Rapport package team
 t-test Template
 2011-04-26 20:25 CET

Contents
•	Description
–	Introduction
–	Overview
–	Descriptives
–	Diagnostics
–	Results
•	Description
–	Introduction
–	Overview
–	Descriptives
–	Diagnostics
–	Results
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 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 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 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
	
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

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This report was generated with R (3.0.1) and rapport (0.51) in 0.88 sec on x86_64-unknown-linux-gnu platform.