EP INTRODUCTION TO PROBABILITY+STAT.
14th Edition
ISBN: 2810019974203
Author: Mendenhall
Publisher: CENGAGE L
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Chapter 13.4, Problem 13.5E
a.
To determine
To find: The model that has been selected to fit the data.
b.
To determine
To explain: The extent of the reliability of the data.
c.
To determine
To explain: Whether the enough evidences are provided by the data set to show that the information for the prediction of y is contributed by the model.
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Chapter 13 Solutions
EP INTRODUCTION TO PROBABILITY+STAT.
Ch. 13.4 - Prob. 13.1ECh. 13.4 - Prob. 13.2ECh. 13.4 - Suppose that you fit the model E(y)=0+1x1+2x2+3x3...Ch. 13.4 - Prob. 13.4ECh. 13.4 - Prob. 13.5ECh. 13.4 - Prob. 13.6ECh. 13.4 - Prob. 13.7ECh. 13.4 - Prob. 13.8ECh. 13.4 - Prob. 13.9ECh. 13.4 - College Textbooks A publisher of college textbooks...
Ch. 13.4 - Prob. 13.11ECh. 13.4 - Prob. 13.12ECh. 13.4 - Prob. 13.13ECh. 13.4 - Prob. 13.14ECh. 13.4 - Prob. 13.15ECh. 13.4 - Prob. 13.16ECh. 13.5 - Prob. 13.17ECh. 13.5 - Prob. 13.18ECh. 13.5 - Prob. 13.19ECh. 13.5 - Prob. 13.20ECh. 13.5 - Prob. 13.21ECh. 13.5 - Prob. 13.22ECh. 13.5 - Prob. 13.23ECh. 13.5 - Construction Projects In a study to examine the...Ch. 13 - Prob. 13.25SECh. 13 - Prob. 13.26SECh. 13 - Prob. 13.28SECh. 13 - Prob. 13.29SECh. 13 - Prob. 13.30SECh. 13 - Prob. 13.31SECh. 13 - Prob. 13.32SECh. 13 - Prob. 13.33SECh. 13 - Quality Control A manufacturer recorded the number...Ch. 13 - Prob. 13.35SECh. 13 - Prob. 13.36SE
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_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_forwardSuppose you fit the first-order model y = Po + B, x, + B2x2 + B3X3 + B,x4 + B5X5 + ɛ to n=28 data points and obtain SSE = 0.33 and R = 0.94. Complete parts a and b. a. Do the values of SSE and R suggest that the model provides a good fit to the data? Explain. A. Yes. Since R = 0.94 is close to 1, this indicates the model provides a good fit. Also, SSE = 0.33 is fairly small, which indicates the model provides a good fit. B. There is not enough information to decide. No. Since R = 0.94 is close to 1, this indicates the model does not provide a good fit. Also, SSE = 0.33 is fairly small, which indicates the model does not provide a good fit. b. Is the model of any use in predicting Test the null hypothesis Ho: B, = B2 = ... = B5 = 0 against the alternative hypothesis: At least of the %3D parameters B1, B2, B5 is nonzero. Use a = 0.05. The test statistic is (Round to two decimal places as needed.)arrow_forward
- Which of the following is the most appropriate equation to model the data? ŷ = 1.1x + 1.467 ŷ = 1.467x + 1.1 ŷ = 1.1(1.467)x ŷ = 1.467(1.1)xarrow_forwardQ22arrow_forwardIn the graph shown below consider the horizontal line to represent the average y value for the data set and the slant line to represent the linear regression equation. use only the graph of the data to do the following:arrow_forward
- Forty observations were used to estimate y = Bo + ẞ1x1 + B2x2 + ε. The regression results are shown in the accompanying table. Coefficients Intercept x1 x2 13.83 -2.53 0.29 Standard Error t Stat 2.42 0.15 5.71 -16.87 p-Value 1.56E-06 5.84E-19 0.06 4.83 2.38E-05 a. Interpret the point estimate for ẞ1. As x1 increases by 1 unit, y is predicted to decrease by 2.53 units. As x1 increases by 1 unit, y is predicted to increase by 0.29 units. As x1 increases by 1 unit, y is predicted to decrease by 2.53 units, holding x2 as a constant. As x1 increases by 1 unit, y is predicted to increase by 0.29 units, holding x2 as a constant. b. What is the sample regression equation? (Negative value should be indicated with a minus sign. Round final answer to 2 decimal places.) ŷ = + X1 X2-arrow_forward2. ATV Deaths. The number of deaths from all-terrain vehicles for selected years is given in the table. Number of Deaths Year a) Create a scatterplot for the data. Graph the scatterplot below. b) Determine what type of model is appropriate for the data. 1995 200 1997 241 1999 398 c) Use the appropriate regression on your calculator to find a model and store it in Y1. Graph the regression equation in the same coordinate plane below. 517 636 2001 2003 2005 467 d) Estimate the years in which the number of ATV deaths was or will be 300. Discuss the accuracy of your estimates.arrow_forwardQ2) Convert the data in table below into information using regression approach. X 1 2 3 4 5 6 Y 6 1 9 5 17 12arrow_forward
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