SOUTHSIDE 1-1000 (director/writer: Boris Ingster; screenwriter: Leo Townsend/from an unpublished story by Milton M. Raison and Bert C. Brown; cinematographer: Russell Harlan; cast: Don DeFore (John Riggs/Nick Starns), Andrea King (Nora Craig), Bret Hamilton (Car Attendant), George Tobias (Reggie), Barry Kelley (Evans), Morris Ankrum (Eugene Deane), Robert Osterloh (Albert), Charles Cane (Harris), Kippee Valez (Singer), William Forrest (Warden), Douglas Spencer (Chaplain), John Harmon (Willy, Pickpocket), 1950)
Reviewed by Dennis Schwartz
This seemingly laconic B&W pseudodocumentary-styled noir film, with a patriotic message supporting the U.S. war effort in Korea and offering a scathing commentary on counterfeiters, comparing them to saboteurs, as the film states the most powerful weapon in America's arsenal against Communism- is money, turns out to be basically a promotional movie for the work that the Treasury Department agents do.
Southside is directed and written by Boris Ingster, who directed the first American noir film, "Stranger on the Third Floor" (40). In this film, he never brings about any tension or real shock to the story, as he did in his first noir work. He simply goes about stating his case against a master counterfeiter, Eugene Deane (Morris Ankrum), who is serving a life sentence in San Quentin, and tells how he was able while in prison to engrave some plates of money currency and smuggle them out of prison by fooling the prison chaplain (Spencer) and hiding it in the liner of his bag.
The movie reminds me of the 1950s TV series, Dragnet. It is played in the same sobering tone, with no room for comedy or any quirkiness to the vanilla story unfolding. In the end, it is a certainty that everything will be wrapped up in a tidy manner.
 So it seems strange that he would be interested in still pursuing his criminal activities, especially since money can't be of use to him anymore. In his cell, he is always seen with his Bible, and he is quite fond of showing off his knowledge of the scriptures, even correcting the padre on one of his sermons about money being the root of evil, as he tells the padre, St. Paul in his epistle said, it is the love of money that is the root of evil, not money itself.
A bunco cop working the beat in an unnamed baseball park, which looked to me like Yankee Stadium (but the story takes place in Los Angeles-which goes to show you how widespread the counterfeit money operation is), spots a pickpocket (Harmon), and when arrested the cops realize that this is "queer money." The secret service is informed about this and they put a tail on the man who was pickpocketed, the traveling salesman, Mr.Evans (Barry Kelley). He leads them to a smoke shop where he exchanges good money for counterfeit ones, and the secret service arrest the smoke shop owners and Evans. Federal agent John Riggs (Don DeFore) now tails Evans when he is out on bond, who is facing a 10-year sentence if convicted. But when a trap is set for Evans to meet his contact man, a wily criminal, Reggie (Tobias), the criminals lose the cop's tail and the cop's pigeon is thrown out of a window, and there goes the best lead that they had.
But Riggs goes undercover, using the name Nick Starns as he checks into a hotel that he knows Evans frequents. He poses as an underworld figure and a bank robber, and catches the attention of the hotel manager, Nora Craig (Andrea King). They get to know each other and soon become romantically and professionally linked together, though she suspects that he might be a cop and has him watched by Reggie.
To make a short story shorter, she's the daughter of Deane and the big boss of the syndicate. When Deane escapes from prison to see her, after he dies, she looks through his belongings and recognizes a sketch her father made of Riggs, depicting him as a T-agent.
 He was outwitted by the ever-careful Reggie, who foiled any attempt at being followed to the gang's building where the deal was to go down, but Riggs was smart enough to have a $10 bill with an emergency message and the number SO 1-1000 of the local secret service agency on it, which was used to pay for the beer he ordered in a grocery store, and he was lucky enough to have the immigrant grocer's wife who looked at the bill, call the agency.
The agents arrive in time to rescue Riggs from being burned to death and arrest all the criminals, but for Nora, who runs into the downtown area and across a trestle and slips to her death onto the railroad tracks below. Her embrace of hatred for him on the trestle is contrasted to their loving embrace in her apartment. This final scene was filmed aboard Los Angeles' "Angel's Flight," a cable-car service dangling 40 feet above the ground.
The film was based on a true story, and seems as if it were a documentary.
REVIEWED ON 12/6/99 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Millions in funding for former armed services projects and awards for schools announced.
Education Secretary Nicky Morgan has announced a package of measures to help schools instil character in pupils - including extra funding for projects run by former armed service personnel which help turn around the lives of disadvantaged children.
Eight projects will be given almost £5 million to work with schools - using the values and expertise of the armed services to help young people do better at school and develop their character, including values such as self-confidence, respect and leadership.
Schools that develop and build character, resilience and grit in their pupils will also be recognised for the first time through the new character awards - so that more schools focus on developing well rounded pupils prepared for life in modern Britain.
Applicants will be judged on their approaches and practices to develop character by a panel of education experts, with awards including:
Education Secretary Nicky Morgan said:
Delivering the best schools and skills is a key part of our long-term economic plan that is turning Britain around.
As well as high academic standards, this means providing opportunities for all young people to develop the character and resilience they need to succeed in modern Britain.
For pupils who may have faced challenges or difficulties in their personal life, these initiatives run by former armed services personnel can offer a sense of greater aspiration and can help build the skills and confidence they need to go on to good jobs and successful futures.
Coupled with the new character awards schools will now have the tools and support they need to ensure they develop well rounded pupils ready to go onto an apprenticeship, university or the world of work.
The new funding for military ethos projects comes as a review into evidence and feedback published alongside the announcement shows how these projects can have a positive impact in improving behaviour, attendance and resilience for the young people taking part allowing them to get the most out of school.
All delivery providers recorded examples of impact in the classroom and beyond. Teachers, parents and pupils described how these projects have had a positive impact on the confidence, self-esteem, self-respect and inter-personal skills of those involved, the type of skills which will help pupils thrive in higher or further education and in the world of work. Teachers also described how pupils who had previously been disengaged were now actively participating in school life.
Pupils themselves reflected on how the schemes had increased their confidence, made them less self-critical and more self-aware.
Last year, more than 52,000 pupils participated in these schemes across 460 primary schools, secondary schools, sixth-form and further education colleges, and other institutions. This included more than 16,000 pupils who were identified as being disengaged with their school life and 1,333 pupils in alternative provision or excluded from schools.
Character can be developed in pupils in a wide variety of ways, through teaching values in personal, social, health and economic ( PSHE ) or citizenship lessons, through the full curriculum, or by competing on the playing field or taking part in extra-curricular activities such as the Duke of Edinburgh Award, the National Citizen Service or after school debating clubs.
Applicants to the character awards, who can enter from January, will be judged on the extent to which they have been successful in promoting character education in children and young people, on the level of innovation demonstrated, on the extent to which success is shared with others to spread practice and on their future plans to spend any prize money attracted.
In September Education Secretary Nicky Morgan announced the £5 million Character Innovation Fund, which made available £5 million over the next 2 years to support the development of character in schools. Today’s (8 December 2014) announcement is the first stage of this, with further announcements to follow.
1) Military ethos providers deliver a range of activities aimed at helping pupils do well in school and beyond, based around an ‘A, B, C, D building blocks of character’ model:
2) A total of £4.8 million has been awarded in 8 grants to use military ethos in schools to improve education attainment of the most disengaged pupils:
Mike Hamilton, Director of Commando Joe’s said:
This extra funding is vital for our mission to bring Commando Joe’s to more and more schools across the UK, helping thousands of children to have greater self-confidence and resilience. We’re focusing on fostering altruism, bounce back, comfort zone busting and determination in school children, all of which ultimately boost attendance and attainment.
Simon Dean, Founder and Chief Executive of Challenger Troop CIC said:
We are delighted to have received this recognition. We see first-hand the often life-changing impact our programmes have on a young person, as they learn how to take responsibility for their actions, communicate better with their peers and engage more effectively with their education. This grant funding will enable us to reach even more young people and improve their learning outcomes and life chances.
KSA Education and Training which provides young people with a programme of physical activities, team-building and work-related learning and prepare them for post-16 education, training or employment has been awarded £412,000
SkillForce which provides a programme of challenges in the outdoor environment, integrated to support literacy and numeracy has been awarded £400,000
CVQO which works with pupils from all backgrounds and abilities, delivering education to help them improve their lives has been awarded £757,000
Guy Horridge, Chief Executive, CVQO said:
We are absolutely delighted to be able to continue having a positive impact on the lives of pre- GCSE learners through our Schools Partnership Project. Education is not a one-size fits all solution, which is precisely why programmes like ours exist.
All young people have the potential to be incredibly prodigious and achieve great things; it is simply a case of unlocking that potential in a way that resonates with them.
We’ve seen such dramatic improvements in students’ behaviour, attendance and self-confidence over the course of the first year, with many now engaging in community and voluntary work in their own time.
CVQO looks forward to another year of progression and making a real difference to students nationwide.
Martina Milburn, Chief Executive of The Prince’s Trust said:
There are many young people who, often through no fault of their own, have struggled with their education and have completely lost their confidence. This can have a knock on effect making them feel hopeless for the future and in need of support to get their lives back on track.
The Prince’s Trust runs programmes which work with young people to help them re-engage with education and get the qualifications they need to move on with their lives.
By working in partnership with the military ethos programme we will be able to build on the work we are already doing in schools and colleges, supporting the young people who need it the most and helping them to create a positive future for themselves.
Outreach, a youth-diversion programme run by the Army Cadet Force has been awarded £270,000. Outreach is designed to help socially and educationally disadvantaged young people who are in danger of being drawn into crime. The programme gives them a short burst of challenging team-based activity to help them to gain confidence, develop self-esteem and realise their potential.
Training 2000, a registered charity which provides work-based learning programmes for young people and adults in construction; engineering; business administration; health and social care and foundation programmes has been awarded £270,000. It is rated ‘good’ by Ofsted (2011) who describe the quality of provision and leadership/management as outstanding.
3) Examples of high-performing schools where character education approaches feature include:
Outwood Academy Portland in Worksop, Nottinghamshire - ranked among the worst in the country just over 2 years ago, since becoming a sponsored academy Outwood Academy Portland has undergone one of the quickest turnarounds ever achieved and was earlier this year rated Outstanding by Ofsted. Principal Dr Phillip Smith says that a renewed focus on attainment in core subjects combined with the introduction of numerous extra-curricular activities has been crucial to the school’s improvement. The school offers more than 100 after-school classes - with everything from extra English and maths, to an orchestra, a choir and a debating club, Minecraft club and circus skills.
School 21, a free school in Stratford, east London - which follows a holistic approach to education, identifying 21 qualities as attributes of a successful student. The school provide leadership and debating classes to enhance pupils’ self-confidence, assertiveness and public speaking skills.
King Solomon Academy in north west London - headteacher Max Haimendorf has introduced much of the thinking of the American charter school movement and specifically that of its Knowledge is Power program ( KIPP ), where schools have longer days and a structured approach to developing and talking about character.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 8 December 2014
From: Department for Education The Rt Hon Nicky Morgan MP
Part of: Young people


