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 |
Today we covered data tables, individuals, and variables. We also talked about the difference between categorical and quantitative variables.
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?
If we want to compare states to see which are safer, why is it better to compare the rates instead of the total fatalities?
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.
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.
The median of numbers is located at position .
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.
Which is greater, the mean or the median household income?
Can you think of a distribution that is skewed left?
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.
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 rule to determine if data is an outlier.
We started with this simple example:
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?
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 |
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 |
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 |
Day | Section | Topic |
---|---|---|
Mon, Sep 22 | 1.3 | Bias versus random error |
Wed, Sep 24 | Review | |
Fri, Sep 26 | Midterm 1 |
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 |
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 |
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 |
Day | Section | Topic |
---|---|---|
Mon, Oct 20 | 5.2 | Confidence intervals for a proportion - con’d |
Wed, Oct 22 | Review | |
Fri, Oct 24 | Midterm 2 |
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 |
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 |
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 |
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 |
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 |