log(salary) ~ edulevel + sector * sex + year_n, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and sex"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and sex"))
model <- lm(log(salary) ~ edulevel + year_n * sector + sex, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and year"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and year"))
model <- lm(log(salary) ~ edulevel * sector * sex * year_n, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector, year, edulevel and sex"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector, year, edulevel and sex"))
}
}
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
anova_table <- anova_table %>% rowwise() %>% mutate(contcol = str_count(term, ":"))
summary_table <- summary_table %>% rowwise() %>% mutate(contcol = str_count(term, ":"))
merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (term.y == "sector") %>%
filter (interaction == "none") %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) +
labs(
x = "Increase in salaries (% / year)",
y = "F-value for sector"
)
merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
# only look at the interactions between all four variables in the case with interaction sector, year, edulevel and sex
filter (!(contcol.y < 3 & interaction == "sector, year, edulevel and sex")) %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) +
labs(
x = "Increase in salaries (% / year)",
y = "F-value for interaction"
)
The tables with all occupational groups sorted by F-value in descending order.
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (term.y == "sector") %>%
filter (interaction == "none") %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
select (ssyk, estimate, statistic.y, interaction) %>%
rename (`F-value` = statistic.y) %>%
rename (`Increase in salary` = estimate) %>%
arrange (desc (`F-value`)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation for F-value (sector) and the yearly increase in salaries')
| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 242 Organisation analysts, policy administrators and human resource specialists | 1.609869 |<PHONE_NUMBER> | none |
| 819 Process control technicians | 2.250895 |<PHONE_NUMBER> | none |
| 251 ICT architects, systems analysts and test managers | 2.217446 |<PHONE_NUMBER> | none |
| 331 Financial and accounting associate professionals | 1.964825 |<PHONE_NUMBER> | none |
| 962 Newspaper distributors, janitors and other service workers | 1.971413 |<PHONE_NUMBER> | none |
| 334 Administrative and specialized secretaries | 2.410127 |<PHONE_NUMBER> | none |
| 351 ICT operations and user support technicians | 2.474549 |<PHONE_NUMBER> | none |
| 241 Accountants, financial analysts and fund managers | 2.534461 |<PHONE_NUMBER> | none |
| 335 Tax and related government associate professionals | 2.586686 |<PHONE_NUMBER> | none |
| 515 Building caretakers and related workers | 2.522386 |<PHONE_NUMBER> | none |
| 321 Medical and pharmaceutical technicians | 2.493038 |<PHONE_NUMBER> | none |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.303600 |<PHONE_NUMBER> | none |
| 134 Architectural and engineering managers | 3.161068 |<PHONE_NUMBER> | none |
| 333 Business services agents | 3.001597 |<PHONE_NUMBER> | none |
| 411 Office assistants and other secretaries | 2.227235 |<PHONE_NUMBER> | none |
| 243 Marketing and public relations professionals | 1.481519 |<PHONE_NUMBER> | none |
| 264 Authors, journalists and linguists | 2.046538 |<PHONE_NUMBER> | none |
| 129 Administration and service managers not elsewhere classified | 4.059900 |<PHONE_NUMBER> | none |
| 342 Athletes, fitness instructors and recreational workers | 1.586943 |<PHONE_NUMBER> | none |
| 159 Other social services managers | 2.541205 | 69.9268014 | none |
| 123 Administration and planning managers | 3.849200 | 50.0164438 | none |
| 541 Other surveillance and security workers | 2.460130 | 41.9287342 | none |
| 235 Teaching professionals not elsewhere classified | 1.415591 | 40.1919620 | none |
| 911 Cleaners and helpers | 1.938366 | 35.3850213 | none |
| 533 Health care assistants | 2.157379 | 22.0488822 | none |
| 534 Attendants, personal assistants and related workers | 1.959595 | 21.6985964 | none |
| 332 Insurance advisers, sales and purchasing agents | 2.637486 | 19.1041252 | none |
| 131 Information and communications technology service managers | 4.000609 | 17.0841502 | none |
| 311 Physical and engineering science technicians | 2.325958 | 16.5471030 | none |
| 214 Engineering professionals | 2.626260 | 15.6152029 | none |
| 432 Stores and transport clerks | 1.231854 | 13.9155189 | none |
| 723 Machinery mechanics and fitters | 2.362984 | 12.5847785 | none |
| 532 Personal care workers in health services | 2.906578 | 6.3336230 | none |
| 512 Cooks and cold-buffet managers | 2.483280 | 5.4612144 | none |
| 732 Printing trades workers | 2.158854 | 5.2405535 | none |
| 234 Primary- and pre-school teachers | 2.985653 | 4.6801860 | none |
| 422 Client information clerks | 2.527801 | 1.7881181 | none |
| 531 Child care workers and teachers aides | 1.881615 | 0.8334164 | none |
| 611 Market gardeners and crop growers | 1.980288 | 0.3732826 | none |
| 341 Social work and religious associate professionals | 2.357787 | 0.0276157 | none |
| 941 Fast-food workers, food preparation assistants | 1.981512 | 0.0046670 | none |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "sector and sex") %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
select (ssyk, estimate, statistic.y, interaction) %>%
rename (`F-value` = statistic.y) %>%
rename (`Increase in salary` = estimate) %>%
arrange (desc (`F-value`)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation for F-value (sector and sex) and the yearly increase in salaries')
| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 911 Cleaners and helpers | 1.966955 |<PHONE_NUMBER> | sector and sex |
| 331 Financial and accounting associate professionals | 1.988628 | 84.3723061 | sector and sex |
| 342 Athletes, fitness instructors and recreational workers | 1.609407 | 53.2856268 | sector and sex |
| 351 ICT operations and user support technicians | 2.474549 | 53.1541368 | sector and sex |
| 241 Accountants, financial analysts and fund managers | 2.549161 | 39.5233707 | sector and sex |
| 333 Business services agents | 2.952973 | 32.5926396 | sector and sex |
| 611 Market gardeners and crop growers | 1.936338 | 25.7248443 | sector and sex |
| 243 Marketing and public relations professionals | 1.489860 | 24.3260135 | sector and sex |