--- layout: post status: publish published: true title: 'Real Cool: New Shuffle & iPod Video(80G)' author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 476 wordpress_url: http://leaskh.wordpress.com/2006/09/16/real-cool-new-shuffle-ipod-video80g date: '2006-09-16 14:43:32 +0800' date_gmt: '2006-09-16 14:43:32 +0800' categories: - iPod + iTunes tags: [] comments: [] ---


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The $aria service contains helper methods for applying common ARIA attributes to HTML directives.
ngAria injects common accessibility attributes that tell assistive technologies when HTML elements are enabled, selected, hidden, and more. To see how this is performed with ngAria, let's review a code snippet from ngAria itself:
ngAriaModule.directive('ngDisabled', ['$aria', function($aria) {
return $aria.$$watchExpr('ngDisabled', 'aria-disabled', nativeAriaNodeNames, false);
}])
Shown above, the ngAria module creates a directive with the same signature as the
traditional ng-disabled
directive. But this ngAria version is dedicated to
solely managing accessibility attributes on custom elements. The internal $aria
service is
used to watch the boolean attribute ngDisabled
. If it has not been explicitly set by the
developer, aria-disabled
is injected as an attribute with its value synchronized to the
value in ngDisabled
.
Because ngAria hooks into the ng-disabled
directive, developers do not have to do
anything to enable this feature. The aria-disabled
attribute is automatically managed
simply as a silent side-effect of using ng-disabled
with the ngAria module.
The full list of directives that interface with ngAria:
Read the ngAria Developer Guide for a thorough explanation of each directive.
Requires the ngAria
module to be installed.


