Table of Contents

**Class Activities**

To download the Excel files, go on *File*, then select *Download as*, and select *Microsoft Excel (.xlsx)*

- Ch. 1: Selecting a Random Sample of European Firms
- Ch. 2: Distribution of Homeruns in MLB (2014 season)
- Ch. 3: Who is the Greatest Basketball Player… yet?
- Ch. 3: Computing the Correlation Coefficient Between the Value and Revenue of the Teams in the NBA
- Ch. 4: Bayes Rule in a Bidding War
- Ch. 6: Finding a Parking Spot in Bricktown, OKC
- Ch. 8: Estimating a Confidence Interval for the Average Time Spent on Social Media over a Weekend
- Ch. 9: Testing for Mean Weight of Chocolate Bars
- Ch. 9: Testing a Set of Hypotheses About The Average Home Attendance in MLB Games (1990-2010)
- Ch. 9 and 10: Hypothesis Testing Summary

**Previous Exams**

- Midterm Exam 1.: On Descriptive Statistics (Fall 2015, Spring 2016, Fall 2016, and Spring 2017)
- Midterm Exam 2.: On Probability (Fall 2015, Spring 2016, Fall 2016, and Spring 2017)
- The Final: On Inferential Statistics (Fall 2015, Spring 2016, and Fall 2016)
- Comprehensive Make-up Exam: On Descriptive Statistics and Probability (Fall 2015, Spring 2016, and Fall 2016)

**Statistical Packages**

As described in Course Description, I use MS Excel to illustrate how descriptive and inferential statistical analyses may be conducted using computers. This software is chosen because students have already had some exposures to MS Office, and they also have free access to this package at the University of Oklahoma.

Upon completion of this course, students are able to learn more about statistical packages. As indicated in the lecture series, there are many packages out there. (Wikipedia, for instance, keeps a long list of those packages). Given what students have access to at the University of Oklahoma, I recommend the following courses.

Go on Lynda, sign in using your 4×4, and look for:

- Statistics with Excel (part one is 3h45m, and part two is 2h)
- Learning R (2h25m)
- R for Excel Users (1h26m)
- R Statistics Essential Training (5h59m)
- Python for Data Science Essential Training (6h32m)

The above are video series, using which you may learn how to employ statistical packages for descriptive and inferential statistical analyses. At this point, this is your choice. Given your background and interest, you may choose any of the above courses. Excel is already available to you. R and Python are also freely available. Go for the package of your choice, and have fun learning.