Outline of topics– 223 Midterm
Your best guide is the Powerpoint lectures and the quizzes. However, because it has been requested, I have created this guide to give you an outline of the topics that have been covered.
For this exam, I will provide all formulas along with a z-table.
You should bring a simple calculator. Be sure it has a square-root key. You may NOT bring any kind of calculator if it is part of a data-device (eg iphone or other organizer). Graphing calculators are allowed.
· Quantitative vs Categorical variables
· Charting data: Which charts do you use for categorical data? Which for quantitative (aka nominal) data?
· Charting data: When to use bar vs pie charts.
· Deceptions or misleading information when using pie charts
· Use of histograms.
o Limtations/Misinterpretations of histograms
o Difference between histogram and bar chart. For example, a histogram should not have spaces between the bars – unless there is 0 data for that particular bin.
o Impact of skewed data on a histogram
· Outliers: Identification.
· Mean vs Median
· The term “resistant” (as it applies to statistics)
o 5-Number summary
o 1.5 rule for outliers
· Standard Deviation
o What the concept means
o “Properties” of s
· Density curves
o How the z-score lets you compare “apples and oranges” (e.g. SAT scores and ACT scores)
o Calculations involving areas/percentages/z-scores etc
o Normal distribution: concept & interpretation, properties
· Normal quantile plot
o When are they used?
o Explanatory vs response variables
o Interpretation (eg what does a negative slope tell us? Strength, Outliers)
o Categorical data as the explanatory variable
· Correlation coefficient including properties of r
o What is a regression line? Why is it helpful?
o What is the name of the method we have used to determine the best regression line?
o Understand the y = b0 + b1*x formula
o Know how to find and calculate b0 and b1
o Correlation vs regression
o Extrapolation – problems with
· Coefficient of determination (R2) – what does it mean?
· Residuals and residual plots
· Effect of outliers and influentials points
· Why is it important to always plot data? (see example in regression lecture)
· Lurking/Confounding variables
· Difference between association and causation
· Anecdotal data
· Difference between Population vs sample
· Counfounding variable
· “Controls” in experimentation
· Biases: review and understand the examples. Review ways to avoid bias
· Double-blind experimentation
· Randomization (not how to do it, but why it is important)