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Data preparation
Load datasets
The data can be downloaded from here.
# data of treatment assignment by school
treatment_school <- read_dta("../data/Duflo_Hanna_Ryan/data/Incentives_FINAL/Raw-Data/Tests/treatschool.dta") %>%
mutate(treatment = 1)
# result of random check if a school is open
random_check <- read_dta("../data/Duflo_Hanna_Ryan/dataverse_files/RandomCheck.dta")
# pre-treatment test results
pre_test <- read_dta("../data/Duflo_Hanna_Ryan/dataverse_files/Pretest.dta")
# post-treatment test results
post_test <- read_dta("../data/Duflo_Hanna_Ryan/dataverse_files/Posttest.dta")
Data processing
# Make school-level post treatment average test results
# - indicator for taking a written test
# - score for written test (NA for those who did not take tests)
post_test_school <- post_test %>%
group_by(schid) %>%
summarise_at(vars(post_writ, post_total_w), mean, na.rm = TRUE)
# For later use, get the mean of the average test results across schools
post_total_w_mean <- mean(post_test_school$post_total_w)
# School-level post treatment average test results, where
# scores of students who didn't take tests are replaced to 0
post_test_school_all <- post_test %>%
mutate(post_total_w = replace_na(post_total_w, 0)) %>%
group_by(schid) %>%
summarise(post_total_w_all = mean(post_total_w,na.rm = TRUE))
# Make school-level pre treatment average test results
pre_test_school <- pre_test %>%
group_by(schid) %>%
summarise_at(vars(pre_writ), mean, na.rm = TRUE)
# School-level share of opens
# (In Imbens-Rubin textbook, it appears that all records, including pre-treatment, are counted for this variable.
# For information of post-treatment opens, use filter by month and year)
school_open <- random_check %>%
mutate(open = ifelse(a1_1 == 1, 1, 0)) %>%
# mutate(
# post_med = ifelse(year >= 2005 | (month >= 4 & year >= 2004), 1, 0),
# post_end = ifelse(year >= 2005 | (month >= 11 & year >= 2004), 1, 0),
# ) %>%
# filter(post_med == 1) %>%
group_by(schid) %>%
summarise_at(vars(open), mean, na.rm = TRUE)
df <- post_test_school %>%
# merge treatment status information
left_join(treatment_school, by = c("schid" = "schid")) %>%
mutate(treatment = ifelse(is.na(treatment), 0, 1)) %>%
# merge pre-treatment test results data
left_join(pre_test_school, by = c("schid" = "schid")) %>%
# merge post-treatment test results data with NA replaced to 0
left_join(post_test_school_all, by = c("schid" = "schid")) %>%
# merge school open information
left_join(school_open, by = c("schid" = "schid")) %>%
# It appears that, the post-treatment test scores are divided by the average (ignoring students who didn't take exam)
# Not sure exactly why
mutate(
post_total_w = post_total_w / post_total_w_mean,
post_total_w_all = post_total_w_all / post_total_w_mean,
)
# Summary statistics for control and treatment group variables
control_treat_sum <- df %>%
group_by(treatment) %>%
summarise_at(
vars(pre_writ, open, post_writ, post_total_w, post_total_w_all),
list(mean, sd)
) %>%
pivot_longer(cols = -treatment, names_to = c(".value", "fn"), names_patter = "(.*)(_.*$)") %>%
mutate(
across(-treatment, ~ formatC(., digits = 2, format = "f")),
across(-treatment, ~ ifelse(fn == "_fn2", paste0("(", ., ")"), .)),
) %>%
select(-treatment, -fn) %>%
as.matrix() %>%
t()
# Summary statistics for all schools
all_school_sum <- df %>%
summarise_at(
vars(pre_writ, open, post_writ, post_total_w, post_total_w_all),
list(min, max)
) %>%
pivot_longer(cols = everything(), names_to = c(".value", "fn"), names_patter = "(.*)(_.*$)") %>%
mutate(across(-fn, ~ formatC(., digits = 2, format = "f"))) %>%
select(-fn) %>%
as.matrix() %>%
t()
Summary statistics (Replication of Table 6.1)
cbind(
c("pctprewritten", "open", "pctpostwritten", "written", "written_all"),
control_treat_sum, all_school_sum
) %>%
set_rownames(NULL) %>%
kable("html", booktabs = TRUE) %>%
add_header_above(
c(" ", rep(c("Average", "(S.D.)"), 2), "Min", "Max")
) %>%
add_header_above(
c(
"Variable",
"Control ($N_c$ = 54)" = 2,
"Treatment ($N_c$ = 53)" = 2,
" ", " "
)
) %>%
pack_rows(index = c(
"Pre-treatment" = 1,
"Post-treatment" = 4
)) %>%
kable_styling(position = "center")
|
Variable
|
Control (\(N_c\) = 54)
|
Treatment (\(N_c\) = 53)
|
|
|
|
|
Average
|
(S.D.)
|
Average
|
(S.D.)
|
Min
|
Max
|
|
Pre-treatment
|
|
pctprewritten
|
0.19
|
(0.19)
|
0.16
|
(0.17)
|
0.00
|
0.67
|
|
Post-treatment
|
|
open
|
0.58
|
(0.19)
|
0.80
|
(0.13)
|
0.00
|
1.00
|
|
pctpostwritten
|
0.47
|
(0.19)
|
0.52
|
(0.23)
|
0.05
|
0.92
|
|
written
|
0.92
|
(0.45)
|
1.09
|
(0.42)
|
0.07
|
2.20
|
|
written_all
|
0.45
|
(0.32)
|
0.59
|
(0.39)
|
0.04
|
1.43
|
Estimates of components of variance of estimator for the effect on open
variable (Replication of Table 6.2)
df_control <- df %>% filter(treatment == 0)
df_treatment <- df %>% filter(treatment == 1)
# mean of outcome for control
Yobs_c <- mean(df_control$open)
# mean of outcome for treatment
Yobs_t <- mean(df_treatment$open)
# estimate for average treatment effect
tau_hat <- Yobs_t - Yobs_c
# estimated variance for control (obtain as SD)
s_c <- sqrt(sum((df_control$open - Yobs_c)^2) / (nrow(df_control) - 1))
# estimated variance for treatment (obtain as SD)
s_t <- sqrt(sum((df_treatment$open - Yobs_t)^2) / (nrow(df_treatment) - 1))
# estimated variance for all (obtain as SD)
s <- sqrt(
(s_c^2 * (nrow(df_control) - 1) + s_t^2 * (nrow(df_treatment) - 1)) /
(nrow(df) - 2)
)
# Sampling variance estimates, ignoring heterogeneous treatment effects (obtain as SD)
sd_neyman <- sqrt(s_c^2 / nrow(df_control) + s_t^2 / nrow(df_control))
# Sampling variance estimates under constant treatment effects (obtain as SD)
sd_const <- sqrt(s^2 * (1 / nrow(df_control) + 1 / nrow(df_treatment)))
# Sampling variance estimates when potential outcomes are perfectly correlated (obtain as SD)
sd_rho1 <- sqrt(
s_c^2 * (nrow(df_treatment) / (nrow(df) * nrow(df_control))) +
s_t^2 * (nrow(df_control) / (nrow(df) * nrow(df_treatment))) +
s_c * s_t * 2 / nrow(df)
)
bind_cols(
name = c(
"$\\bar{Y}_c^{obs}$", "$\\bar{Y}_t^{obs}$", "$\\hat{\\tau}$",
"$s_c^2$", "$s_t^2$", "$s^2$",
"$\\hat{V}^{\\text{neyman}} = s^2 / N_c + s^2 / N_t$",
"$\\hat{V}^{\\text{const}} = s^2 \\cdot \\left( 1 / N_c + 1 / N_t \\right)$",
"$\\hat{V}^{\\rho_{tc} = 1} = s_c^2 \\cdot \\left( N_t / (N \\cdot N_c ) \\right) + s_t^2 \\cdot \\left( N_c / (N \\cdot N_t) \\right) + s_c \\cdot s_t \\cdot \\left( 2 / N \\right)$"
),
value = c(
Yobs_c, Yobs_t, tau_hat,
s_c, s_t, s,
sd_neyman, sd_const, sd_rho1
)
) %>%
mutate(value = formatC(value, digits = 2, format = "f")) %>%
