Table 8: Regression of Weight Loss on Hours of Exercise and Caloric IntalkVariablese b95% CIHours of Exercise0.270.110.05, 0.49Caloric Intake-0.002.0008-0.0035, -0.0004Intercept8.500.737.05, 9.95Note: R? = .35, adj. R? = .35, F = 22.18, df = 2,117; n = 120%3Dswer the following questions about this regression analysis.Which predictors of weight loss are statistically significant, if either?What is the predicted weight loss for someone who consumes 2,000 calorihour per day on average?What is the literal interpretation for the slope of hours of aerobic exercise

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Answered: Which predictors of weight loss are…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 1TI: Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use...

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What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Olympic 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?
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. The decrease in blood pressure y will vary about this subpopulation mean. What is the distribution of y for this subpopulation?
Table 4.12 refers to ratings of agricultural extension agents in North Carolina. In each of five districts, agents were classified by their race and by whether they qualified for a merit pay increase. a. Conduct the Cochran–Mantel–Haenszel test of the hypothesis that the merit pay decision is independent of race, conditional on the district. Interpret. b. Show how you could alternatively test the hypothesis in (a) using a test about a parameter in a logistic regression model. c. What information can you get from a model-based analysis that you do not get from the CMH test?
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25.

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Which predictors of weight loss are statistically significant, if either? What is the predicted weight loss for someone who consumes 2,000 calori hour per day on average? What is the literal interpretation for the slope of hours of aerobic exercise