Midterm information
The first midterm, in class on Monday, will cover all material up to Chapter 13. Please note that I
forgot to list Chapter 10 on the syllabus, but we did cover it. (Of course, you all know that, because you
have been reading along.) Students generally find the material covered in the first part of the class easier
than the material in the second half of the class.
Here is a list of material you should be familiar with:
The difference between categorical and quantitative variables
The definition of a distribution
How to read and create the various types of charts: pie charts, bar graphs, histograms, stemplots
(including split-stems and back-to-back), time plots, boxplots
The shape (symmetric, skewed left, skewed right, two-peaked, etc.), center, and spread of a distribution
Identifying outliers
Being able to determine x,s, and the five-number summary, and knowing when to use which
The relationship between the median and mean (including resistance to outliers)
The definition of a density curve, and the median and mean of a density curve
Normal distributions, including the meaning of Āµand Ļ, standardizing (finding z-scores), the standard
Normal distribution, calculating Normal distribution probabilities by using the 68-95-99.7 rule or by using
az-table, ābackwardsā calculations involving a Normal distribution
The difference between observational studies and experiments
The meaning of explanatory, response, and lurking variables
The difference between populations and samples, and between parameters and statistics
The difference between different sampling techniques, what sampling bias is, and why we usually prefer
probability samples
How to choose an SRS or stratified random sample, using a table of random digits
Sampling issues, such as undercoverage, nonresponse, response bias, and question wording
Identifying the subjects, factors, and treatments involved in an experiment
Experiment design, including completely randomized designs, matched-pairs designs, and block designs
Use of placebos and double-blind conditions
The meaning of statistical significance
The concept behind probability
Probability models, including the rules
Types of random variables: discrete or continuous
What a sampling distribution is
The distribution of a sample from a Normally distributed population
What the Central Limit Theorem means and how to use it to approximate the sampling distribution of
a large sample size
What a confidence interval is, and how to find confidence intervals for a Normally distributed population
when Ļis known
How confidence intervals behave when Ļ,nor the confidence level changes
Determining the sample size nneeded to obtain a certain margin of error
On the exam, you will be allowed to use a copy of the tables and a sheet of formulas. (The pink insert
is perfect.) You will not be allowed to bring sample problems.
Attached is a handout of practice problems. Ignore problem 9.
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