SUMMARY OUTPUT Regression Statistics Multiple R 0.69406672 R Square Adjusted R Square Standard Error Observations 0.481728612 0.476440129 3.936905277 100 ANOVA Regression Residual Total df SS MS F 1 1411.826131 1411.82613 91.09012 Significance F 1.18186E-15 98 99 1518.923869 2930.75 15.4992232 Coefficients Standard Error Intercept Father's Age (Death) 29.28584902 0.627659604 4.539696555 t Stat P-value 6.45105872 4.23E-09 0.065764048 9.54411453 1.18E-15 Lower 95% Upper 95% 20.27696896 38.29472909 0.497152992 0.758166217 Lower 95.0% Upper 95.0% 20.27696896 0.497152992 38.29472909 0.758166217 Customer's Age (Death) Excel was used to run a simple linear regression analysis. Excel output and diagnostic plots follow for your reference in answering the remaining questions. 15 Father's Age (Death) Residual Plot Residuals -10 -15 10 10 5 0 40 50 80 90 100 Father's Age (Death) 100 90 80 Normal Probability Plot 70 50 40 30 20 10 0 0 20 40 60 80 100 120 Sample Percentile

SUMMARY OUTPUT Regression Statistics Multiple R 0.69406672 R Square Adjusted R Square Standard Error Observations 0.481728612 0.476440129 3.936905277 100 ANOVA Regression Residual Total df SS MS F 1 1411.826131 1411.82613 91.09012 Significance F 1.18186E-15 98 99 1518.923869 2930.75 15.4992232 Coefficients Standard Error Intercept Father's Age (Death) 29.28584902 0.627659604 4.539696555 t Stat P-value 6.45105872 4.23E-09 0.065764048 9.54411453 1.18E-15 Lower 95% Upper 95% 20.27696896 38.29472909 0.497152992 0.758166217 Lower 95.0% Upper 95.0% 20.27696896 0.497152992 38.29472909 0.758166217 Customer's Age (Death) Excel was used to run a simple linear regression analysis. Excel output and diagnostic plots follow for your reference in answering the remaining questions. 15 Father's Age (Death) Residual Plot Residuals -10 -15 10 10 5 0 40 50 80 90 100 Father's Age (Death) 100 90 80 Normal Probability Plot 70 50 40 30 20 10 0 0 20 40 60 80 100 120 Sample Percentile

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 43SGR

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Find the mean hourly cost when the cell phone described above is used for 240 minutes.
Noise and Intelligibility Audiologists study the intelligibility of spoken sentences under different noise levels. Intelligibility, the MRT score, is measured as the percent of a spoken sentence that the listener can decipher at a cesl4ain noise level in decibels (dB). The table shows the results of one such test. (a) Make a scatter plot of the data. (b) Find and graph the regression line. (c) Find the correlation coefficient. Is a linear model appropriate? (d) Use the linear model in put (b) to estimate the intelligibility of a sentence at a 94-dB noise level.
Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
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?