mutate(
value = ifelse(row_number() >= 4, paste0("$", value, "^2$"), paste0("$", value, "$"))
) %>%
set_colnames(NULL) %>%
kable("html", booktabs = TRUE) %>%
pack_rows(index = c(
"Estimated means" = 3,
"Estimated variance compnents" = 3,
"Sampling variance estimates" = 3
)) %>%
kable_styling(position = "center")
|
Estimated means
|
|
\(\bar{Y}_c^{obs}\)
|
\(0.58\)
|
|
\(\bar{Y}_t^{obs}\)
|
\(0.80\)
|
|
\(\hat{\tau}\)
|
\(0.22\)
|
|
Estimated variance compnents
|
|
\(s_c^2\)
|
\(0.19^2\)
|
|
\(s_t^2\)
|
\(0.13^2\)
|
|
\(s^2\)
|
\(0.16^2\)
|
|
Sampling variance estimates
|
|
\(\hat{V}^{\text{neyman}} = s^2 / N_c + s^2 / N_t\)
|
\(0.03^2\)
|
|
\(\hat{V}^{\text{const}} = s^2 \cdot \left( 1 / N_c + 1 / N_t \right)\)
|
\(0.03^2\)
|
|
\(\hat{V}^{\rho_{tc} = 1} = s_c^2 \cdot \left( N_t / (N \cdot N_c ) \right) + s_t^2 \cdot \left( N_c / (N \cdot N_t) \right) + s_c \cdot s_t \cdot \left( 2 / N \right)\)
|
\(0.03^2\)
|
Estimates of, and confidence intervals for average treatment effects (Replication of Table 6.3)
make_table_6_3 <- function(df, outcome) {
df_control <- df %>% filter(treatment == 0)
df_treatment <- df %>% filter(treatment == 1)
# mean of outcome for control
Yobs_c <- mean(df_control %>% pull(outcome))
# mean of outcome for treatment
Yobs_t <- mean(df_treatment %>% pull(outcome))
# estimate for average treatment effect
tau_hat <- Yobs_t - Yobs_c
# estimated variance for control (obtain as SD)
s_c <- sqrt(sum((df_control %>% pull(outcome) - Yobs_c)^2) / (nrow(df_control) - 1))
# estimated variance for treatment (obtain as SD)
s_t <- sqrt(sum((df_treatment %>% pull(outcome) - Yobs_t)^2) / (nrow(df_treatment) - 1))
# Sampling variance estimates, ignoring heterogeneous treatment effects (obtain as SD)
sd_neyman <- sqrt(s_c^2 / nrow(df_control) + s_t^2 / nrow(df_control))
ci <- paste0(
"(",
(tau_hat - 1.96 * sd_neyman) %>% formatC(digits = 2, format = "f"), ", ",
(tau_hat + 1.96 * sd_neyman) %>% formatC(digits = 2, format = "f"),
")"
)
return(c(
tau_hat %>% formatC(digits = 2, format = "f"),
paste0("(", sd_neyman %>% formatC(digits = 2, format = "f"), ")"),
ci
))
}
cbind(
c("open", "pctpostwritten", "written", "written_all"),
map(
c("open", "post_writ", "post_total_w", "post_total_w_all"),
~ make_table_6_3(df, .)
) %>%
bind_cols() %>%
t()
) %>%
set_rownames(NULL) %>%
kable("html", booktabs = TRUE, align = c("l", "c", "c", "c")) %>%
add_header_above(
c("Variable", "$\\widehat{ATE}$", "$(\\widehat{\\text{s.e.}})$", "95\\% C.I.")
) %>%
kable_styling(position = "center")
## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
|
Variable
|
\(\widehat{ATE}\)
|
\((\widehat{\text{s.e.}})\)
|
95% C.I.
|
|
open
|
0.22
|
(0.03)
|
(0.15, 0.28)
|
|
pctpostwritten
|
0.05
|
(0.04)
|
(-0.03, 0.13)
|
|
written
|
0.17
|
(0.08)
|
(0.00, 0.33)
|
|
written_all
|
0.14
|
(0.07)
|
(0.00, 0.27)
|
estimates of, and confidence intervals for average treatment effects by covariate (Replication of Table 6.4)
make_table_6_4 <- function(df, outcome, covar0) {
if (covar0 == TRUE) {
df_control <- df %>% filter(treatment == 0) %>% filter(pre_writ == 0)
df_treatment <- df %>% filter(treatment == 1) %>% filter(pre_writ == 0)
} else {
df_control <- df %>% filter(treatment == 0) %>% filter(pre_writ > 0)
df_treatment <- df %>% filter(treatment == 1) %>% filter(pre_writ > 0)
}
# Average treatment effects for samples with pre-writ == 0
# mean of outcome for control
Yobs_c <- mean(df_control %>% pull(outcome))
# mean of outcome for treatment
Yobs_t <- mean(df_treatment %>% pull(outcome))
# estimate for average treatment effect
tau_hat <- Yobs_t - Yobs_c
# estimated variance for control (obtain as SD)
s_c <- sqrt(sum((df_control %>% pull(outcome) - Yobs_c)^2) / (nrow(df_control) - 1))
# estimated variance for treatment (obtain as SD)
s_t <- sqrt(sum((df_treatment %>% pull(outcome) - Yobs_t)^2) / (nrow(df_treatment) - 1))
# Sampling variance estimates, ignoring heterogeneous treatment effects (obtain as SD)
sd_neyman <- sqrt(s_c^2 / nrow(df_control) + s_t^2 / nrow(df_control))
ci <- paste0(
"(",
(tau_hat - 1.96 * sd_neyman) %>% formatC(digits = 2, format = "f"), ", ",
(tau_hat + 1.96 * sd_neyman) %>% formatC(digits = 2, format = "f"),
")"
)
return(c(
tau_hat %>% formatC(digits = 2, format = "f"),
paste0("(", sd_neyman %>% formatC(digits = 2, format = "f"), ")"),
ci
))
}
make_table_6_4_diff <- function(df, outcome) {
reg_res <- lm(as.formula(paste(outcome, "treatment*(pre_writ == 0)", sep = " ~ ")), df) %>%
coeftest(., vcov = vcovHC(., type = "HC1"))
est <- reg_res["treatment:pre_writ == 0TRUE", "Estimate"]
se <- reg_res["treatment:pre_writ == 0TRUE", "Std. Error"]
ci <- paste0(
"(",
(est - 1.96 * se) %>% formatC(digits = 2, format = "f"), ", ",
(est + 1.96 * se) %>% formatC(digits = 2, format = "f"),
")"
)
return(c(
est %>% formatC(digits = 2, format = "f"),
paste0("(", se %>% formatC(digits = 2, format = "f"), ")"),
ci
))
}
cbind(
c("open", "pctpostwritten", "written", "written_all"),
map(
c("open", "post_writ", "post_total_w", "post_total_w_all"),
~ make_table_6_4(df, ., covar0 = TRUE)
) %>%
bind_cols() %>%
t(),
map(
c("open", "post_writ", "post_total_w", "post_total_w_all"),
~ make_table_6_4(df, ., covar0 = FALSE)
) %>%
bind_cols() %>%
t(),
map(
c("open", "post_writ", "post_total_w", "post_total_w_all"),
~ make_table_6_4_diff(df, .)
) %>%
bind_cols() %>%
t()
) %>%
set_rownames(NULL) %>%
kable("html", booktabs = TRUE, align = c("l", rep("c", 9))) %>%
add_header_above(
c(" ", rep(c("$\\tau$", "($\\widehat{\\text{s.e.}}$)", "95\\% C.I."), 2), "EST", "($\\widehat{\\text{s.e.}}$)", "95\\% C.I.")
) %>%
add_header_above(
c("", "($N$ = 40)" = 3, "($N$ = 67)" = 3, " " = 3)
) %>%
add_header_above(
c("Variable", "pctprewritten = 0" = 3, "pctprewritten $\\gt$ 0" = 3, "Difference" = 3)
) %>%
kable_styling(position = "center")
## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
|
Variable
|
pctprewritten = 0
|
pctprewritten \(\gt\) 0
|
Difference
|
|
|
(\(N\) = 40)
|
(\(N\) = 67)
|
|
|
|
\(\tau\)
|
(\(\widehat{\text{s.e.}}\))
|
95% C.I.
|
\(\tau\)
|
(\(\widehat{\text{s.e.}}\))
|
95% C.I.
|
EST
|
(\(\widehat{\text{s.e.}}\))
|
95% C.I.
|
|
open
|
0.23
|
(0.05)
|
(0.14, 0.32)
|
0.21
|
(0.04)
|
(0.13, 0.29)
|
0.02
|
(0.06)
|
(-0.10, 0.14)
|
|
pctpostwritten
|
-0.04
|
(0.06)
|
(-0.16, 0.08)
|
0.11
|
(0.05)
|
(0.01, 0.21)
|
-0.15
|
(0.08)
|
(-0.31, 0.00)
|
|
written
|
0.20
|
(0.10)
|
(-0.00, 0.41)
|
0.18
|
(0.10)
|
(-0.02, 0.38)
|
0.03
|
(0.15)
|
(-0.26, 0.31)
|
|
written_all
|
0.04
|
(0.08)
|
(-0.10, 0.19)
|
0.22
|
(0.09)
|
(0.05, 0.39)
|
-0.18
|
(0.12)
|
(-0.40, 0.05)
|