| 533 Health care assistants | 2.157379 | 19.0714901 | sector and sex |
| 732 Printing trades workers | 2.207617 | 16.8065594 | sector and sex |
| 512 Cooks and cold-buffet managers | 2.497408 | 15.7408059 | sector and sex |
| 159 Other social services managers | 2.511558 | 15.1529257 | sector and sex |
| 123 Administration and planning managers | 3.819995 | 14.2436097 | sector and sex |
| 532 Personal care workers in health services | 2.899482 | 13.1974037 | sector and sex |
| 334 Administrative and specialized secretaries | 2.416957 | 12.8317750 | sector and sex |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.317893 | 10.2339741 | sector and sex |
| 134 Architectural and engineering managers | 3.161068 | 9.7816667 | sector and sex |
| 321 Medical and pharmaceutical technicians | 2.493038 | 8.6670096 | sector and sex |
| 941 Fast-food workers, food preparation assistants | 1.989465 | 8.6486138 | sector and sex |
| 335 Tax and related government associate professionals | 2.586686 | 8.4427211 | sector and sex |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.594999 | 8.1271264 | sector and sex |
| 723 Machinery mechanics and fitters | 2.337983 | 6.4299201 | sector and sex |
| 332 Insurance advisers, sales and purchasing agents | 2.627736 | 5.7400150 | sector and sex |
| 311 Physical and engineering science technicians | 2.316138 | 5.3473607 | sector and sex |
| 234 Primary- and pre-school teachers | 2.999161 | 5.0012138 | sector and sex |
| 129 Administration and service managers not elsewhere classified | 4.121066 | 4.6687219 | sector and sex |
| 819 Process control technicians | 2.250895 | 3.7631281 | sector and sex |
| 534 Attendants, personal assistants and related workers | 1.959595 | 3.5292601 | sector and sex |
| 131 Information and communications technology service managers | 4.029303 | 2.5233240 | sector and sex |
| 264 Authors, journalists and linguists | 2.033203 | 2.5032667 | sector and sex |
| 341 Social work and religious associate professionals | 2.357787 | 2.2988889 | sector and sex |
| 251 ICT architects, systems analysts and test managers | 2.217446 | 2.1823519 | sector and sex |
| 422 Client information clerks | 2.519631 | 2.1300830 | sector and sex |
| 432 Stores and transport clerks | 1.231854 | 1.5040767 | sector and sex |
| 541 Other surveillance and security workers | 2.457007 | 1.3945913 | sector and sex |
| 214 Engineering professionals | 2.629057 | 1.3225970 | sector and sex |
| 962 Newspaper distributors, janitors and other service workers | 1.971413 | 0.3480210 | sector and sex |
| 531 Child care workers and teachers aides | 1.879581 | 0.1432868 | sector and sex |
| 515 Building caretakers and related workers | 2.522844 | 0.1379141 | sector and sex |
| 235 Teaching professionals not elsewhere classified | 1.424488 | 0.0670314 | sector and sex |
| 411 Office assistants and other secretaries | 2.227235 | 0.0001653 | sector and sex |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "sector and edulevel") %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
select (ssyk, estimate, statistic.y, interaction) %>%
rename (`F-value` = statistic.y) %>%
rename (`Increase in salary` = estimate) %>%
arrange (desc (`F-value`)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation for F-value (sector and edulevel) and the yearly increase in salaries')
| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 335 Tax and related government associate professionals | 2.586686 | 48.2902588 | sector and edulevel |
| 234 Primary- and pre-school teachers | 2.936162 | 32.3560802 | sector and edulevel |
| 214 Engineering professionals | 2.636843 | 29.4462376 | sector and edulevel |
| 332 Insurance advisers, sales and purchasing agents | 2.420632 | 26.7105578 | sector and edulevel |
| 432 Stores and transport clerks | 1.231854 | 24.9194979 | sector and edulevel |
| 134 Architectural and engineering managers | 3.161068 | 20.3665858 | sector and edulevel |
| 321 Medical and pharmaceutical technicians | 2.493038 | 18.1556092 | sector and edulevel |
| 723 Machinery mechanics and fitters | 2.358171 | 17.3272819 | sector and edulevel |
| 311 Physical and engineering science technicians | 2.330300 | 12.9686992 | sector and edulevel |
| 123 Administration and planning managers | 3.809019 | 11.7436022 | sector and edulevel |
| 732 Printing trades workers | 2.220728 | 11.0453452 | sector and edulevel |
| 241 Accountants, financial analysts and fund managers | 2.647068 | 10.5100504 | sector and edulevel |
| 235 Teaching professionals not elsewhere classified | 1.396076 | 8.6264575 | sector and edulevel |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.264110 | 7.7952190 | sector and edulevel |
| 941 Fast-food workers, food preparation assistants | 1.981512 | 7.5766373 | sector and edulevel |
| 331 Financial and accounting associate professionals | 1.980338 | 6.9758948 | sector and edulevel |
| 532 Personal care workers in health services | 2.919933 | 6.6231457 | sector and edulevel |
| 534 Attendants, personal assistants and related workers | 1.959595 | 5.7464596 | sector and edulevel |
| 962 Newspaper distributors, janitors and other service workers | 1.971413 | 5.4435379 | sector and edulevel |
| 131 Information and communications technology service managers | 3.908111 | 4.9661376 | sector and edulevel |
| 159 Other social services managers | 2.565523 | 4.7197836 | sector and edulevel |
| 129 Administration and service managers not elsewhere classified | 4.160995 | 4.3062655 | sector and edulevel |
| 251 ICT architects, systems analysts and test managers | 2.209131 | 3.5539841 | sector and edulevel |
| 512 Cooks and cold-buffet managers | 2.435903 | 3.4242806 | sector and edulevel |
| 333 Business services agents | 2.955970 | 3.3635706 | sector and edulevel |
| 243 Marketing and public relations professionals | 1.473064 | 3.2639790 | sector and edulevel |
| 264 Authors, journalists and linguists | 2.043677 | 3.0449469 | sector and edulevel |
| 334 Administrative and specialized secretaries | 2.387467 | 2.8528940 | sector and edulevel |
| 422 Client information clerks | 2.527801 | 2.3007696 | sector and edulevel |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.612900 | 1.9599904 | sector and edulevel |
| 351 ICT operations and user support technicians | 2.474549 | 1.5903279 | sector and edulevel |
| 819 Process control technicians | 2.250895 | 1.5586112 | sector and edulevel |
| 341 Social work and religious associate professionals | 2.357787 | 1.3263497 | sector and edulevel |
