Introductory Statistics (10th Edition)
10th Edition
ISBN: 9780321989178
Author: Neil A. Weiss
Publisher: PEARSON
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Textbook Question
Chapter A.4, Problem 75E
Explain why the predictor variables are useless as predictors of the response variable if the partial slopes of the population regression plane are all zero.
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Students have asked these similar questions
We have data on Lung Capacity of persons and we wish
to build a multiple linear regression model that predicts
Lung Capacity based on the predictors Age and
Smoking Status. Age is a numeric variable whereas
Smoke is a categorical variable (0 if non-smoker, 1 if
smoker). Here is the partial result from STATISTICA.
b*
Std.Err.
of b*
Std.Err.
N=725
of b
Intercept
Age
Smoke
0.835543
-0.075120
1.085725
0.555396
0.182989
0.014378
0.021631
0.021631
-0.648588
0.186761
Which of the following statements is absolutely false?
A. The expected lung capacity of a smoker is expected
to be 0.648588 lower than that of a non-smoker.
B. The predictor variables Age and Smoker both
contribute significantly to the model.
C. For every one year that a person gets older, the lung
capacity is expected to increase by 0.555396 units,
holding smoker status constant.
D. For every one unit increase in smoker status, lung
capacity is expected to decrease by 0.648588 units,
holding age constant.
Researchers at a large nutrition and weight management company are trying to build a model to predict a person’s body fat percentage from an array of variables such as body weight, height, and body measurements around the neck, chest, abdomen, hips, biceps, etc. A variables selection method is used to build a regression model. The final model is shown .
Question: What percentage of the variation in percent body fat remains unexplained, even after introducing weight and abdomen circumference into the model? Also state interpretation of the slope for weight.
An assumption of regression analysis is homoscedasticity, which states that the
residuals exhibit no patterns across values for the dependent variable.
relationship between the independent and dependent variables is linear.
residuals exhibit no patterns across values for the independent variable.
variation of the dependent variable is the same across all values for the independent variable.
Chapter A Solutions
Introductory Statistics (10th Edition)
Ch. A.1 - A. 1 Regarding linear equations in two or more...Ch. A.1 - Fill in the blanks. a. The graph of a linear...Ch. A.1 - Consider a linear equation y = b0 + b1x1 + b2x2. ...Ch. A.1 - Prob. 4ECh. A.1 - Prob. 5ECh. A.1 - Prob. 6ECh. A.1 - Banquet Room Rental. The banquet room at the...Ch. A.1 - Prob. 8ECh. A.1 - In each of Exercises A.9A.12, a. determine the...Ch. A.1 - In each of Exercises A.9A.12, a. determine the...
Ch. A.1 - In each of Exercises A.9A.12, a. determine the...Ch. A.1 - In each of Exercises A.9A.12, a. determine the...Ch. A.1 - Prob. 13ECh. A.1 - Prob. 14ECh. A.1 - Prob. 15ECh. A.1 - In each of Exercises A.13A.22, you are given the...Ch. A.1 - Prob. 17ECh. A.1 - Prob. 18ECh. A.1 - In each of Exercises A.13A.22, you are given the...Ch. A.1 - Prob. 20ECh. A.1 - Prob. 21ECh. A.1 - In each of Exercises A.13A.22, you are given the...Ch. A.1 - In each of Exercises A.23A.30, we have identified...Ch. A.1 - Prob. 24ECh. A.1 - Prob. 25ECh. A.1 - Prob. 26ECh. A.1 - In each of Exercises A.23A.30, we have identified...Ch. A.1 - Prob. 28ECh. A.1 - Prob. 29ECh. A.1 - Prob. 30ECh. A.1 - Why is it often preferable to use more than one...Ch. A.1 - Grade Prediction. The Statistics Department at a...Ch. A.1 - Prob. 33ECh. A.1 - Blood Pressure Medication. A medical researcher...Ch. A.1 - Infant Mortality Rate. A social scientist wants to...Ch. A.2 - Regarding a scatterplot matrix: a. Identify two of...Ch. A.2 - Regarding the criterion used to decide tits a set...Ch. A.2 - Prob. 38ECh. A.2 - Regarding the variables in a multiple linear...Ch. A.2 - Answer true or false to the following statements...Ch. A.2 - In each of Exercises A.41 and A.42, a. construct...Ch. A.2 - In each of Exercises A.41 and A.42, a. construct...Ch. A.2 - Advertising and Sales. A household-appliance...Ch. A.2 - Corvette Prices. The data on age and price for 10...Ch. A.2 - Graduation Kales. Graduation rates and what...Ch. A.2 - Custom Home Resales. Hanna Properties specializes...Ch. A.2 - Advertising and Sales. Refer to Exercise A.43. Use...Ch. A.2 - Prob. 48ECh. A.2 - Graduation Rates. Refer to Exercise A.45. Use the...Ch. A.2 - Custom Home Resales. Refer to Exercise A.46. Use...Ch. A.3 - Fill in the blanks. a. A measure of total...Ch. A.3 - In this section we introduced a descriptive...Ch. A.3 - Suppose x1, x2, and x3 are predictor variables and...Ch. A.3 - State the four conditions required for making...Ch. A.3 - In each of Exercises A.55A.59, assume the...Ch. A.3 - In each of Exercises A.55A.59, assume the...Ch. A.3 - In each of Exercises A.55A.59, assume the...Ch. A.3 - Prob. 58ECh. A.3 - In each of Exercises A.55A.59, assume the...Ch. A.3 - Fill in the blanks. a. When a sum of squares is...Ch. A.3 - Answer true or false to the following statements...Ch. A.3 - For a particular multiple linear regression...Ch. A.3 - For a particular multiple linear regression...Ch. A.3 - Advertising and Sales. Refer to Exercise A.43 on...Ch. A.3 - Corvette Prices. Refer to Exercise A.44 on page...Ch. A.3 - Graduation Rates. Refer to Exercise A.45 on page...Ch. A.3 - Custom Home Resales. Refer to Exercise A.46 on...Ch. A.3 - Advertising and Sales. Refer to Exercise A.43 on...Ch. A.3 - Corvette Prices. Refer to Exercise A.44 on page...Ch. A.3 - Graduation Rates. Refer to Exercise A.45 on page...Ch. A.3 - Custom Home Resales. Refer to Exercise A.46 on...Ch. A.3 - Suppose that R2 = 1 for a data set. What can you...Ch. A.3 - Suppose that R2 = 0 for a data set. What can you...Ch. A.3 - Use the regression identity for multiple linear...Ch. A.4 - Explain why the predictor variables are useless as...Ch. A.4 - Prob. 76ECh. A.4 - What test statistic is used for a hypothesis test...Ch. A.4 - Answer line or false to the following statements...Ch. A.4 - Advertising and Sales. Refer to Exercise A.43 oil...Ch. A.4 - Prob. 80ECh. A.4 - Graduation Rates. Refer to Exercise A.45 on page...Ch. A.4 - Custom-Home Resales. Refer to Exercise A.46 on...Ch. A.4 - Advertising and Sales. Referring to Exercise A.79,...Ch. A.4 - Prob. 84ECh. A.4 - Graduation Rates. Referring to Exercise A.81, use...Ch. A.4 - Prob. 86ECh. A.5 - What two regression inferences did we discuss in...Ch. A.5 - Prob. 88ECh. A.5 - A sample multiple linear regression equation...Ch. A.5 - Answer true or false to the following statements...Ch. A.5 - Advertising and Sales. Refer to Exercise A.43 on...Ch. A.5 - Corvette Prices. Refer to Exercise A.44 on page...Ch. A.5 - Graduation Rates. Refer to Exercise A.45 on page...Ch. A.5 - Custom-Home Resales. Refer to Exercise A.46 on...Ch. A.5 - Advertising and Sales. Referring to Exercise A.91,...Ch. A.5 - Corvette Sales. Referring to Exercise A.92, use...Ch. A.5 - Graduation Rates. Referring to Exercise A.93, use...Ch. A.5 - Custom-Home Resales. Referring to Exercise A.94,...Ch. A.6 - Fill in the blanks. a. In multiple linear...Ch. A.6 - Describe the difference between a residual and a...Ch. A.6 - Fill in the blanks. a. In multiple linear...Ch. A.6 - Answer true or false to the following statements...Ch. A.6 - Prob. 103ECh. A.6 - Corvette Prices. Refer to Exercise A.44 on page...Ch. A.6 - Advertising and Sales. Refer to Exercise A.43 on...Ch. A.6 - Corvette Prices. Refer to Exercise A.44 on page...Ch. A.6 - Graduation Rates. Refer to Exercise A.45 on page...Ch. A.6 - Custom-Homes Resales. Refer to Exercise A.46 on...Ch. A - For a linear equation y = b0 + b1x1 + b2x2 + b3x3,...Ch. A - Consider the linear equation y = 5 + 4x1 3x2. a....Ch. A - Answer true or false to each of the following...Ch. A - What kind of plot is useful for deciding whether...Ch. A - Prob. 5RPCh. A - Prob. 6RPCh. A - Regarding multiple linear regression analysis: a....Ch. A - Prob. 8RPCh. A - For each of the following sums of squares in...Ch. A - Prob. 10RPCh. A - Prob. 11RPCh. A - Suppose x1 and x2 are predictor variables for a...Ch. A - Fill in the blanks. a. The F-statistic for a test...Ch. A - Answer true or false to each of the following...Ch. A - Which interval is wider: (a) the 95% confidence...Ch. A - What plots did we use in this module to decide...Ch. A - Regarding analysis of residuals, decide in each...Ch. A - Annual Income. The Census Bureau collects data on...Ch. A - Annual Income. Refer to Problem 18 and the...Ch. A - Annual Income. Refer to Problem 18, Outputs...Ch. A - Recall from Chapter 1 (page 34 of your text) that...Ch. A - At the beginning of this module on page A-0, we...
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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_forwardCellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forward
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- An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model, where y = cost of drilling the new well (in $thousands) and x = number of feet drilled to create the well. Using data collected for a sample of n=83 wells, the following results were obtained: = 10.5 + 16.20x Give a practical interpretation of the estimate of the slope of the least squares line. An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model, where y =…arrow_forwardIt is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysisarrow_forwardA county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…arrow_forward
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