Statistics Notes

Math 121 - Fall 2025

Jump to: Syllabus, Week 1 , Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12, Week 13, Week 14

Week 1 Notes

Day Section Topic
Mon, Aug 25 1.2 Data tables, variables, and individuals
Wed, Aug 27 2.1.3 Histograms & skew
Fri, Aug 29 2.1.5 Boxplots

Mon, Aug 25

Today we covered data tables, individuals, and variables. We also talked about the difference between categorical and quantitative variables.

  1. We looked at a case of a nurse who was accused of killing patients at the hospital where she worked for 18 months. One piece of evidence against her was that 40 patients died during the shifts when she worked, but only 34 died during shifts when she wasn’t working. If this evidence came from a date table, what would be the most natural individuals (rows) & variables (columns) for that table?
  1. In the data table in the example above, who or what are the individuals? What are the variables and which are quantitative and which are categorical?

  2. If we want to compare states to see which are safer, why is it better to compare the rates instead of the total fatalities?

  3. What is wrong with this student’s answer to the previous question?

Rates are better because they are more precise and easier to understand.

I like this incorrect answer because it is a perfect example of bullshit. This student doesn’t know the answer so they are trying to write something that sounds good and earns partial credit. Try to avoid writing bullshit. If you catch yourself writing B.S. on one of my quizzes or tests, then you can be sure that you a missing a really simple idea and you should see if you can figure out what it is.

Wed, Aug 27

Today we did our first in-class workshop:

Before that, we talked about how to summarize data. We talked briefly about making bar charts for categorical data. Then we used the class data we collected last time to introduce histograms and stem-and-leaf plots (also known as stemplots).

We talked about how to tell if data is skewed left or skewed right. We also reviewed the mean and the median.

Median versus Average

The median of NN numbers is located at position N+12\dfrac{N+1}{2}.

The median is not affected by skew, but the average is pulled in the direction of the skew. So the average will be bigger than the median when the data is skewed right, and smaller when the data is skewed left.

We finished by talking about these examples.

  1. Which is greater, the mean or the median household income?

  2. Can you think of a distribution that is skewed left?

  3. Why isn’t this bar graph from the book a histogram?

Until recently, Excel did not have an easy way to make histograms, but Google Sheets does. If you need to make a histogram, I recommend using Google Sheets or this histogram plotter tool.

Fri, Aug 29

We introduced the five number summary and box-and-whisker plots (boxplots). We also talked about the interquartile range (IQR) and how to use the 1.5×IQR1.5 \times \text{IQR} rule to determine if data is an outlier.

We started with this simple example:

  1. An 8 man crew team actually includes 9 men, the 8 rowers and one coxswain. Suppose the weights (in pounds) of the 9 men on a team are as follows:

     120  180  185  200  210  210  215  215  215

    Find the 5-number summary and draw a box-and-whisker plot for this data. Is the coxswain who weighs 120 lbs. an outlier?


Week 2 Notes

Day Section Topic
Mon, Sep 1 Labor day - no class
Wed, Sep 3 2.1.4 Standard deviation
Fri, Sep 5 4.1 Normal distribution

Week 3 Notes

Day Section Topic
Mon, Sep 8 4.1.5 68-95-99.7 rule
Wed, Sep 10 4.1.4 Normal distribution computations
Fri, Sep 12 2.1, 8.1 Scatterplots and correlation

Week 4 Notes

Day Section Topic
Mon, Sep 15 8.2 Least squares regression introduction
Wed, Sep 17 8.2 Least squares regression practice
Fri, Sep 19 1.3 Sampling: populations and samples

Week 5 Notes

Day Section Topic
Mon, Sep 22 1.3 Bias versus random error
Wed, Sep 24 Review
Fri, Sep 26 Midterm 1

Week 6 Notes

Day Section Topic
Mon, Sep 29 1.4 Randomized controlled experiments
Wed, Oct 1 3.1 Defining probability
Fri, Oct 3 3.1 Multiplication and addition rules

Week 7 Notes

Day Section Topic
Mon, Oct 6 3.4 Weighted averages & expected value
Wed, Oct 8 3.4 Random variables
Fri, Oct 10 7.1 Sampling distributions

Week 8 Notes

Day Section Topic
Mon, Oct 13 Fall break - no class
Wed, Oct 15 5.1 Sampling distributions for proportions
Fri, Oct 17 5.2 Confidence intervals for a proportion

Week 9 Notes

Day Section Topic
Mon, Oct 20 5.2 Confidence intervals for a proportion - con’d
Wed, Oct 22 Review
Fri, Oct 24 Midterm 2

Week 10 Notes

Day Section Topic
Mon, Oct 27 5.3 Hypothesis testing for a proportion
Wed, Oct 29 6.1 Inference for a single proportion
Fri, Oct 31 5.3.3 Decision errors

Week 11 Notes

Day Section Topic
Mon, Nov 3 6.2 Difference of two proportions (hypothesis tests)
Wed, Nov 5 6.2.3 Difference of two proportions (confidence intervals)
Fri, Nov 7 7.1 Introducing the t-distribution

Week 12 Notes

Day Section Topic
Mon, Nov 10 7.1.4 One sample t-confidence intervals
Wed, Nov 12 7.2 Paired data
Fri, Nov 14 7.3 Difference of two means

Week 13 Notes

Day Section Topic
Mon, Nov 17 7.3 Difference of two means
Wed, Nov 19 Review
Fri, Nov 21 Midterm 3
Mon, Nov 23 7.4 Statistical power

Week 14 Notes

Day Section Topic
Mon, Dec 1 6.3 Chi-squared statistic
Wed, Dec 3 6.4 Testing association with chi-squared
Fri, Dec 5 Choosing the right technique
Mon, Dec 8 Last day, recap & review