Chapter 12: Problem 29
What diagnostic plot can you use to determine whether the incorrect model has been used? What should the plot look like if the correct model has been used?
Chapter 12: Problem 29
What diagnostic plot can you use to determine whether the incorrect model has been used? What should the plot look like if the correct model has been used?
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Get started for freeWhat diagnostic plot can you use to determine whether the data satisfy the normality assumption? What should the plot look like for normal residuals?
The following data (Exercises 12.16 and 12.24 ) were obtained in an experiment relating the dependent variable, \(y\) (texture of strawberries), with \(x\) (coded storage temperature). $$ \begin{array}{l|rrrrr} x & -2 & -2 & 0 & 2 & 2 \\ \hline y & 4.0 & 3.5 & 2.0 & 0.5 & 0.0 \end{array} $$ a. Estimate the expected strawberry texture for a coded storage temperature of \(x=-1 .\) Use a \(99 \%\) confidence interval. b. Predict the particular value of \(y\) when \(x=1\) with a \(99 \%\) prediction interval. c. At what value of \(x\) will the width of the prediction interval for a particular value of \(y\) be a minimum, assuming \(n\) remains fixed?
Does a team's batting average depend in any way on the number of home runs hit by the team? The data in the table show the number of team home runs and the overall team batting average for eight selected major league teams for the 2006 season. \(^{14}\) $$ \begin{array}{lcc} \text { Team } & \text { Total Home Runs } & \text { Team Batting Average } \\\ \hline \text { Atlanta Braves } & 222 & .270 \\ \text { Baltimore Orioles } & 164 & .227 \\ \text { Boston Red Sox } & 192 & .269 \\ \text { Chicago White Sox } & 236 & .280 \\ \text { Houston Astros } & 174 & .255 \\ \text { Philadelphia Phillies } & 216 & .267 \\ \text { New York Giants } & 163 & .259 \\ \text { Seattle Mariners } & 172 & .272 \end{array} $$ a. Plot the points using a scatterplot. Does it appear that there is any relationship between total home runs and team batting average? b. Is there a significant positive correlation between total home runs and team batting average? Test at the \(5 \%\) level of significance. c. Do you think that the relationship between these two variables would be different if we had looked at the entire set of major league franchises?
In Exercise 12.15 (data set EX1215), we measured the armspan and height of eight people with the following results: $$ \begin{array}{l|clll} \text { Person } & 1 & 2 & 3 & 4 \\ \hline \begin{array}{l} \text { Armspan (inches) } \\ \text { Height (inches) } \end{array} & 68 & 62.25 & 65 & 69.5 \\ & 69 & 62 & 65 & 70 \\ \text { Person } & 5 & 6 & 7 & 8 \\ \hline \text { Armspan (inches) } & 68 & 69 & 62 & 60.25 \\ \text { Height (inches) } & 67 & 67 & 63 & 62 \end{array} $$ a. Does the data provide sufficient evidence to indicate that there is a linear relationship between armspan and height? Test at the \(5 \%\) level of significance. b. Construct a \(95 \%\) confidence interval for the slope of the line of means, \(\beta\). c. If Leonardo da Vinci is correct, and a person's armspan is roughly the same as the person's height, the slope of the regression line is approximately equal to \(1 .\) Is this supposition confirmed by the confidence interval constructed in part b? Explain.
A marketing research experiment was conducted to study the relationship between the length of time necessary for a buyer to reach a decision and the number of alternative package designs of a product presented. Brand names were eliminated from the packages to reduce the effects of brand preferences. The buyers made their selections using the manufacturer's product descriptions on the packages as the only buying guide. The length of time necessary to reach a decision was recorded for 15 participants in the marketing research study. $$ \begin{array}{l|l|l|l} \begin{array}{l} \text { Length of Decision } \\ \text { Time, } y(\mathrm{sec}) \end{array} & 5,8,8,7,9 & 7,9,8,9,10 & 10,11,10,12,9 \\ \hline \text { Number of } & & & \\ \text { Alternatives, } x & 2 & 3 & 4 \end{array} $$ a. Find the least-squares line appropriate for these data. b. Plot the points and graph the line as a check on your calculations. c. Calculate \(s^{2}\). d. Do the data present sufficient evidence to indicate that the length of decision time is linearly related to the number of alternative package designs? (Test at the \(\alpha=.05\) level of significance.) e. Find the approximate \(p\) -value for the test and interpret its value. f. If they are available, examine the diagnostic plots to check the validity of the regression assumptions. g. Estimate the average length of time necessary to reach a decision when three alternatives are presented, using a \(95 \%\) confidence interval.
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