A phone manufacturer is studying the battery life of a popular product. They gather a random...
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A phone manufacturer is studying the battery life of a popular product. They gather a random sample of customers and ask how many hours the phone typically lasts between charges. The company also records the number of months since the manufacture date of the phone. The company suspects that the typical battery life between charges decreases with the age of the phone. A linear model of battery life (in hours) versus phone age (in months) was created. A computer output and residual plot are provided below. Coefficients: Estimate Std Err t-value (Intercept) 28.181 0.2691 104.7038 phoneAge -0.1564 0.0134 -11.6956 4.00E-10 Residual standard error: 0.5095 on 19 degrees of freedom R-squared: 0.878, Adjusted R-squared: 0.8716 F-statistic: 136.7865 on 1 and 19 DF, p-value: 4.00E-10 Battery Life (hours) 24 $2 10 15 p-value 0 20 25 30 Phone Age (months) Residual DA ezt 1042 064 054 05 -+126 Does a linear model appear to be a good fit for the company's data? Explain. 00 10 Phone Age (monka) a) What is the equation of the best-fit line? Let "X" represent phone age in months and y represent phone battery life in hours. b) Identify the correlation coefficient. Interpret it in context. Correlation coefficient: Interpret: c) Find and interpret the slope of the regression line. Slope: Interpret the slope: d) This data was presented at a company board meeting, and the presenter made the claim: "When our phones are 36 months old, we predict that the battery life will be about 22.6 hours on average." Is this claim justified by the company's data? Explain. Check Answer A phone manufacturer is studying the battery life of a popular product. They gather a random sample of customers and ask how many hours the phone typically lasts between charges. The company also records the number of months since the manufacture date of the phone. The company suspects that the typical battery life between charges decreases with the age of the phone. A linear model of battery life (in hours) versus phone age (in months) was created. A computer output and residual plot are provided below. Coefficients: Estimate Std Err t-value (Intercept) 28.181 0.2691 104.7038 phoneAge -0.1564 0.0134 -11.6956 4.00E-10 Residual standard error: 0.5095 on 19 degrees of freedom R-squared: 0.878, Adjusted R-squared: 0.8716 F-statistic: 136.7865 on 1 and 19 DF, p-value: 4.00E-10 Battery Life (hours) 24 $2 10 15 p-value 0 20 25 30 Phone Age (months) Residual DA ezt 1042 064 054 05 -+126 Does a linear model appear to be a good fit for the company's data? Explain. 00 10 Phone Age (monka) a) What is the equation of the best-fit line? Let "X" represent phone age in months and y represent phone battery life in hours. b) Identify the correlation coefficient. Interpret it in context. Correlation coefficient: Interpret: c) Find and interpret the slope of the regression line. Slope: Interpret the slope: d) This data was presented at a company board meeting, and the presenter made the claim: "When our phones are 36 months old, we predict that the battery life will be about 22.6 hours on average." Is this claim justified by the company's data? Explain. Check Answer
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Related Book For
Probability & Statistics For Engineers & Scientists
ISBN: 9780130415295
7th Edition
Authors: Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying
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