Introduction to Probability and Statistics
14th Edition
ISBN: 9781133103752
Author: Mendenhall, William
Publisher: Cengage Learning
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Question
Chapter 12.6, Problem 12.36E
(a)
To determine
To explain: Whether any pattern other than a linear relation-ship is seen in the original plot.
(b)
To determine
To explain: the information about the fit of the regression line where the value of
(c)
To determine
To explain: whether there is any pattern in the residuals and explain whether the relationship between the number of months and the number of books written is something other than linear
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The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females.
Regression Analysis: Height Versus Shoe Size, Gender
Coefficients
Term
Coef
SE Coef
T-Value
P-Value
Constant
55.26
1.08
51.17
0.000
Shoe Size
1.167
0.17
0.000
Gender
2.578
0.482
5.35
0.000
(a)
Find the value of the test statistic for shoe size. (Round your answer to two decimal places.)
t = ??
(b) Is the regression coefficient of shoe size statistically significant? (Use ? = 0.05.)
The regression coefficient of shoe size is/is not statistically significant.
(c)
Does the variable shoe size belong in the model?
The variable shoe size may/may not belong in the model.
(d)
Interpret the regression coefficient of Gender.
Males are (??) inches taller than females, on average, controlling for shoe size.
Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where ?X is the age of the crab in months and ?ˆY^ is the predicted value of ?Y, the size of the male crab in cm.
?ˆ=9.5603+0.3976?Y^=9.5603+0.3976X
What is the value of ?ˆY^ when a male crab is 24.9118 months old? Provide your answer with precision to two decimal places.
Y=
Chapter 12 Solutions
Introduction to Probability and Statistics
Ch. 12.4 - Prob. 12.1ECh. 12.4 - Prob. 12.2ECh. 12.4 - Prob. 12.3ECh. 12.4 - Prob. 12.4ECh. 12.4 - Prob. 12.5ECh. 12.4 - You are given five points with these coordinates:...Ch. 12.4 - Prob. 12.7ECh. 12.4 - Prob. 12.8ECh. 12.4 - Prob. 12.9ECh. 12.4 - Prob. 12.10E
Ch. 12.4 - Prob. 12.11ECh. 12.4 - Prob. 12.12ECh. 12.4 - Prob. 12.13ECh. 12.4 - Prob. 12.14ECh. 12.4 - Prob. 12.15ECh. 12.4 - Prob. 12.16ECh. 12.4 - Prob. 12.17ECh. 12.5 - Prob. 12.19ECh. 12.5 - Prob. 12.20ECh. 12.5 - Prob. 12.21ECh. 12.5 - Prob. 12.22ECh. 12.5 - Prob. 12.23ECh. 12.5 - Prob. 12.24ECh. 12.5 - Professor Asimov, continued Refer to thedata in...Ch. 12.5 - Prob. 12.26ECh. 12.5 - Prob. 12.27ECh. 12.5 - Prob. 12.28ECh. 12.5 - Prob. 12.29ECh. 12.5 - Prob. 12.30ECh. 12.6 - Prob. 12.34ECh. 12.6 - Prob. 12.35ECh. 12.6 - Prob. 12.36ECh. 12.6 - Prob. 12.37ECh. 12.6 - Prob. 12.38ECh. 12.7 - Refer to Exercise 12.7. Portions of the MINITAB...Ch. 12.7 - Prob. 12.41ECh. 12.7 - Prob. 12.42ECh. 12.7 - Prob. 12.43ECh. 12.7 - Prob. 12.44ECh. 12.7 - Prob. 12.45ECh. 12.7 - Prob. 12.46ECh. 12.8 - Prob. 12.50ECh. 12.8 - Prob. 12.51ECh. 12.8 - Prob. 12.52ECh. 12.8 - Prob. 12.53ECh. 12.8 - Prob. 12.55ECh. 12.8 - Prob. 12.56ECh. 12.8 - Prob. 12.58ECh. 12.8 - Baseball Stats Does a team’s batting average...Ch. 12 - Prob. 12.62SECh. 12 - Prob. 12.63SECh. 12 - Prob. 12.65SECh. 12 - Prob. 12.66SECh. 12 - Prob. 12.67SECh. 12 - Tennis, Anyone? If you play tennis, you know that...Ch. 12 - Prob. 12.69SECh. 12 - Prob. 12.70SECh. 12 - Prob. 12.71SECh. 12 - Movie Reviews How many weeks cana movie run and...Ch. 12 - In addition to increasingly large bounds onerror,...Ch. 12 - Prob. 12.74SECh. 12 - Prob. 12.76SECh. 12 - Prob. 1CSCh. 12 - Prob. 2CSCh. 12 - Prob. 3CS
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardFor the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.arrow_forward
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