| 611 Market gardeners and crop growers | 2.016345 | 1.2719035 | sector and edulevel |
| 541 Other surveillance and security workers | 2.460130 | 1.0672924 | sector and edulevel |
| 411 Office assistants and other secretaries | 2.227235 | 1.0114046 | sector and edulevel |
| 515 Building caretakers and related workers | 2.526100 | 0.8254389 | sector and edulevel |
| 342 Athletes, fitness instructors and recreational workers | 1.540952 | 0.8230839 | sector and edulevel |
| 531 Child care workers and teachers aides | 1.897863 | 0.7969406 | sector and edulevel |
| 533 Health care assistants | 2.157379 | 0.5425248 | sector and edulevel |
| 911 Cleaners and helpers | 1.938366 | 0.0965584 | sector and edulevel |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "sector and year") %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
select (ssyk, estimate, statistic.y, interaction) %>%
rename (`F-value` = statistic.y) %>%
rename (`Increase in salary` = estimate) %>%
arrange (desc (`F-value`)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation for F-value (sector and year) and the yearly increase in salaries')
| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 129 Administration and service managers not elsewhere classified | 5.8528187 | 17.1667457 | sector and year |
| 351 ICT operations and user support technicians | 3.2455362 | 16.6101284 | sector and year |
| 334 Administrative and specialized secretaries | 1.0672775 | 14.9508269 | sector and year |
| 534 Attendants, personal assistants and related workers | 2.2769477 | 12.4036331 | sector and year |
| 422 Client information clerks | 3.2733483 | 11.6812523 | sector and year |
| 962 Newspaper distributors, janitors and other service workers | 2.4907346 | 11.5673142 | sector and year |
| 264 Authors, journalists and linguists | 2.9905121 | 10.5945994 | sector and year |
| 531 Child care workers and teachers aides | 2.4510286 | 9.5936100 | sector and year |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.3355826 | 7.7816291 | sector and year |
| 432 Stores and transport clerks | 0.5332567 | 7.4196762 | sector and year |
| 243 Marketing and public relations professionals | 2.1828057 | 5.5660180 | sector and year |
| 732 Printing trades workers | 2.8825380 | 4.3716553 | sector and year |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.8099783 | 3.6786224 | sector and year |
| 611 Market gardeners and crop growers | 2.3161238 | 3.3017939 | sector and year |
| 131 Information and communications technology service managers | 3.3504930 | 2.8750373 | sector and year |
| 532 Personal care workers in health services | 2.7411030 | 2.8290655 | sector and year |
| 235 Teaching professionals not elsewhere classified | 1.8047571 | 2.7841902 | sector and year |
| 311 Physical and engineering science technicians | 2.8608741 | 2.6173373 | sector and year |
| 533 Health care assistants | 1.9920113 | 2.2486864 | sector and year |
| 214 Engineering professionals | 3.0209963 | 2.0446973 | sector and year |
| 723 Machinery mechanics and fitters | 2.6258864 | 1.4272778 | sector and year |
| 515 Building caretakers and related workers | 2.3423357 | 1.3492650 | sector and year |
| 321 Medical and pharmaceutical technicians | 2.1376334 | 1.2494762 | sector and year |
| 941 Fast-food workers, food preparation assistants | 2.1906312 | 1.1458297 | sector and year |
| 234 Primary- and pre-school teachers | 3.1522849 | 0.8075167 | sector and year |
| 411 Office assistants and other secretaries | 2.4217447 | 0.7799156 | sector and year |
| 241 Accountants, financial analysts and fund managers | 2.2625663 | 0.7751592 | sector and year |
| 541 Other surveillance and security workers | 2.3018747 | 0.6646888 | sector and year |
| 134 Architectural and engineering managers | 3.3811821 | 0.5852232 | sector and year |
| 333 Business services agents | 3.2520012 | 0.5424390 | sector and year |
| 335 Tax and related government associate professionals | 2.3986233 | 0.3139375 | sector and year |
| 123 Administration and planning managers | 4.0945884 | 0.2682143 | sector and year |
| 911 Cleaners and helpers | 2.0082873 | 0.2021948 | sector and year |
| 341 Social work and religious associate professionals | 2.3994292 | 0.0754320 | sector and year |
| 342 Athletes, fitness instructors and recreational workers | 1.6673896 | 0.0633281 | sector and year |
| 512 Cooks and cold-buffet managers | 2.4249631 | 0.0494622 | sector and year |
| 819 Process control technicians | 2.2225873 | 0.0378303 | sector and year |
| 331 Financial and accounting associate professionals | 2.0557505 | 0.0377336 | sector and year |
| 251 ICT architects, systems analysts and test managers | 2.2536138 | 0.0294035 | sector and year |
| 332 Insurance advisers, sales and purchasing agents | 2.6015664 | 0.0125275 | sector and year |
| 159 Other social services managers | 2.5487758 | 0.0013436 | sector and year |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 1) %>%
filter (interaction == "sector, year, edulevel and sex") %>%
filter (!(contcol.y < 3 & interaction == "sector, year, edulevel and sex")) %>%
mutate (estimate = (exp(estimate) - 1) * 100) %>%
select (ssyk, estimate, statistic.y, interaction) %>%
rename (`F-value` = statistic.y) %>%
rename (`Increase in salary` = estimate) %>%
arrange (desc (`F-value`)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation for F-value (sector, year, edulevel and sex) and the yearly increase in salaries')
| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 264 Authors, journalists and linguists | 2.0717871 | 5.0646298 | sector, year, edulevel and sex |
| 311 Physical and engineering science technicians | -1.9358596 | 3.3593399 | sector, year, edulevel and sex |
| 159 Other social services managers | 2.1339049 | 2.5206357 | sector, year, edulevel and sex |
| 134 Architectural and engineering managers | -0.2221716 | 2.4150753 | sector, year, edulevel and sex |
| 331 Financial and accounting associate professionals | 0.2989757 | 2.2892768 | sector, year, edulevel and sex |
| 342 Athletes, fitness instructors and recreational workers | 2.7799167 | 2.1976399 | sector, year, edulevel and sex |
| 214 Engineering professionals | 5.2732904 | 1.9988132 | sector, year, edulevel and sex |
| 723 Machinery mechanics and fitters | 2.1334983 | 1.8843643 | sector, year, edulevel and sex |
| 432 Stores and transport clerks | -0.2883737 | 1.8215339 | sector, year, edulevel and sex |
| 241 Accountants, financial analysts and fund managers | 2.6268377 | 1.8184489 | sector, year, edulevel and sex |
