The following data represent the commute time (in minutes) x and a score on a well-being survey y. The equation of the least-squaresregression line is y = -0.0480x + 69.1570 and the standard error of the estimate is 0.3036. Complete parts (a) through (e) below.Xy40 5577 110 D10 20 3069.0 68.1 67.5 67.2 66.2 65.9 63.8(a) Predict the mean well-being index composite score of all individuals whose commute time is 35 minutes.y=(Round to two decimal places as needed.)(b) Construct a 90% confidence interval for the mean well-being index composite score of all individuals whose commute time is 35 minutes.Lower BoundUpper Bound...(Round to two decimal places as needed.)(Round to two decimal places as needed.)(c) Predict the well-being index composite score of Jane, whose commute time is 35 minutes.y=(Round to two decimal places as needed.) d) Construct a 90% prediction interval for the well-being index composite score of Jane, whose commute time is 35 minutes.Lower Bound(Round to two decimal places as needed.)(Round to two decimal places as needed.)Upper Bound(e) What is the difference between the predictions made in parts (a) and (c)?=OA. The prediction made in part (a) is an estimate of the well-being score for the individual, Jane, whose commute is 35 minutes, aprediction made in part (c) is an estimate of the mean well-being score for all individuals whose commute is 35 minutes.OB. The prediction made in part (a) is an estimate of the mean well-being score for all individuals whose commute is 35 minutes, andprediction made in part (c) is an estimate of the well-being score for the individual, Jane, whose commute is 35 minutes.OC. The prediction made in part (a) is the average well-being score for all individuals whose commute is 67.48 minutes, and theprediction made in part (c) is an estimate of the well-being score for the individual, Jane, whose commute is 67.48 minutes.

Answered: The following data represent the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 2.66 ** Bedrooms 2306 0.33 74.3 -4.36 House Size Lot Size S-25023 Error Total R-Sq-56.0 % 96 99 14177 6994 52.98 17.02 0.742 0.164 -0.26 0.798 Source DE SS MS F Regression 3 76501718347 25500572782 *** Residual 60109046053 626135896 R-Sq (adj) -54.6% P d) Is y significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypoteses. f) Give an interpretation to the term “R-sq” and comment on its value.
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
what % of the variation is ( height, or head circumference) explained by the least-squares regression model. (Round to one decimal place as needed.)
I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 43 37 15 19 24 37 18 y 72 67 25 29 42 65 32 Send data to Excel Regression line equation: y = 1.7777x + 1.5838
Report the equation of the regression line and interpret it in the context of the problem
Use the given data to find the scatter plot, equation of the regression line, and prediction. 4) Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance 59 63 65 69 58 77 76 | 69 70 64 Attitude 72 67 78 82 75 87 92 83 87 78 What function do you use to see the scatterplot on the calculator. Scatterplot from the calculator:
Draw a graph of the least-squares regression line on your scatterplot. (For hand-drawing, round the slope and y-intercept to one decimal place before drawing the line.) Be sure to show how you were able to plot the line starting with its equation. Model City Miles per Gallon Highway Miles per Gallon Acura RLX 20 29 BMW 530i 24 34 Buick LaCrosse eAssist 25 35 Chevrolet Malibu 29 36 Ford Hybrid FWD 43 41 Honda Civic 32 42 Infiniti Q50 Red Sport 20 26 Kia Forte 30 40 Lexus ES 350 22 33 Mercedes Benz AMG S 21 30 Mini Cooper Clubman 24 32 Nissan Maxima 20 30 Suburu Legacy AWD 25 34 Toyota Prius ECO 58 53
4
Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footaget) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq (adj) =54.6% Source DF MS F P Regression Residual 76501718347 25500572782 *** **** Error 96 60109046053 626135896 Total 99 d) Is y significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. f) Give an interpretation to the term “R-sq" and comment on its value.

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