% Rapport package team
% t-test Template
% 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 [_t-distribution_](https://en.wikipedia.org/wiki/Student%27s_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
------------------------------------------------------
Table: 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 0.168 3e-52
(Kolmogorov-Smirnov)
normality test
Anderson-Darling normality 18.75 7.261e-44
test
--------------------------------------------
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 [_t-distribution_](https://en.wikipedia.org/wiki/Student%27s_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 0 12 3.199 2.144 4.595
(hours per day)
-------------------------------------------------------------
Table: 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 0.168 3e-52
(Kolmogorov-Smirnov)
normality test
Anderson-Darling normality 18.75 7.261e-44
test
--------------------------------------------
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 [R](http://www.r-project.org/) (3.0.1) and [rapport](http://rapport-package.info/) (0.51) in _0.88_ sec on x86_64-unknown-linux-gnu platform.
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