| 533 Health care assistants | 1.3176280 | 1.6357939 | sector, year, edulevel and sex |
| 911 Cleaners and helpers | 1.1875630 | 1.6319874 | sector, year, edulevel and sex |
| 129 Administration and service managers not elsewhere classified | 16.0932403 | 1.5716145 | sector, year, edulevel and sex |
| 512 Cooks and cold-buffet managers | 2.2335677 | 1.4882138 | sector, year, edulevel and sex |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.9377802 | 1.4494844 | sector, year, edulevel and sex |
| 234 Primary- and pre-school teachers | 2.6286211 | 1.4408203 | sector, year, edulevel and sex |
| 235 Teaching professionals not elsewhere classified | 1.4806649 | 1.4162230 | sector, year, edulevel and sex |
| 532 Personal care workers in health services | 3.0809717 | 1.3439893 | sector, year, edulevel and sex |
| 962 Newspaper distributors, janitors and other service workers | 1.7217534 | 1.3344044 | sector, year, edulevel and sex |
| 332 Insurance advisers, sales and purchasing agents | 4.5515134 | 0.9604905 | sector, year, edulevel and sex |
| 123 Administration and planning managers | 1.6860436 | 0.9155189 | sector, year, edulevel and sex |
| 541 Other surveillance and security workers | 3.1372549 | 0.9113280 | sector, year, edulevel and sex |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.0956711 | 0.9044780 | sector, year, edulevel and sex |
| 422 Client information clerks | 7.6231100 | 0.8611877 | sector, year, edulevel and sex |
| 515 Building caretakers and related workers | 3.8284291 | 0.8343329 | sector, year, edulevel and sex |
| 334 Administrative and specialized secretaries | -1.8021942 | 0.7940149 | sector, year, edulevel and sex |
| 531 Child care workers and teachers aides | 2.9556278 | 0.7580341 | sector, year, edulevel and sex |
| 131 Information and communications technology service managers | 6.2536436 | 0.7429188 | sector, year, edulevel and sex |
| 243 Marketing and public relations professionals | -0.7939038 | 0.7338409 | sector, year, edulevel and sex |
| 351 ICT operations and user support technicians | 3.2312302 | 0.7188959 | sector, year, edulevel and sex |
| 732 Printing trades workers | 3.9993823 | 0.6905319 | sector, year, edulevel and sex |
| 321 Medical and pharmaceutical technicians | 2.2164674 | 0.6326293 | sector, year, edulevel and sex |
| 611 Market gardeners and crop growers | 2.2753348 | 0.6157880 | sector, year, edulevel and sex |
| 341 Social work and religious associate professionals | 3.0666620 | 0.5266982 | sector, year, edulevel and sex |
| 941 Fast-food workers, food preparation assistants | 2.7940568 | 0.4253642 | sector, year, edulevel and sex |
| 335 Tax and related government associate professionals | 5.0470456 | 0.3217115 | sector, year, edulevel and sex |
| 251 ICT architects, systems analysts and test managers | 2.9031256 | 0.2684425 | sector, year, edulevel and sex |
| 819 Process control technicians | 1.6741717 | 0.2603997 | sector, year, edulevel and sex |
| 333 Business services agents | 1.8143344 | 0.1540308 | sector, year, edulevel and sex |
| 411 Office assistants and other secretaries | 5.5231019 | 0.0911334 | sector, year, edulevel and sex |
| 534 Attendants, personal assistants and related workers | 2.6927719 | 0.0899580 | sector, year, edulevel and sex |
Let’s check what we have found.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "242 Organisation analysts, policy administrators and human resource specialists")
model <- lm (log(salary) ~ year_n + eduyears + sector + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "941 Fast-food workers, food preparation assistants")
model <-lm (log(salary) ~ year_n + eduyears + sector + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "911 Cleaners and helpers")
model <- lm (log(salary) ~ year_n + eduyears + sector * sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sex", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "411 Office assistants and other secretaries")
model <- lm (log(salary) ~ year_n + eduyears + sector * sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sex", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "335 Tax and related government associate professionals")
model <- lm (log(salary) ~ year_n + eduyears * sector + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sector"))
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "911 Cleaners and helpers")
model <- lm (log(salary) ~ year_n + eduyears * sector + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sector"))
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "129 Administration and service managers not elsewhere classified")
model <- lm (log(salary) ~ year_n * sector + eduyears + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "year_n", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "159 Other social services managers")
model <- lm (log(salary) ~ year_n * sector + eduyears + sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "year_n", "sector"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "264 Authors, journalists and linguists")
model <- lm (log(salary) ~ year_n * eduyears * sector * sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "year_n", "sex", "sector"))
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
## TableGrob (2 x 1) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (2-2,1-1) arrange gtable[layout]
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "534 Attendants, personal assistants and related workers")
model <- lm (log(salary) ~ year_n * eduyears * sector * sex, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "year_n", "sex", "sector"))
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
## TableGrob (2 x 1) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (2-2,1-1) arrange gtable[layout]


Provides a custom JUnit 4.5 Runner
that takes a screenshot of failed GUI tests executed, for example, in an
IDE.
The custom JUnit Runner
, {@link org.fest.swing.junit.v4_5.runner.GUITestRunner}
, can
take screenshots of failed GUI tests. To use it, just annotate your test class with
"@RunWith(GUITestRunner.class)
." Screenshots of failed tests will be saved in the directory
"failed-gui-tests" (relative to the directory where tests are executed.)
GUITestRunner
has been tested with Eclipse 3.4.1. It is currently not working with IntelliJ IDEA due to bug
IDEA-13389.


NOTE: SITE WILL BE READY ON WEDNESDAY 29TH 2017 AT 12:45PM GMT.. PLEASE COME BACK AND GIVE US A GO....


What's new in Spark 2.0?
