Homework: Data

Submit this homework via Dropbox. For the deadline please consult the schedule posted on Blackboard.

• Python-scripts are submitted in one file called: homework7.py

For this homework you need to use the Baltimore dataset posted here. Keep in mind that there are numerous ways to import data into Python.

The procedure I showed in class requires that you save the data from within Excel as a .csv file. In Excel go to save as and switch the filetype from .xlsx to .cvs. This should save the Excel file as a .csv file (comma delimited values). Before you save it make sure that you remove all the extra formatting from the numbers in Excel, so that you don’t run into problems importing them later into Python. Marking numbers and then setting them to “general” in the ribbon should normally do the trick and remove extra formatting symbols like commas etc. For instance, a number that reads 16,216 should read 16216 after reformatting etc.

You can also try to import the Excel file directly. The import command is described in the lecture notes.

Exercise 1:

Import the data into a DataFrame called: myData

Exercise 2:

Make summary statistics (e.g., mean, median, mode, and standard deviations) of all numerical variables. That is, print a table that says something like this:

Variable 1:
==========
mean: mean(Variable 1)
median: median(variable 1)
mode: mode(variable 1)
standard deviation: stand.Dev.(Variable 1)

Variable 2:
==========
mean: mean(Variable 2)
median: median(variable 2)
mode: mode(variable 2)
etc.

Exercise 3:

Make a scatterplot of Median Income and Bachelors degree. The latter variable measures the percentage of the population in the county that has a bachelors degree.

Exercise 4:

Run a regression model of the following form:

\[\begin{split}\text{JuvenileArrestRate} = \beta_0 + \beta_1 \text{Population} + \beta_2 \text{MedianIncome} \\ + \beta_3 \text{Unemployment} + \beta_4 \text{Eduation} + \beta_5 \text{Poverty} + \epsilon\end{split}\]

Report a table with all coefficient estimates including p-values.

Exercise 5:

Run the model from Exercise 4 again, but this time replace Population with a categorical variable for county size. You should make 3 categories here: small_counties, middle_counties, and large_counties.

A small county is defined as a county with a population smaller than \(6,500\). A mid size county has a population between \(6,500\) and \(10,000\) and a large size county has a population larger than \(10,000\) people. Make dummy variables and include those into your regression. Review how to make dummies etc. from your stats notes or from the brief introduction in the lecture.