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

Contents
•	Description
–	Introduction
–	Normality assumption check (Internet usage for educational purposes (hours per day))
–	Normality assumption check (Age)
•	Description
–	Introduction
–	The F-test
•	Description
–	Introduction
–	The F-test
Description
This template will run an F-test to check if two continuous variables have the same means.
Introduction
F test compares the means of two continuous variables. In other words it shows if their means were statistically different. We should be careful, while using the F test, because of the strict normality assumption, where strict means approximately normal ditribution is not enough to satisfy that.
Normality assumption check (Internet usage for educational purposes (hours per day))
The Shapiro-Wilk test, the Lilliefors test and the Anderson-Darling test help us to decide if the above-mentioned assumption can be accepted of the Internet usage for educational purposes (hours per day).
Method
	Statistic
	p-value
	
Lilliefors (Kolmogorov-Smirnov) normality test
	0.2223
	2.243e-92
	
Anderson-Darling normality test
	42.04
	3.31e-90
	
Shapiro-Wilk normality test
	0.7985
	6.366e-28

So, the conclusions we can draw with the help of test statistics:
•	based on Lilliefors test, distribution of Internet usage for educational purposes (hours per day) is not normal
•	Anderson-Darling test confirms violation of normality assumption
•	according to Shapiro-Wilk test, the distribution of Internet usage for educational purposes (hours per day) is not normal
As you can see, the applied tests confirm departures from normality.
Normality assumption check (Age)
The Shapiro-Wilk test, the Lilliefors test and the Anderson-Darling test help us to decide if the above-mentioned assumption can be accepted of the Internet usage for educational purposes (hours per day).
Method
	Statistic
	p-value
	
Lilliefors (Kolmogorov-Smirnov) normality test
	0.17
	6.193e-54
	
Anderson-Darling normality test
	32.16
	1.26e-71
	
Shapiro-Wilk normality test
	0.8216
	9.445e-27

So, the conclusions we can draw with the help of test statistics:
•	based on Lilliefors test, distribution of Age is not normal
•	Anderson-Darling test confirms violation of normality assumption
•	according to Shapiro-Wilk test, the distribution of Age is not normal
As you can see, the applied tests confirm departures from normality.
In this case it is advisable to run a more robust test, then the F-test.
Description
This template will run an F-test to check if two continuous variables have the same means.
Introduction
F test compares the means of two continuous variables. In other words it shows if their means were statistically different. We should be careful, while using the F test, because of the strict normality assumption, where strict means approximately normal ditribution is not enough to satisfy that.
The F-test
Here is the the result of the F test to compare the means of Internet usage for educational purposes (hours per day) and Age.
Method
	Statistic
	p-value
	
F test to compare two variances
	0.08618
	3.772e-180

We can see from the table (in the p-value coloumn) that there is a significant difference between the means of Internet usage for educational purposes (hours per day) and Age.
Description
This template will run an F-test to check if two continuous variables have the same means.
Introduction
F test compares the means of two continuous variables. In other words it shows if their means were statistically different. We should be careful, while using the F test, because of the strict normality assumption, where strict means approximately normal ditribution is not enough to satisfy that.
The F-test
Here is the the result of the F test to compare the means of cyl and drat.
Method
	Statistic
	p-value
	
F test to compare two variances
	11.16
	1.461e-09

We can see from the table (in the p-value coloumn) that there is a significant difference between the means of cyl and drat.
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This report was generated with R (3.0.1) and rapport (0.51) in 0.814 sec on x86_64-unknown-linux-gnu platform.