Who am I
- Dennis Hunziker
- Software Engineer, currently working for BlackRock
- Originally from Zurich, Switzerland
- Working on projects using Spring XD, Cassandra, Neo4j and more recently, Spark (with Parquet)
- Spending offline time with 3 yr old Pomeranian Billy, training Muay Thai and preparing for the Brighton Marathon
About this Talk
- Spark 2.0 was released in July 2016
- Since then, versions 2.0.1 and 2.0.2 have been released, containing more than 300 stability and bug fixes
- Main selling points of version 2.0 are the improvements in API usability and performance
- Not covering MLlib and SparkR
What's Changed
Changes in behaviour that should be considered before migrating:
- Default build is using Scala 2.11 instead of 2.10
- Floating point literals are being inferred as Decimal types
- Parquet summary files no longer written by default
- Some breaking changes to the JavaRDD were made (e.g. mapPartitions and countByKey), those issues were already present in 1.0
What's Going
Features that might get removed in further 2.x releases:
- Java 7 support (Last released Apr 2015)
- Python 2.6 support (Last released Oct 2013)
- Fine-grained mode in Apache Mesos
What's Gone
Some features that were removed in this release:
- org.apache.spark.Logging
- Methods deprecated in Spark 1.x
- Some advanced streaming receivers (e.g. Twitter and Akka)
- DataFrame no longer exists as a separate class
- Hadoop 2.1 support
SparkSession
val spark = SparkSession.builder() // Replaces SQLContext
.appName("My App")
.master("local[2]")
.enableHiveSupport() // Replaces HiveContext
.getOrCreate()
Example Data
- Dog density data available under Open Government Licence
- CSV with 2 columns, postcode and estimated dog population
- Postcode data from doogal.co.uk
PostcodeDistrict,EstimatedDogPopulation
AB10,"1,921.75"
AB11,"2,377.35"
AB12,"4,130.35"
Dataset
- Supports primitive types, case classes and tuples
- Use as[type] to turn DataFrame into typed Dataset
- Provides additional compiler support for writing correct logic
- When reading data, type and/or schema can be provided, but inference doesn't yet work consistently
- Creating additional types for query results isn't very convenient
DataFrame
- Is now just a type alias for Dataset[Row]
- Get values using ResultSet style accessors (e.g. getString(Int), getInt(Int))
- Feels more SQL-like to use, especially for joins
Accumulator V2
- New API available in AccumulatorV2, can be extended to support user defined types
- Meant to perform better than the previous implementation
// AccumulatorV2, keeps track of sum and count
val acc2 = spark.sparkContext.doubleAccumulator
dogDensity.foreach(row =>
acc2.add(row.estimatedDogPopulation))
New Features
- CSV supported natively without having to include additional packages
- Off-heap StorageLevel available for use in persisting/caching DataFrame
- If enabled, execution engine might make use of off-heap allocation as well
- Complete support of the SQL2003 standard including sub-queries
Performance Improvements
- The Catalyst optimizer has been improved
- Introduction of whole-stage code generation, which collapses entire queries into single functions
- DataFrames sourced from many files are coalesced automatically
Structured Streaming
- New API to run incremental queries continuously
- Not part of the Spark Streaming project
- The aim was to support the same operations as batch queries for for streams
- It's an ALPHA release part of 2.0 and not yet ready for production
Thank You!


The Secret Sharing API allows data—what we'll refer to as artifacts—to a server using Shamir's Secret Sharing Scheme.
Secret sharing works like this: suppose you have a secret encrypted message shared among 20 (n
) people. Each of these people own a partial key to unlock this secret message. In order to access the secret, you must have at least 10 (k
) of these partial keys—any 10 will do. In our case, suppose that the secret message is a search query and the people who share the secret are all of the n
users that have submitted that search query. In order to reveal a search query, we may require that it have been submitted by at least k
people. If each of the n
users that have submitted that search query upload the encrypted query and their partial key, and if n ≥ k
, then that search query will be decryptable.
Now let's define what artifacts are. An artifact is an object with three fields: a primary field, a secondary field, and a count field. E.g.,
{ primaryData: "how many calories in an oatmeal cookie", secondaryData: "", count: 1 }
The primary and secondary data fields will be encrypted separately, but using the same key. The count is not encrypted. The encrypted fields and the count are then encrypted with the server's public key:
serverPubKeyEncypted( [encrypted(primaryField), encrypted(secondaryField), count] )
The encrypted primary field is the "shared secret". That is, k
users must share exactly the same primary field in order for the primary and secondary fields of the corresponding tuples to be decrypted. In the simple example above, the primary data field can only be decrypted if at least k
users uploaded tuples with a primary field with the value "how many calories in an oatmeal cookie
".
The secondary field is a way of allowing additional information to be uploaded that is not required to be shared among k
users, but can only be decrypted if the primary field is shared by k
users. This is a slippery slope, so be careful using it. The motivation behind including it is to allow, e.g., the canonical and original form of a query phrase to be revealed, while only requiring the canonical form to be shared. The canonical may be the original query converted to all lower-case and with non-alpha-numeric characters stripped. The secondary field may be the original query in it's complete form (e.g., mixed casing and with non-alpha-numeric characters), like this:
{ primaryData: "how many calories in an oatmeal cookie", secondaryData: "How many calories in an oatmeal cookie?", count: 1 }
Use the secondary field wisely and be clear with users if you intend to use the secondary field.
api.privacy.secretSharing.packAndSendArtifacts
artifacts
primaryData
, secondaryData
, and count
.
id
n
k
anonymizers
serverPubKey
userId
pass_phrase
preference, if set, or a random string.
on_success
on_error
Uploads a set of artifacts in batches, each batch being assigned randomly to one of the anonymizers. Artifacts are encrypted using the secret sharing scheme and then encrypted with the servers public key.


Single Page Apps in Salesforce
A scalable workflow for React and Angular projects on Force.com
React vs Lightning


This website pulls in the latest data from Johns Hopkins University regarding the coronavirus outbreak within the United States. Hover over each state in the map to get basic data regarding the virus in that state. You may also look at more state specific data by switching over to the 'State Data' tab. And finally, if you would like to see whether or not 'the curve has flattened', feel free to see the 'Graph' tab for the total picture of the coronavirus in the US. In addition, if you want to donate to the fight against coronavirus, feel free to go to the 'Donate' page. And, as always, stay safe!


 Gamin'.
We set you up with other folks, in popular games such as Call of Duty Warzone, while dabbling in Among Us and finding other fun events to host for you and your friends to engage in.
Look for some of our streams & past uploads on Twitch, YouTube, you can also check out a few of our favorite highlights.
Feel free to contact us at fatstaxxllc[at]gmail.com if you'd like for us to give you a shoutout on stream!


If your contribution is not trivial (not a typo fix, etc.), we can only accept it if you dedicate your copyright for the contribution to the public domain. Make sure you understand what it means (see http://unlicense.org/)! If you agree, please add yourself to AUTHORS.md file, and include the following text to your pull request description or a comment in it:
I dedicate any and all copyright interest in this software to the
public domain. I make this dedication for the benefit of the public at
large and to the detriment of my heirs and successors. I intend this
dedication to be an overt act of relinquishment in perpetuity of all
present and future rights to this software under copyright law.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.


