- Suppose you were to collect data for a pair of variables and want to make a scatter plot. Which variable would you use as the explanatory variable and which as the response variable. Explain your choices. What would you expect to see in the scatter plot. Discuss the likely direction, form, and scatter. Make a sketch of what you would expect to see. Look at problems 7.1-4
- Look at a plot of the residuals vs the predicted (fitted) value and a plot of the residuals vs the predictor variable and tell what it indicates about the appropriateness of the linear model that was fit to the data. Explain more than "good fit" or "bad fit". Two parts. Look at problems 8.3-4
- Look at some scatter plots. Identify which plots show little or no association (of any kind, not just linear), negative association, linear association, moderately strong association, and very strong association. Then take the given values for the correlation coefficient and match them to the scatter plots. Look at problems 7.5-6 and 7.11-12.
- An introduction to the data and then the regression equation, table of
coefficients, r-squared, and a residual plot from a regression
problem are given. Write a model to predict
one of the variables. Explain whether or not a linear model is appropriate.
Explain the reliability of the estimates. Find the residual for a given data
point. Explain what the value of r
^{2}means. Look at problems 8.8, 22, 26, 31. - A scatter plot with the regression line drawn on it is given. Each axis is centered about its mean and the tick marks are one standard deviation apart. Find the standard deviation for the response and predictor variables. The slope of the regression line is given, use it and the centroid to find the equation of the best fit line. Use the equation to estimate the response variable for a given value of the predictor variable. Use algebra to find the correlation coefficient (formula is given to you). Look at the notes from class.
- Explain how you would set up a simulation to answer the question. You do not need to actually perform the simulation, but be complete in your description. Two parts. Look at problems 11.9-29
- A report about a statistical study is given. Identify the population, the population parameter of interest, the sampling frame, the sample, the sampling method, including whether or not randomization was employed, and any potential sources of bias you can detect and any problems you see in generalizing to the population of interest. Look at problems 12.1-10.
- Examine the question for possible bias. If you think the question is biased, indicate how and propose a better question. Three parts. Look at problems 12.13-15.
- Decide whether the statistical research was an observational study or an experiment. Identify (if possible) the subjects studied and the nature and scope of the conclusion the study or experiment an reach. Three parts. Look at problems 13.1-18.
- Read a report about an observational study. Identify (if possible) whether it was retrospective or prospective and the parameter of interest. Look at problems 13.1-18.
- Identify the type of sampling used in each of the situations. Ten parts. Know the sampling methods from chapter 12.
- Read a report about an experiment. Identify (if possible) the factor(s) in the experiment and the number of levels for each, the number of treatments, the response variable measured, the design (completely, randomized, blocked, or matched), and whether it was blind (or double-blind). Look at problems 13.1-18.

- Most of the problems are similar to problems in the textbook. Very few of the problems are identical to the problems from the text.
- You will not need Minitab or Active Stats. Your computer screens should be off during the exam.
- Whenever there are problems that ask for an explanation, be sure you explain. Those parts are worth more points based on the explanation.
- You will need a calculator.

# | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Total |
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Pts | 5 | 4 | 13 | 10 | 10 | 10 | 12 | 9 | 3 | 4 | 10 | 10 | 100 |