Dr Peggy Kern's Capstone Statistics Series

Welcome to my intro to statistics series! As part of the MAPP (Masters of Applied Positive Psychology) programs, first at UPenn and then at UniMelb, I have taught an introductory statistics course in an online learning format. I enjoy making statistics a bit more interesting and digestible for students who might not like math or are scared of numbers. Building on that, this series is designed to cover an entire semester of introductory material, through bite sized 10-15 minute videos. The first set of lectures focuses on descriptive statistics, in which we are describing a set of data, visually and through numbers. The second set of lectures focuses on inferential statistics, in which we use what we know about a sample to make inferences about the larger population. The series touches on important techniques, focusing on conceptual understanding and practical skills.

I hope to create a full series, but need to find the time for this. This provides a starting set. 

Lecture 1: Introduction

Introduction to the series, including what are statistics, a bit of historical background, why you should care.

Lecture 2: Frequencies

Descriptive statistics part 1, focusing on understanding frequencies and frequency distributions.

Lecture 3: Central Tendency

Descriptive statistics part 2, measures of central tendencies - ways of describing the middle of a set of data. Includes means, median, mode, and trimmed mean.

Lecture 4: Spread

Descriptive statistics part 3: describing the spread of the data, including minimum, maximum, range, quartiles, percentiles, and inter-quartile range.

Lecture 5: Variance

Descriptive statistics part 4: standard deviation and variance. 

Lecture 6: Graphing Data

Descriptive statistics part 5: graphing and visualizing data, including histograms and bar charts, box plots, and more. 

Lecture 7: Descriptive Summary

Lecture 8: Descriptives in Excel
Descriptive statistics part 6: how descriptive statistics come together and are reported in a paper, informing the methods and results sections.
In progress...

Lecture 9: Descriptives in SPSS

Lecture 10: Z-Scores
In progress...
Z-scores offer another type of descriptive statistic that  move us toward inferential statistics. Introduction to the normal distribution, proportions, Z scores, and standization. See problem set 2 for practice.

Lecture 11: Correlations part 1

Lecture 12: Correlations part 2
Correlations part 1. Introduces the concept of correlation - the relationship between two variables, including the size and direction of such associations.
Correlations part 2. Here we consider how to calculate correlations, drawing on examples from my recent cycling adventures to illustrate. See below for practice problems.

Lecture 13: Sample & populations part 1

​Samples and populations, part 1. We shift toward inferential statistics, considering sampling decisions - who is included in the sample - and how these choices impact the generalizabilty of results.

Lecture 14: Samples & populations part 2

Samples and populations part 2. We examine sampling distributions, and consider how to use data from a sample to approximate population values, with adjustments to our standard deviation and correlation formulas. 
Stay tuned - more videos coming soon!!

Practice Problems

In my own learning journey, I have found that I learn statistics best by doing. I will be adding some practice problems corresponding with the material throughout the series. (Download the file and try to complete the problems before looking at the answer sheet.)
Practice Problems #1: Measures of central tendency and spread (lectures 3-5)
     Problems              Answer sheet

Practice Problems #2: The Normal distribution and z scores (lecture 10)
     Problems              Answer sheet

Practice Problems #3: Correlations (lectures 11 & 12)
     Problems              Answer sheet