---
title: "Extra Credit #2: The Duke GPA Data Analysis"
author: "Milica Cudina"
date: "`r Sys.Date()`"
output: pdf_document
---
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knitr::opts_chunk$set(
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---

## Task 1. (1 point)
First, we will read the data from our csv file "gpa.csv" into a data.frame called `gpa.data`:

If you want to see what your data.frame looks like, you can click on it in the **Global environment** in the upper right pane. The data.frame will get displayed in the upper left pane. 

## Task 2. (1 point)
You interested in the types and names of the variables in your data.frame. What do you run?

You see that the students/**cases** all have corresponding **rows**. They are labeled by the row indices. The **column** names stand for the variable names.

Then, you can do a bit of exploratory analysis. 

## Task 3. (3 points)
What are the minimum and maximum GPAs? What is the mean GPA? What is the median GPA?


## Task 4. (2 points)
Plot the histogram of the GPAs. Make sure that your plot has the main title and that the axes are also labeled. 

## Task 5. 
Plot the histogram of the number of hours spent studying per week. Make sure that your plot has the main title and that the axes are also labeled. 

## Task 6. 
Is the mean number of hours spent studying different for females than for males? 

## Task 7. 
Any difference in the GPA?

## Task 8. 
Is there a relationship between the hours studied and the GPA?

## Task 9. 
Is there an effect of gender? Let's create a scatterplot with the two categories indicated by different colors.