This section lists and describes the Java Platform Standard Edition, Java SE) commands.
The Java SE commands provide a way for developers to handle development tasks such as compiling and running a program, packaging source files in to a JAR file, applying security policies to a JAR file, and more.
This command reference describes the full set of JDK commands. The JDK commands that perform related functions are grouped into parts, as follows.
appletviewer
: Runs applets outside of a web browser.
extcheck
: Detects version conflicts between a target Java Archive (JAR) file and currently installed extension JAR files.
jar
: Combines multiple files into a single JAR file.
java
: Starts a Java application.
javac
: Reads Java class and interface definitions and compiles them into bytecode and class files.
javadoc
: Generates HTML pages of API documentation from Java source files.
javah
: Generates C header and source files from a Java class.
javap
: Disassembles one or more class files.
jdb
: Finds and fixes bugs in Java platform programs.
These commands set security policies on your local system and create applications that can work within the scope of the security policies set at remote sites.
keytool
: Manages a keystore (database) of cryptographic keys, X.509 certificate chains, and trusted certificates.
jarsigner
: Signs and verifies JAR files.
policytool
: Reads and writes a plain text policy file based on user input through the utility GUI.
native2ascii
: Creates localizable applications by converting a file with characters in any supported character encoding to one with ASCII and/or Unicode escapes (\uxxxx) notation for all characters that are not part of the ASCII character set, or vice versa.
These commands create applications that interact over the web or with another network.
rmic
: Generates stub, skeleton, and tie classes for remote objects that use the Java Remote Method Protocol (JRMP) or Internet Inter-Orb protocol (IIOP). Also generates Object Management Group (OMG) Interface Definition Language (IDL).
rmiregistry
: Starts a remote object registry on the specified port on the current host.
rmid
: Starts the activation system daemon that enables objects to be registered and activated in a Java Virtual Machine (JVM).
serialver
: Returns the serial version UID for specified classes.
These commands create applications that use OMG-standard IDL and CORBA/IIOP.
tnameserv: Starts the Java Interface Definition Language (IDL) name server.
idlj
: Generates Java bindings for a given Interface Definition Language (IDL) file.
orbd
: Enables clients to locate and call persistent objects on servers in the CORBA environment.
servertool
: Provides an easy-to-use interface for the application programmers to register, unregister, start up, and shut down a server.
pack200
: Packages a JAR file into a compressed pack200 file for web deployment.
unpack200
: Transforms a packed file produced by pack200
(1) into a JAR file for web deployment.
jconsole
: Starts a graphical console that lets you monitor and manage Java applications.
jvisualvm
: Visually monitors, troubleshoots, and profiles Java applications.
Most of these these commands are unsupported and experimental and might not be available in future JDK release.
jps
: Experimental. Lists the instrumented Java Virtual Machines (JVMs) on the target system.
jstat
: Experimental. Monitors JVM statistics
jstatd
: Experimental. Monitors JVMs and enables remote monitoring tools to attach to JVMs.
jmc
: Starts Java Mission Controla tool for monitoring and managing running Java applications and JVMs.
schemagen
: Generates a schema for every name space that is referenced in your Java classes.
wsgen
: Reads a web service endpoint implementation (SEI) class and generates all of the required artifacts for web service deployment, and invocation.
wsimport
: Generates JAX-WS portable artifacts that can be packaged in a web application archive (WAR) file and provides an Ant task.
xjc
: Compiles an XML schema file into fully annotated Java classes.
Most of these commands are unsupported and experimental and might not be available in future JDK releases.
jcmd
: Sends diagnostic command requests to a running JVM.
jinfo
: Experimental. Generates configuration information.
jhat
: Experimental. Analyzes the Java heap.
jmap
: Experimental. Prints shared object memory maps or heap memory details for a process, core file, or remote debug server.
jsadebugd
: Experimental. Attaches to a Java process or core file and acts as a debug server.
jstack
: Experimental. Prints Java thread stack traces for a Java process, core file, or remote debug server.
This command is unsupported and experimental and might not be available in future JDK releases.
jrunscript
: Experimental. Runs a command-line script shell that supports interactive and batch modes.


