project2-starter.ipynb

Kernel: Python 2

Project 2

In this project, you will implement the exploratory analysis plan developed in Project 1. This will lay the groundwork for our our first modeling exercise in Project 3.

Step 1: Load the python libraries you will need for this project

#imports
from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pylab as pl
import numpy as np
%matplotlib inline

Step 2: Read in your data set

#Read in data from source 
df_raw = pd.read_csv("../assets/admissions.csv")
print df_raw.head()

   admit  gre   gpa  prestige
0      0  380  3.61         3
1      1  660  3.67         3
2      1  800  4.00         1
3      1  640  3.19         4
4      0  520  2.93         4

Questions

Question 1. How many observations are in our dataset?

df_raw.count()

admit       400
gre         398
gpa         398
prestige    399
dtype: int64

Answer:

Question 2. Create a summary table

#function

Question 3. Why would GRE have a larger STD than GPA?

Answer:

Question 4. Drop data points with missing data

Question 5. Confirm that you dropped the correct data. How can you tell?

Answer:

Question 6. Create box plots for GRE and GPA

#boxplot 1

#boxplot 2 

Question 7. What do this plots show?

Answer:

Question 8. Describe each distribution

# plot the distribution of each variable 

Question 9. If our model had an assumption of a normal distribution would we meet that requirement?

Answer:

Question 10. Does this distribution need correction? If so, why? How?

Answer:

Question 11. Which of our variables are potentially colinear?

# create a correlation matrix for the data

Question 12. What did you find?

Answer:

Question 13. Write an analysis plan for exploring the association between grad school admissions rates and prestige of undergraduate schools.

Answer:

Question 14. What is your hypothesis?

Answer:

Bonus/Advanced

1. Bonus: Explore alternatives to dropping obervations with missing data

2. Bonus: Log transform the skewed data

3. Advanced: Impute missing data