Blissymbol board 0.8.0 (a subset of Blissymbols)
(files)
(a note: physical keyboard keys can be linked to buttons)


 Her funeral, which took place today, was attended by Amitabh Bachchan, Rekha, Farhan Akhtar and Shibani Dandekar and others
Shabana Azmi’s mother Shaukat Kaifi breathed her last on Friday due to age-related issues.(Image Source:- Viral Bhayani)
Many Bollywood celebrities have come to her residence to pay their last respects and share the family's grief.(Image Source:- Viral Bhayani)
In attendance, we see Bollywood megastars Amitabh Bachchan and Rekha arriving at their residence to offer their condolences.(Image Source:- Viral Bhayani)
Celebs like Tabu, Urmila Matondkar and Saiyami Kher can be seen in the picture clicked outside her residence.(Image Source:- Viral Bhayani)
Shabana Azmi with Tanvi Azmi head towards the crematory(Image Source:- Viral Bhayani)
Padmaavat actress Aditi Rao Hydari and Waiting actor Naseeruddin Shah also came in to pay their last respects to the late actress Ms Kaifi.(Image Source:- Viral Bhayani)
Shabana Azmi’s son Farhan Akhtar also seen with his girlfriend Shibani Dandekar at the funeral (Image Source:- Viral Bhayani)


638_yangchris11.py
class Solution:
def minCostConnectPoints(self, points: List[List[int]]) -> int:
costs = [[0] * len(points) for _ in range(len(points))]
n = len(points)
for i in range(n):
for j in range(i+1, n):
if i != j:
costs[i][j] = abs(points[i][0]-points[j][0]) + abs(points[i][1]-points[j][1])
costs[j][i] = abs(points[i][0]-points[j][0]) + abs(points[i][1]-points[j][1])
def min_key(key, mstSet):
min = float('inf')
for v in range(n):
if key[v] < min and mstSet[v] == False:
min = key[v]
min_index = v
return min_index
key = [float('inf')] * n
parent = [None] * n
key[0] = 0
mstSet = [False] * n
parent[0] = -1
for cout in range(n):
u = min_key(key, mstSet)
mstSet[u] = True
for v in range(n):
if costs[u][v] > 0 and mstSet[v] == False and key[v] > costs[u][v]:
key[v] = costs[u][v]
parent[v] = u
ans = 0
for i in range(1,n):
ans += costs[i][parent[i]]
# print(parent[i], "-", i, "\t", costs[i][ parent[i]])
return ans


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About this blog/me
September 24, 2014
Hello! Tea, anyone ? ☺
—>
Well, well, well!
This has been brewing in the kettle of my heart/mind since long…
And the time has finally come to serve 1 it!
Confession:
I am an ardent lover of Tea—a fact my friends all over the world can vouch for.
(No offence meant; you are free to substitute Tea with Coffee or whatever beverage (or activity!) that you love with the same connotation. My dear friend Vijay would definitely go for Coffee, while my dear wife Rajeshwari would clearly choose fresh fruit-juice!)
And dunking 2 biscuits in piping hot-tea is such a ‘Zen’-experience in itself! :)
Just a tiny cup o’ tea makes me
-
…feel “i” cease to exist for the duration of sipping it (I only realize it later!)…
-
…get “LOST” in a trance of THOUGHT-LeSS-ness, and NO-MIND-ness,
-
…move to a state of EMPTY-ness & joy-filled ALONE-ness despite being in the company of my loved & loving ones,
-
…feel a strange kind of WARMTH & BLiSS that is, to put it in my way, PRiCE-Le$$ ! :)
To “FIND” (out) Something beautiful, we need to get “LOST” sometimes!
Don’t you agree ? :)
Well, when I am in India—both in my native village as well as in Bangalore, it is usually hot tea(with milk) laced with aromatic Lemon-Grass, accompanied by melt-in-the-mouth goodness of Parle-G biscuits! ♥ :)
And when I was in the UK, thanks to my loving friends, it was tea with the classic Mc-Vities Digestives:
(the above image is courtesy The Telegraph, UK, in a recent story)
But, it’s not just about Biscuits and Tea!
+ + =
Through this site,
-
I want to share some stories/experiences that are as ‘beautiful’ & ‘en-lightening’ as are moments of dipping into your favourite book with a cup of hot tea and snacks during chilly mornings or drizzly late-noons & evenings. :)
-
I want to share my little “EUREKA!”, “AHA!”, and even the “WTF!" moments (the last being especially ubiquitous these days in software); the epiphanies, along with (m)any other learnings connected to Computers or otherwise through this blog.
‘Feeling’ the presence of a beautiful poem, story, equation, program, function, algorithm, song, pattern, or an image gives me the same kind of ‘kick’ that a cup of hot tea does! :)
And hence the parallel with tea.
And me…?
Oh, by the way, I am Raghu Ugaré — a passionate programmer, and an avid Art, Science & Math student for life.
I love to play with IDEAS, PATTERNS, WORDS & SYMBOLS (not realizing that it might be the other way round ?!?).3
I love ALL kinds of patterns be it in Nature, Philosophy, Religion, Science, Mathematics, Painting, Prose, Poetry, Music, Programming, etc., and the mysterious connections and unity underneath them all.
I love typography and am a font-fanatic.4
I love to build simple models from which I try to ‘mine’ & ‘glean’ useful data & insights.
Above all, I love to SHARE my discoveries with my dear friends! :)
I am indeed incredibly blessed to have come into the contact of some truly great gurus & brilliant friends from diverse fields, & I want to share all that I can possibly recollect & deem worthy of sharing that I learnt from them, directly or otherwise.
Please keep reading these free-flowing ‘letters’ to YOU — I hope they make you smile, for a while! :)
Do visit my personal blog for thoughts on other topics…
Recently, I also wrote an article on Medium about the Genius of Sreenivasa Ramanujan.
Have a wonder-filled time with your dear ones!
Love & Best Wishes,
Raghu Ugaré
Planet Earth
❝ We design our world, while our world acts back on us and designs us. ❞
– Anne-Marie Willis
Footnotes
-
“serve” on the web, of course! :) ↩
-
Yes, ‘dunk’ rather than just ‘dip’. Look it up by clicking on the word. :) ↩
-
Sometimes, I do wonder: “Does our Mind manipulate IDEAS, PATTERNS, WORDS & SYMBOLS or is it in fact, manipulated/shaped by them?” ↩
-
You will no doubt be witness to a constant ‘flux’ of changes in the typefaces (fonts) on this site as I keep experimenting! ↩


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Partial Bibliography of Published Information about Astronomy Camp
Select an item below to jump to a specific section of the page!