This is a demo of the jlottie player. This player has a smaller bundled file size compared to
other Lottie Players and has been written for better performance whilst using a smaller memory
footprint. Paste a valid Lottie JSON Url in the input to preview and test an animation on the
jlottie Player.


There are some projects I have created during programming course. These projects are:
This is my project I created during SPOC program, it talks about different traditional games for children in Africa and instructions of how you can play those games. To create this project I used HTML and Css languages and I used also Markdown language for README.md file. You can find this project via this link My-project
I created this project during prep course in Moringa school when I learnt git program. I used HTML language. To find this project use this link hello-world
I created this project when I learnt to create a web page using terminal and vision studio. I used HTML and css languages. To see web page use this link my-first-website
This project was created when I learnt indentation and Spacing, I used HTML language. You can find this project via this link cookie-recipe
My name is Kayitesi Jackie, I live in Kigali city, Gasabo District, Kimironko sector. I have a bachelor degree in computer science with education and a post graduate diploma in IT.
I have an experience in accounting, data entry and data collection. But I want to be a best programmer in future that's why I decided to take a class on programming in Moringa School.
You can contact me via my email<EMAIL_ADDRESS>or my phone number:<PHONE_NUMBER>
When I am free I like to do diffent activities like:
I have skills in computer skills and web design.
| welcome to my comment form | |
|---|---|
| your message | |


The Tesla Cybertruck isn’t for anybody who doesn’t like bold design.
Some critics have already characterized it as freakish. Other adore Tesla, CEO Elon Musk, and design head Franz Von Holzhausen’s daring.
If you look closely at the Cybertruck, you can see numerous car-design references, ranging from fighter planes to movie machines.
Here’s a rundown:
The DMC DeLorean. The infamous brainchild of John DeLorean, the stainless-steel-skinned car that bore his name hit the road for a brief time before a drug bust and financial shenanigans killed the brand. It was renewed by the “Back to the Future” movies.
Lockheed’s F-117 Nighthawk Stealth Fighter is a clustering of radar-defeating angles and edges that clearly caught Tesla designer Franz Von Holzhausen’s attention.
A Spinner from the movie “Blade Runner” — these flying car’s from director Ridley Scott’s 1982 sci-fi epic were cited by Musk as inspiration for the Cybertruck.
So was the Lotus Esprit S1 from the 1977 James Bond flick “The Spy Who Loved Me.” That film car transformed into a submarine — and Musk actually owns the original.
The Triumph TR7 was built from 1974-1981 and was immortalized for its wedge profile by the the “shape of things to come” advertising tagline
If Elon Musk has an equal for automotive showmanship, it’s Land Rover design head Gerry McGovern. He welcomed the scorn for the brand’s Evoque SUV, which has gone on to be a top-seller, despite its initially polarizing styling.
In the realm of actual pickup trucks, the Cybertruck’s high sides for its bed are reminiscent of the first-generation Honda Ridgeline. These are called “sail pillars.”
The now-discontinued Chevy Avalanche also made use of this design feature, as did …
… The mechanically similar Cadillac Escalade EXT.
Sorry, but I see some Pontiac Aztek in the Cybertruck. The reviled proto-crossover perhaps deserves more respect than it usually gets.
The war horse Hummer H1 isn’t made up of sharp triangles, but it shows some intimidating edges and those wheel arches are very Cybertruck-y.
The Batmobile “Tumbler” and its successors in the films that followed offered a militaristic take on the Dark Knight’s ride — and further Cybertruck inspiration from Hollywood.
Harley Earl’s Dream Cars remind us that it’s OK to show extremely out-there designs.
And of course the Cybertruck’s headlight is pure … Cylon.


