A researcher developed a regression model to predict the tear rating of a bag of coffee based on the plate gap in bag-sealing equipment. Data were collected on 28 bags in which the plate gap was varied. An analysis of variance from theregression showed that b, = 0.7909 and Sp.= 0.2294.a. At the 0.05 level of significance, is there evidence of a linear relationship between the plate gap of the bag-sealing machine and the tear rating of a bag of coffee?b. Construct a 95% confidence interval estimate of the population slope, B1......

Answered: A researcher developed a regression…

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015

1st Edition

ISBN:9781680331141

Author:HOUGHTON MIFFLIN HARCOURT

Publisher:HOUGHTON MIFFLIN HARCOURT

Chapter4: Writing Linear Equations

Section: Chapter Questions

Problem 14CR

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1. Which one can describe the regression estimation? A) Predict the mean value of the dependent variable bascd on valucs of the cxplanatory variables. B) Predict the mcdian valuc of the dependent variable based on valucs of the explanatory variables. C) Predict the mcan valuc of the independent variables on the basis of the dependent variable. D) Predict the median value of the independent variables on the basis of the dependent variable.
A random sample of records of electricity usage of homes gives the amount of electricity used and size (in square feet) of 135 homes. A simple linear regression to predict the amount of electricity used (in kilowatt-hours) based on size has an * = 0.71. Assume that a linear model is appropriate. Interpret r. O A. The prediction error using the regression line to predict electricity use is 71% smaller than the prediction error using y to predict it. B. The prediction error for predicting electricity use is about the same when using the regression line and y. C. The prediction error using the regression line to predict electricity use is 29% larger than the prediction error using y to predict it. D. The prediction error using the regression line to predict electricity use is 71% larger than the prediction error using y to predict it. O E. The prediction error using the regression line to predict electricity use is 29% smaller than the prediction error using y to predict it.
A sample of 40 individuals collects their shoe size and the height (cm) for CSI data. The scatter plot and results from a simple linear regression are recorded: HEIGHT = 126.76238 + 4.8782178 SHOE SIZE Sample size: 40R (correlation coefficient) = 0.77918855P-value < 0.0001 (a) Does the scatterplot and results show a linear correlation between shoe size and height? (b) How can you tell? (c) Will it be appropriate to use the linear regression equation given in the output? (d) Krusty the Clown wears size 8.5 shoes. According to the linear regression equation, how tall is Krusty the Clown? (e) Sideshow Bob wears size 14 shoes. According to the linear regression equation, how tall is Sideshow Bob?
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"+ describes a study to investigate the effect of frying time (in seconds) and acrylamide concentration (in micrograms per kilogram) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Acrylamide Time Concentration 150 240 240 270 300 300 150 + 115 190 180 145 275 (a) Find the equation of the least-squares line for predicting acrylamide concentration using frying time. (Round your answers to four decimal places.) ŷ = (b) Does the equation of the least-squares line support the conclusion that longer frying times tend to be paired with higher acrylamide concentrations? Explain. No, the least squares regression line equation…
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"+ describes a study to investigate the effect of frying time (in seconds) and acrylamide concentration (in micrograms per kilogram) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Acrylamide Time Concentration 150 240 240 270 300 300 150 125 + 195 185 135 275 USE SALT (a) Find the equation of the least-squares line for predicting acrylamide concentration using frying time. (Round your answers to four decimal places.) ŷ = (b) Does the equation of the least-squares line support the conclusion that longer frying times tend to be paired with higher acrylamide concentrations? Explain. O No, the least squares regression line…
The administration of a midwestern university commissioned a salary equity study to help establish benchmarks for faculty salaries. The administration utilized the following regression model for annual salary, y : ?(?) β0+β1x ,where ?=0 if lecturer, 1 if assistant professor, 2 if associate professor, and 3 if full professor. The administration wanted to use the model to compare the mean salaries of professors in the different ranks. a) Explain the flaw in the model. b)Propose an alternative model that will achieve the administration’s objective. c) If the global F-test for the model you proposed in 2 is conducted, what would be the value of the numerator degrees of freedom?
The accompanying table lists overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for an overhead width of 8.9 cm using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Click the icon to view the seal data. a. The explained variation is (Round to the nearest integer as needed.) b. The unexplained variation is. (Round to the nearest integer as needed.) c. The 99% prediction interval for an overhead width of 8.9 cm is kg
The accompanying table lists overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for an overhead width of 9.2 cm using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Click the icon to view the seal data. a. The explained variation is (Round to the nearest integer as needed.) b. The unexplained variation is (Round to the nearest integer as needed.) c. The 99% prediction interval for an overhead width of 9.2 cm is ☐ kg
In a sample of cars reviewed by Motor Trend magazine, the mean horsepower (hp) was 150 hp with a standard deviation of 36 hp. The mean weight (lbs) was 2500 lbs with a standard deviation of 720 lbs. Assume the relationship between weight and horsepower is linear and has a correlation of r = +0.55. What is the slope of the linear regression model predicting weight (y-variable) from horsepower (x-variable)? using the value of the slope you found, now compute the intercept of the linear regression model predicting weight (y-variable) from horsepower (x-variable). 850 550 1150 250
The accompanying table lists systolic blood pressures (mm Hg) and diastolic blood pressures (mm Hg) of adult females. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for a systolic blood pressure of 120 mm Hg using a 95% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Click the icon to view the blood pressure data. a. The explained variation is (Round to two decimal places as needed.) C... Blood Pressures Systolic 125 106 128 108 157 97 154 111 123 114 104 128 Print Diastolic 71 67 74 64 73 52 91 69 67 74 61 67 Done X
The accompanying table lists systolic blood pressures (mm Hg) and diastolic blood pressures (mm Hg) of adult females. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for a systolic blood pressure of 118 mm Hg using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Systolic Diastolic 127 69 103 65 130 73 104 64 157 74 97 51 155 90 111 69 124 68 112 75 103 60 128 67
4. To assess the effect of state right to work laws on union membership (which do not require membership in unions as a precondition for employment), the following regression results were obtained from the data for 50 states in the United States for 1982: PVT, = 19.8066 – 9.3917 RTW, t= (17.0352) (-5.1086) R = 0.3522 where PVT = percentage of private sector employees in unions in 1982 RTW = 1 if right-to-work law exists, 0 otherwise (in 1982, 20 states had right- to-work laws). %3D A priori, what is the expected relationship between PVT and RTW? Do the regression results support this expected relationship? Interpret the regression results. What was the average percent of private sector employees in unions in the states that did not have the right-to-work laws?

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