Jump to Contents
If you know of any other published information about Astronomy Camp which is not included in this list, please send a reference and/or copy to Dr. McCarthy.
Jump to Contents
- Amateur Radio Contact with the Space Station
- 25th Anniversary
- Poetry
- Contributions to Educational Research
- Major Articles About Astronomy Camp
- Articles Published by Astronomy Campers
- Articles About Educational Activities
- Contributions to Astronomical Research
- Short Articles, Testimonials, References
- Images
If you know of any other published information about Astronomy Camp which is not included in this list, please send a reference and/or copy to Dr. McCarthy.
Amateur Radio Contact with the Space Station (June 8, 2013)
25th Anniversary
- McCarthy, D., 2012, "Lighting the Fire." Mercury, vol. 41, #2, pp. 16-21.
- "2012 Education Prize" of the American Astronomical Society: UANews; AZ Daily Wildcat; AZ Daily Star; ASP
- McCarthy, D. Podcast (October 17, 2011) concerning Astronomy Camp's 25th anniversary. Also available at iTunesU by subscribing to Steward Observatory's Public Evening lecture series.
- Sweet, W., 2012, "Sky Guy." Tucson Lifestyle magazine, Sept. 2012, vol. 31, #9, p. 15-16.
Poetry
- "Detour" Ms. Lindsay Renick-Mayer (Advanced Teen Camper 1999)
- "Mt. Lemmon Steward Observatory, 1990" Ms. Alison Deming (Adult Camper 1990), (Darkmatter - Poems of Space; Calouste Gulbenkian Foundation, 2008; edited by Maurice Riordan and Jocelyn Bell Burnell), pp. 88-92
Contributions to Educational Research
- McCarthy, D., and Lockwood, J., 2013, "Authentic Research in Science Education and Outreach"Astronomy Beat, #113, July 17, 2013.
- Fields, D. A., 2009, "What do Students Gain from a Week at Science Camp? Youth Perceptions and the Design of an Immersive Research-oriented Astronomy Camp." International Journal of Science Education, 31(2), 151-171.
- Fields, D. A., 2007, "Identities and Astronomy Camp: How Individual Campers Make Meaning of Science Experiences." Proceedings of the Seventh International Conference of the Learning Sciences. Mahweh: NJ, pp. 920-921.
- Fields, D. A., 2006, "What do Students Gain from a Week at Science camp? Youth Perceptions and the Design of an Immersive Research-oriented Astronomy Camp." Proceedings of the National Association of Research in Science Teaching Conference, CD-ROM.
- McCarthy, Deborah, 2002, "An Exploratory Study of Advanced Astronomy Camp." Master's thesis, Department of Curriculum and Instruction, University of Wisconsin-Madison, 96 pages.
Major Articles About Astronomy Camp
- Buckley, E., 2014. "Discovering the Universe at Astronomy Camp", Mercury, vol. 43, #1, pp. 28-34.
- Whitehouse, M., 2012, "Creative Teaching with Astronomically Inspired Music." Mercury, vol. 41, #2, pp. 22-25.
- Levy, D. H., 2010, "Camp to the Stars." Astronomy, December 2010, vol. 38, #12, pp. 58.
- McCarthy, D. and Levy, D. H., 2010, "A Famous Telescope Turns 40." Astronomy, July 2010, vol. 38, #7, pp. 52-53.
- McCarthy, D., Lebofsky, L, Higgins, M., and Lebofsky, N., 2010, "The James Webb Space Telescope's Near-Infrared Camera: Making Models, Building Understanding." Mercury, vol. 39, #4, pp. 29-32.
- McCarthy, D. W. 2000, "High Observatory Adventure at Astronomy Camp." Sky & Telescope, April 2000, vol. 99, #4, pp. 80-83.
- Dollar, T. and McCain, E. 1998, "In the Realm of the Long Eyes." Arizona Highways, August 1998, pp. 7-11.
- Levy, D. H. 1994, "Sharing the Thrill of Discovery." In Star Trails column, Sky & Telescope, vol. 88, pp. 95-96.
- Hooper, E. J., & McCarthy, D. W. 1993, "Astronomy Camp: Adventures in Scientific Research." Mercury, vol. 22, pp. 24-29.
- Rinard, J. E. 1993, "Seeing Stars." National Geographic World, #215, pp. 7-11.
- Walker, K., & Mortimer, J. P. 1993, "With Stars in Their Eyes." Arizona Highways,vol. 69, #6, pp. 32-37.
- Regester, J. 1992, "Reaching for the Stars at Astronomy Camp." Stardate, vol. 20, pp. 16-19.
- "Astronomy Summer Camp." Sky & Telescope, Feb. 1990, vol. 79, p. 207.
- "How & Why." column, The Boston Globe, March 12, 1990, p. 41.
- Rico, G. 1988, "Wide-eyed about night sky. " Tucson Citizen newspaper, June 16, p. 6D.
Articles Published by Astronomy Campers
- Buckley, E., 2014, listed in "major articles" above
- Whitehouse, M., 2013, listed in "major articles" above
- Gibbons, J. 2010. "University of Arizona Astronomy Camp", Planetary Studies Foundation Newsletter, vol. 20.3, #3, pp. 10-11.
- Howard, J. 2010. "A Positive Inspiration", Astronomy, November 2010, vol. 38, #11, p. 10.
- Joseph, E. 2010. "Mountain Climbing", Think: The Magazine of Case Western Reserve University, fall/winter 2010 issue.
- Cheran, G. 2008. "A Week at Astronomy Camp", The Reflector: Quarterly Magazine of the Astronomical League, December 2008, pp. 12-15.
- Cheran, G. 2008. "A Week at Astronomy Camp", NOVAC: The Newsletter of the Northern Virginia Astronomy Club, July/August #135, vol. 30, p. 3,5
- Morlan, R. and Guynn, C. 2006, "Adult Beginners Astronomy Camp Mt. Lemmon." Astromart, Dec. 20
- Cendes, Y. 2005, "Astronomy Camp Adventures." Universe Today, July 27,
- Waterhouse, E. N. 1997, "The Children of the Blue Marble." Mercury, vol. 26, 10-15.