This course is designed to provide you with a comprehensive understanding of coding and development skills. The curriculum is based on the online Khan Academy platform, which consists of instructional videos and challenges. The course is divided into sprints, each focusing on specific topics. Students will progress through the assigned sprints at Khan Academy to build their knowledge and skills.
August 2-October 4
The Introduction to Web Development 1 class (a.k.a. Intro to Coding Class or Intro Class) is an intensive 10 week class that will introduce you to a broad range of technical skills and tools. You will use code to build and deploy your own website to Github.
Graduates who complete all requirements of the course will graduate with:
This program is competitive and selective. Only the most qualified candidates will be accepted. We will be evaluating you based on your skill level in the following areas:
Before applying to the class, applicants are required to enroll in the web development 1 assessment and finish all the required tasks. Once the tasks are finished, the applicants must attend the Wednesday meetup to get their work approved by a proctor. We highly encourage the applicants to get their work reviewed 2 weeks before the class.
The assessment is due by July 31.
To apply for the upcoming cohort, follow these steps:
No.
The skills you learn in this class will help you in life and with any job you get in the future, but you will not be qualified to work as a Software Engineer yet. This is only a broad introduction. You will still need much more training and experience before you are able to get a full time job. It is a journey that can take years.
This course will help you decide if you want to begin that journey.
After graduating, if you decide you want to work towards becoming a Software Engineer, Refcode can help you move forward.
(3 months)
Graduates are eligible to apply for the Intro to Web Devolopment 2 , where you will learn additional coding skills, mindset and concept.
(6 months - 1 year)
After the bootcamp, participants can apply for a job as a Software Technician at the Refcode Agency. In this program, you will be paid to build sophisticated applications and to grow and develop your skills. Participants who do well will be promoted quickly to Technician II, and Technician III. Software Technicians are paid $11 - 13 per hour depending on level.
(6 months - 1 year)
Those who have been promoted to Software Technician III will be eligible to apply for a Software Engineer I role at the Refcode Software Agency . Engineers take on greater ownership of more complex tasks and will need to show higher creativity and independent problem solving skills. Engineers who do well will be quickly promoted to Software Engineer II and Software Engineer III. Software Engineers are paid $14 - 18 per hour, depending on level and experience.
Engineers who have reached the Engineer III level will be encouraged to look for roles outside of Refcode. Engineers should be eligible to get a job at a company with salaries in the range of $45,000/year to $80,000/year, depending on experience, motivation, and skill.
Sign up for the Summer 2023 cohort now!


| Member | Name | Public Landing Page | Staff Notifications | License | Version | Last Uploaded | |
|---|---|---|---|---|---|---|---|
| {{member.key}} | {{member.name}} | {{landingPage(member)}} | {{member.staffNotificationEmail}} | {{member.licenseName}} | {{member.licenseVersion}} | {{member.license.lastUpdated | date: 'yyyy-MM-dd'}} |
|


Experience
WebLister
June 2013 – August 2013
Designed and wrote Python applications to automate online marketing for real estate agents. Users input data through a Django web application that doubled as a REST API. A remotely controlled (using Fabric) automated Selenium script used the API to create advertisements in online marketplaces. Wrote reporting toolset that allowed users to get realtime reports using AJAX. Application also tracked leads generated by advertisements with a Twilio integrated telephony system.
Name protected under NDA
February 2013 – June 2013
Managed, developed, and maintained the technology stack
of a startup in the automotive insdustry. The system focused on
inventory control and management for automotive dealerships. PHP, Python,
Mechanize, and MySQL.
Name protected under NDA
December 2012 – February 2013
Developed an API based advertising network for the
mobile video game industry. The API communicated with five different providers and decided via an algorithm what advertisement would be showed to the end user. Wrote a sample video game in Android to showcase the capabilities of the
advertising network. Developed the technical documentation for integration with the system
. Python, Gevent, Memcache, MySQL, Java, and
Android.
Docutek
October 2012 – November 2012
Modified and integrated the OpemEMR system. Modifications to the application were
to integrate Twilio based telephony system. Which allowed the medical practice
to automate appointment confirmations and follow ups. Deployed it to twenty seven
medical practices. Trained medical practice personel to use system.
Name protected under NDA
August 2012 – September 2012
Developed an online ordering and payment platform for
restaurants with full facebook API integration. Payments were managed by
integration with Stripe. Used PHP, Codeigniter, Jquery, Javascript and MySQL.
Name protected under NDA
August 2012
Customized a Joomla installation into an E-commerce
platform aimed at the fashion industry. Included developing custom Joomla
modules, integration with the Joomla-Boostrap plug-in and custom Javascript code
to allow users to turn on/off cookies (EU compliance).
Experience
Programming Languages
Python, Ruby, PHP, Go, Javascript, C, C++, C#, Visual Basic, Java, HTML, and CSS.
Frameworks
Django, Flask, Ruby on Rails, Sinatra, Jquery, Gorilla Toolkit, .NET, Codeigniter, Bootstrap, and Android.
Servers
Apache, Nginx, and Gunicorn.
Databases
MySQL, PostgreSQL, SQLite3, MSSQL, Oracle 10g, Redis, Memcache, and MongoDB.
Development Methodologies
Test Driven Development, and Waterfall.
Operating Systems
Ubuntu, CentOS, Debian, ArchLinux, Windows XP, Windows 7, Windows Server, and OSX.
Source Control
Git and Mercurial.
Design Skills
Design Software
Inkscape, Gimp, Photoshop.
Structure
Grid and table based.
Typography
Web based fonts.
Testing
A/B tests, and QA automation tests through Selenium.
Colors
Web swatches, rgba, transparencies.
Client Side
Business Intelligence dashboard design and implementation with various Javascript libraries.
Business Skills
Marketing
Content-based, copywriting, blogging, email, newsletters (Mailchimp), A/B testting, and SEO.
Advertising
Campaign design, deployment and measurement. Google Analytics, Facebook ads, and Reddit ads.
Sales
Online and offline sales experience. Focus on inbound closing.
Negotiations
Project, service and product based.
Open Source Contributions and Projects
Contributions
Tweepy. Redesigned project github page using Bootstrap, and Jekyll.
Link to project.
Projects
Itsuki. A web application that allows users to automate appointment reminders and follow ups. Integration with Twilio.
Voice and SMS capabilities. Link to project.
Hobbies
Robotics
Arduino, Raspberry Pi, various types of sensors, design of mechanical components, circuit design and construction, soldering, and AI.
Currently developing a Sumo-type robot to compete in local event. Using custom parts.
Running
5K, 10K, and half marathons. Currently training for 10K marathon to take place during October 2013.


