## 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.

This report was generated with R (3.0.1) and rapport (0.51) in 0.814 sec on x86_64-unknown-linux-gnu platform. 