- St. Clair, R. W. J. 1997, "First Contact." Mercury, vol. 26, 18-19.
- Glennie, L. 1997, "Coming Full Circle." Mercury, vol. 26, 7.
- Waterhouse, E. 1996, "I Hope that I Could Come." Mercury, vol. 25, #4, pp. 23-24.
- Sinclair, C. 1991, "A Bunch of Happy (Astronomy) Campers." Astronomy, vol. 19, pp.16-17.
Articles About Educational Activities
- McCarthy, D., Lebofsky, L., Higgins, M., and Lebofsky, N., 2010, "The James Webb Space Telescope's Near Infrared Camera: Making Models, Building Understanding", Mercury, autumn issue 2010, Vol. 39, #4, pp. 29-32.
- Kern, S., & McCarthy, D. 2000, "Making Your Own Astronomical Camera." The Universe in the Classroom, No. 50, Second Quarter 2000, 1-4.
- Regester, J., & McCarthy, D. 1997, "A Liquid Nitrogen Cannon." submitted to The Physics Teacher.
Contributions to Astronomical Research
- Spectroscopic Classification of Supernovae (20)
- Discovery of a Near-Earth Object NEO asteroid 2010 KC at the Advanced Adult Astronomy Camp, May 14-18, 2010. The 60" Prime Focus Camera was used to discover this object.
- Discovery of a Near-Earth Object NEO asteroid 2010 JH110 at the Advanced Adult Astronomy Camp, May 14-18, 2010. The 60" Prime Focus Camera was used to discover this object.
- Benecchi, S., Noll, K., Grundy, W., and Levison, H., 2010, "(47171) 1999 TC36, A Transneptunian Triple," Icarus, 207, 978-991.
- Koch, Ingrid, 2008, "Observation, Speed, and Light Curve Estimation of the Asteroid 5992 Nittler," American Astronomical Society, DPS meeting #40, #28.17; results from the 2008 Advanced Astronomy Camp.
- Discovery of a Near-Earth Object Apollo asteroid 2005 MG5 at the Advanced Teen Astronomy Camp, June 24 - July 1, 2005. The 60" Prime Focus Camera was used to discover this object.
- Discovery of a Near-Earth Object Apollo asteroid 2005 MA at the Astronomy Camp for Educators, June 13-17, 2005. The 60" Prime Focus Camera was used to discover this object which approached Earth within 2-3 lunar distances.
- Spahr, T., Marsden, B., Bialozynski, J., Dietrich, D., Greenberg, C., Hooper, E., McBee, D., McCarthy, D., Pici, J., Rudnick, G., Vedeler, C., and Hergenrother, C. 1999, "Comet C/1999 F1 (Catalina).", IAU Circular, 7148, 1. (Confirmation of a comet with the CCD camera at the 61-inch telescope during Astronomy Camp April 1999).
- Pickering, T., Impey, C., van Gorkom, J., and Bothun, G. 1997, "Neutral Hydrogen Distribution and Kinematics of Giant Low Surface Brightness Disk Galaxies," Astronomical Journal, 114, 1858-1882. (Image of Malin 1 in the V-band obtained with the 2048x2048 pixel CCD at the 61-inch telescope during Astronomy Camp May 1996).
Short Articles, Testimonials, References
- McCarthy, D. "Astronomy Camps XXIV," NOAO Newsletter, #104 (September 2011), pp. 38-39.
- McCarthy, D. "Advanced Astronomy Campers Converge for a Week at KPNO," NOAO Newsletter, #102 (September 2010), pp. 36-37.
- McCarthy, D. "Advanced Astronomy Camp at the WIYN 0.9-meter," WIYN Observatory Newsletter, (October 2009), pp. 1-2.
- Garmany, K. and McCarthy, D. "Astronomy Camps 2009 on Kitt Peak," NOAO/NSO Newsletter, #99 (September 2009), pp. 41-43.
- "Students and Teachers Join Astronomers on the WIYN 0.9-meter Telescope," College of Letters & Science, University of Wisconsin-Madison Newsletter, #95 (July 2009).
- McCarthy, D. "Linking Girls to the Sky," Steward Observatory Newsletter, (December 2008).
- Hooper, E. "A Research Adventure for Tucson Educators," NOAO/NSO Newsletter, #95 (September 2008), pp. 47-48.
- Hooper, E. "A Research Adventure for Tucson Educators," WIYN Newsletter, July 2008, pp. 7-8.
- Conover, A. "Touring the Skies from the Mt. Lemmon SkyCenter," Research Corporation Newsletter, 2008
- Rochlin, J. 2003,"Putting Stars in Our Eyes," Arizona Alumnus, Spring 2003
- Nyland, Kristina, 2001, "Astronomy Camp." Reflections of the University Lowbrow Astronomers Newsletter , 2001
- McCarthy, D. 1996, "A 'Road Map' for Science Education." Announcer, American Assoc. of Physics Teachers (winter meeting Jan. 1997), vol. 26, p. 87.
- Goldman, S. J. 1995, "Astronomy on the Internet." Sky & Telescope, August 1995, vol. 90, no. 2, p. 21.
- Baldwin, M. 1993, "What's in the Sky?" North Vernon Plain Dealer, Butlerville, IN, December 2, 6B.
- "Cosmic Camp." Lo Que Pasa, July 12, 1993, vol 16, p. 12 & cover.
- Arizona Alumnus, Spring 1993, vol 70. back cover
- "Astronomy Camps Stir Enthusiasm for Science." Lo Que Pasa, Feb. 1, 1993, vol 16, p. 3.
- Brand, J. 1991, "Astronomy Camp." Bulletin (Indiana Astronomical Society), December, 7.
- "Astronomy Camp Programs for Adults and Youth." Arizona Alumnus, Fall 1990, vol 68, p. 5.
Images
- Block et. al., NGC 3718, Astronomy Picture of the Day, August 31, 2006.
- Regester, J. 1997, "Transparent dome" photograph of the 40-inch reflector, Sky & Telescope, April 1999, vol. 97, no. 4, p. 93.
- Kirkpatrick, J.D. et al. 1991, Image of the Ring Nebula (M57), Astronomy magazine, February 1991, 19, #2, p. 87.
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All Images, Media and Content - Copyright © 2019 Astronomy Camp
Site Design by Jacob Omann. Maintained by Astronomy Camp. Updated December 31, 2018


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