Measuring Up: Python and Statistics Notes

Motivation

This book introduces Python programming, basic statistics, and data visualization. It is written for an audience of first-year undergraduate students with no previous programming experience. Specifically, it is used in the Measuring Up sections of Lewis & Clark College's first-year quantitative seminar Numbers, taught by Peter Drake and Liz Stanhope.

More advanced readers will know that there are more concise, efficient, or Pythonic ways to do things. We have placed more emphasis on using a minimal set of tools to make the material more accessible.

Contents

Level I: Data Visualization

• The Interactive Console
• Arithmetic Operators + - * / **
• Defining a Variable =
• Writing a Python Program
• The print Function
• The List Data Type []
• The String Data Type '' ""
• The math Library
• Observational Units and Variables
• The matplotlib.pyplot Library
• About Bar Charts
• About Histograms
• About Line Plots
• About Scatter Plots

Level II: Measures of Central Tendency

• The len Function
• The sum Function
• The Mean (Average) of a List of Numbers
• Defining a Function def
• The sorted Function
• Getting a Sequence Element at a Specific Index With []
• Testing Equality ==
• Making a Decision if
• Integer Division // %
• The Median (Middle Value) of a List of Numbers
• Counting Occurrences count
• The Mode (Most Common Value) of a List of Numbers

Level III: Measures of Spread

• List Comprehensions
• Type Conversion int float list str bool
• Reading Data From a File csv
• About Outliers
• The Range of a List of Numbers
• Percentiles and Quartiles (Dividing up the Data)
• The Interquartile Range (Range of the Middle Half of the Data)
• About Box Plots
• Standard Deviation (Sophisticated Measure of Spread)

Level IV: Distributions

• Distributions
• The Set Data Type {}
• Plotting a Frequency Bar Chart
• Dealing With Missing Data
• Testing Membership in
• Combining Lists zip
• Slicing Sequences [:]

Level V: Populations, Samples, and the Central Limit Theorem

• Proportions
• Populations and Samples
• The Empirical Rule for Normal Distributions
• Samping Distributions
• The Central Limit Theorem for Proportions
• 95% Confidence Intervals

Level VI: Relationships Between Variables

• Correlation
• Regression