#+TITLE: Rapport package team
#+AUTHOR: t-test Template
#+DATE: 2011-04-26 20:25 CET
** Description
A t-test report with table of descriptives, diagnostic tests and t-test
specific statistics.
*** Introduction
In a nutshell, /t-test/ is a statistical test that assesses hypothesis
of equality of two means. But in theory, any hypothesis test that yields
statistic which follows
[[https://en.wikipedia.org/wiki/Student%27s_t-distribution][/t-distribution/]]
can be considered a /t-test/. The most common usage of /t-test/ is to:
- compare the mean of a variable with given test mean value -
*one-sample /t-test/*
- compare means of two variables from independent samples -
*independent samples /t-test/*
- compare means of two variables from dependent samples -
*paired-samples /t-test/*
*** Overview
Independent samples /t-test/ is carried out with /Internet usage in
leisure time (hours per day)/ as dependent variable, and /Gender/ as
independent variable. Confidence interval is set to 95%. Equality of
variances wasn't assumed.
*** Descriptives
In order to get more insight on the underlying data, a table of basic
descriptive statistics is displayed below.
| Gender | min | max | mean | sd | var | median | IQR |
|----------+-------+-------+---------+---------+---------+----------+-------|
| male | 0 | 12 | 3.27 | 1.953 | 3.816 | 3 | 3 |
| female | 0 | 12 | 3.064 | 2.355 | 5.544 | 2 | 3 |
#+CAPTION: Table continues below
| skewness | kurtosis |
|------------+------------|
| 0.9443 | 0.9858 |
| 1.398 | 1.87 |
*** Diagnostics
Since /t-test/ is a parametric technique, it sets some basic assumptions
on distribution shape: it has to be /normal/ (or approximately normal).
A few normality test are to be applied, in order to screen possible
departures from normality.
**** Normality Tests
We will use /Shapiro-Wilk/, /Lilliefors/ and /Anderson-Darling/ tests to
screen departures from normality in the response variable (/Internet
usage in leisure time (hours per day)/).
| N | p | |
|--------------------------------------------------+----------+-------------|
| Shapiro-Wilk normality test | 0.9001 | 1.618e-20 |
| Lilliefors (Kolmogorov-Smirnov) normality test | 0.168 | 3e-52 |
| Anderson-Darling normality test | 18.75 | 7.261e-44 |
As you can see, applied tests yield different results on hypotheses of
normality, so you may want to stick with one you find most appropriate
or you trust the most..
*** Results
Welch Two Sample t-test was applied, and significant differences were
found.
| | statistic | df | p | CI(lower) | CI(upper) |
|-------+-------------+---------+----------+-------------+-------------|
| *t* | 1.148 | 457.9 | 0.2514 | -0.1463 | 0.5576 |
** Description
A t-test report with table of descriptives, diagnostic tests and t-test
specific statistics.
*** Introduction
In a nutshell, /t-test/ is a statistical test that assesses hypothesis
of equality of two means. But in theory, any hypothesis test that yields
statistic which follows
[[https://en.wikipedia.org/wiki/Student%27s_t-distribution][/t-distribution/]]
can be considered a /t-test/. The most common usage of /t-test/ is to:
- compare the mean of a variable with given test mean value -
*one-sample /t-test/*
- compare means of two variables from independent samples -
*independent samples /t-test/*
- compare means of two variables from dependent samples -
*paired-samples /t-test/*
*** Overview
One-sample /t-test/ is carried out with /Internet usage in leisure time
(hours per day)/ as dependent variable. Confidence interval is set to
95%. Equality of variances wasn't assumed.
*** Descriptives
In order to get more insight on the underlying data, a table of basic
descriptive statistics is displayed below.
| Variable | min | max | mean | sd | var |
|--------------------------------------------------+-------+-------+---------+---------+---------|
| Internet usage in leisure time (hours per day) | 0 | 12 | 3.199 | 2.144 | 4.595 |
#+CAPTION: Table continues below
| median | IQR | skewness | kurtosis |
|----------+-------+------------+------------|
| 3 | 2 | 1.185 | 1.533 |
*** Diagnostics
Since /t-test/ is a parametric technique, it sets some basic assumptions
on distribution shape: it has to be /normal/ (or approximately normal).
A few normality test are to be applied, in order to screen possible
departures from normality.
**** Normality Tests
We will use /Shapiro-Wilk/, /Lilliefors/ and /Anderson-Darling/ tests to
screen departures from normality in the response variable (/Internet
usage in leisure time (hours per day)/).
| N | p | |
|--------------------------------------------------+----------+-------------|
| Shapiro-Wilk normality test | 0.9001 | 1.618e-20 |
| Lilliefors (Kolmogorov-Smirnov) normality test | 0.168 | 3e-52 |
| Anderson-Darling normality test | 18.75 | 7.261e-44 |
As you can see, applied tests yield different results on hypotheses of
normality, so you may want to stick with one you find most appropriate
or you trust the most..
*** Results
One Sample t-test was applied, and significant differences were found.
| | statistic | df | p | CI(lower) | CI(upper) |
|-------+-------------+-------+----------+-------------+-------------|
| *t* | -0.007198 | 671 | 0.9943 | 3.037 | 3.362 |
--------------
This report was generated with [[http://www.r-project.org/][R]] (3.0.1)
and [[https://rapporter.github.io/rapport/][rapport]] (0.51) in /0.88/ sec on
x86\_64-unknown-linux-gnu platform.
[[images/logo.png]]