ncdu- Colorize output:
ncdu --color dark|off- Analyze a given directory:
ncdu path/to/directory- Save results to a file:
ncdu -o path/to/file- Exclude files that match a pattern, argument can be given multiple times to add more patterns:
ncdu --exclude '*.txt'


Use [ ] to compare values, or remove spaces around = to assign (or quote '=' if literal).
# Assignment
var = value
# Comparison
if $var = value
then
echo "Match"
fi
# Assignment
var=value
# Comparison
if [ "$var" = value ]
then
echo "Match"
fi
ShellCheck found an unquoted =
after a word.
If this was supposed to be a comparison, use square brackets: [ "$var" = value ]
If this was supposed to be an assignment, remove spaces around =
: var=value
If the =
was meant literally, quote it:
grep '=true' file.cfg


Computing the convex hull of a set of points is a fundamental problem in the field of computational geometry. For those unaware of what a convex hull is: in 2D, imagine snapping an elastic band around a set of points - the convex hull is the set of points that form the perimeter created by the elastic band. For more information, check this Wikipedia article out.
The purpose of this application is to provide a visualization of the execution of a few popular convex hull algorithms. Click here for the code.
Namely:
| Enter number of vertices: |
| Algorithm: |
|
Execution Speed:
|


{%- extends "bootstrap/base.html" %}
{% block title %}Guard your site away from broken links{% endblock %}
{# While we are at it, we also enable fixes for legacy browsers. First we
import the necessary macros: #}
{% import "bootstrap/fixes.html" as fixes %}
{# Then, inside the head block, we apply these. To not replace the header,
``super()`` is used: #}
{% block head %}
{{super()}}
{{fixes.ie8()}}
{%- endblock %}
{# Adding our own CSS files is also done here. Check the documentation at
http://pythonhosted.org/Flask-Bootstrap/basic-usage.html#available-blocks
for an overview. #}
{% block styles %}
{{super()}}
{% endblock %}
{# Finally, round things out with navigation #}
{% block navbar %}
{% endblock %}
{% block scripts%}
{{super()}}
{% endblock %}


PTI lawmaker indicted in smuggling, tax evasion case
by https://www.dawn.com/authors/7672/naeem-sahoutara, Naeem SahoutaraA Customs court in Karachi has indicted Pakistan Tehreek-i-Insaf (PTI) lawmaker Muhammad Aslam Khan in a case pertaining to alleged smuggling and tax evasion, it emerged on Thursday.
Customs department had booked Khan alias Aslam Singapori, who was elected to the National Assembly from the NA-254 (Karachi Central-II) constituency on a PTI ticket in the 2018 elections, in 1997 for alleged misdeclaration and evasion of duty and taxes.
Judge Sadaf Asif of the special court (customs and taxation) read out the charges against Khan, who has been accused of committing offences under Section 156(1)(8)(89) and (14) of the Customs Act, 1969. However, he pleaded not guilty and opted to contest the case.
The judge has, therefore, summoned the witnesses to record their testimonies on the next date of the hearing.
Customs had booked seven men, Mohammad Azam and Hameed-ud-Din along with their five absconding accomplices — Aslam Khan, Mohammad Saleem Khan, Sajjad, Asif, and Akhlaq Ahmed — in 1997.
According to the prosecution, the accused had smuggled electronics goods worth Rs28.57 million in association with Pakistan Air Force personnel into Pakistan under the garb of defence stores of PAF from 1995 till 1997. They allegedly caused losses to the tune of Rs28.57m to the national exchequer.


SelectButton is a form component to choose a value from a list of options using button elements.
SelectButton requires a value to bind and a collection of options.
The option itself is bound to the model by default, use optionValue to customize the property to pass as the value.
SelectButtons allows selection of either single or multiple items, this behavior is defined with the multiple property. In single case, model should be a single object reference whereas in multiple case should be an array.
Particular options can be prevented from selection using the optionDisabled property.
SelectButton can be used in a model driven form as well.
For custom content support define a ng-template where the local ng-template variable refers to an option in the options collection.
| Name | Type | Default | Description |
|---|---|---|---|
| options | array | null | An array of selectitems to display as the available options. |
| optionLabel | string | label | Name of the label field of an option. |
| optionValue | string | value | Name of the value field of an option. |
| optionDisabled | string | disabled | Name of the disabled field of an option. |
| multiple | boolean | false | When specified, allows selecting multiple values. |
| tabindex | number | 0 | Index of the element in tabbing order. |
| style | string | null | Inline style of the component. |
| styleClass | string | null | Style class of the component. |
| ariaLabelledBy | string | null | Establishes relationships between the component and label(s) where its value should be one or more element IDs. |
| disabled | boolean | false | When present, it specifies that the element should be disabled. |
| dataKey | string | null | A property to uniquely identify a value in options. |
| Name | Parameters | Description |
|---|---|---|
| onChange | event.originalEvent: browser event event.value: single value or an array of values that are selected |
Callback to invoke when value changes. |
| onOptionClick | event.originalEvent: browser event event.option: SelectItem instance of the clicked button event.index: Index of the clicked button |
Callback to invoke when a button is clicked. |
None.


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EBD - Easton Bible DIctionary
Rabboni
RABBONI
"RABBONI "(id.) occurs only twice in the New Testament (
Mark 10:51
, A.V., “Lord,” R.V., “Rabboni;”
John 20:16
). It was the most honourable of all the titles.
"RABBONI "(id.) occurs only twice in the New Testament (Mark 10:51, A.V., “Lord,” R.V., “Rabboni;” John 20:16). It was the most honourable of all the titles.