Question:1) Using test data on 20 types of laundry detergent, an analyst fitted a regressionto predict Cost PerLoad (average cost per load in cents per load) using binary predictorsTopLoad (1 if washer is a top-loading model, 0 otherwise) and Powder (if detergent was inpowder form, 0 otherwise). Interpret the results. Give 10 points.R²Adjusted R²RStd. ErrorANOVA tableSourceRegressionResidualTotalRegression outputvariablesInterceptTop-LoadPowder0.1170.0060.3415.915coefficients26.0000-6.3000-0.2714SS73.8699559.8143633.6842 18n 19k 2Dep. Var. Cost Per Loaddf MS2 36.935016 34.9884std. error t (df = 16)4.18266.2164.5818-1.3752.9150-0.093p-value1.23E-05.1881.9270F1.06confidence interval95% lowerp-value.371017.1333-16.0130-6.450995% upper34.86673.41305.9081

In this problem we have to intepret the results by analyzing the output of a multiple regression…

Answered: Question:1) Using test data on 20 types…

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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Mike's Stables has two different possible cost drivers (trainer hours and number of horses) for budgeting the cost of horses training. The most recent results of the two regression analysis are as follows: Trainer's hours: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.56 Coefficients t Stat Intercept 1,004.65 4.61 X Variable 1 $22.99 7.11 Number of horses: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.63 Coefficients t Stat Intercept $5,240.95 4.44 X Variable 1 $951.48 3.50 What is the estimated total training horses cost for the coming year if 15,000 trainer hours are incurred and the stable has 350 horses to be trained, based upon the best cost driver?
The difference between a regression weight and a beta weight is: A regression weight assumes linearity. A beta weight is for the population while a regression weight is for the sample. A regression weight is less biased. A beta weight is a standardized regression weight.
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A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lr
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When using population size as the explanatory variable, x, and broadband subscribers as the response variable, y, for data on the number of individuals in a country with broadband access and the population size for 36 nations, the regression equation is y = 4,975,098 +0.0342x. a. Interpret the slope of the regression equation. Is the association positive or negative? Explain what this means. b. Predict broadband subscribers at the (i) population size 7,014,655, (ii) population size 1,155,173,053. c. For one nation, y = 71,110,000, and x = 322,413,902. Find the predicted broadband use and the residual for this nation. Interpret the value of this residual. a. Since the association is positive, the slope means that as the (Type an integer or a decimal.) b. (i) The predicted broadband subscribers for population size 7,014,655 is (Round to the nearest whole number as needed.) population size increases by 1 unit, the number of broadband subscribers tends to increase by 0.0342.

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