Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number ofcustomers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysisand the analysis is as follows:Average monthly customer visitsAverage monthly total costsRegression ResultsInterceptb coefficientR²Multiple ChoiceWhat is the percent of the total variance that can be explained by the regression equation? (CMA adapted)OO88.4%29.8%97.9%1,618$ 4,82473.1%$ 1,652$ 2.260.88387

Intercept=$1652b coefficient=$2.26Coefficient of Determination(R2)=0.88387

Answered: Brewsky's is a chain of…

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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Sir Francis Galton, in the late 1800s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of varlables such as the size of parents and the size of their affspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest son. The data are given in tabular form and also displayed in the Figure 1 scatter plot. Also given is the product of the father's height and the son's helght for each of the fifteen pairs. (These products, written in the column labelled "xy", may aid in calculations.) Height of Height of son, father, x y (in centimeters) centimeters) xy (in 192.7 190.6 36,728.62 174.8 179.2 31,324.16 210 158.5 175.3 27,785.05 200 160.9 167.7 26,982.93 180- 171.6 182.0 31,231.2 172.2 171.1 29,463.42 170 179.9 187.9 33,803.21 181.5 177.5 32,216.25 130_…
The Equal Opportunity Commission is investigating questions around unequal pay rates and discriminatory remuneration in various industries. The Pay Equity tab in the excel workbook contains information on 100 employees from a particular industry. Information includes: Salary ($) Gender Years of Education Years of Experience Division of employment Age Run a multiple regression analysis looking at the relationship between salary and years of education and years of experience. What proportion of variation in salary can be explained by these two variables? Conduct a test of the overall significance of the model. Test both the Education and Experience variables separately. Do both contribute to explaining the variation in salaries? Write out the estimated equation and interpret all coefficients. Salary (in $) Education (years) Experience (years) 20860 11 4 30200 16 1 31240 12 1 36860 12 8 44760 14 4 46690 19 3 47400 15 1 47880 14 3 50620 13 6 50690 14 4…
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,000 $ 18,950 February 2,400 $ 22,100 March 1,400 $ 14,050 April 2,600 $ 24,100 May 3,300 $ 28,800 June 2,800 $ 23,100 July 3,600 $ 25,300 August 3,000 $ 23,300 September 1,500 $ 16,600 October 3,200 $ 27,100 November 4,200 $ 32,100 December 3,700 $ 28,300 Summary Output Regression Statistics Multiple R 0.960 R Square 0.921 Adjusted R2 0.913 Standard Error 1,545.17 Observations 12.00 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 7,465.99 1,566.61 4.77 0.00 3,975.37 10,956.62 Machine Hours 5.76 0.53 10.78 0.00 4.57 6.95 If the controller uses regression analysis to estimate costs, the estimate…
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Hello, the attached picture is the background information for the posted question. Thank you! 2e. Calculate the Rsq of the regression and interpret the Rsq.
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The output of a different regression analysis on the effect of radio advertising spending, television advertising spending and online advertising spending on sales is given below. For this study they explored 3 different types of advertising spending (radio, television and online). They modelled it as y = ao + a1¤1 + az¤2 + az¤3. The table below gives the summary output of this regression analysis. Regression Analysis: Sales versus Spend Analysis of Variance Source DF s MS F-Value P-Value Regression d freg 23,986.44 MSR F-val p-val Error dferr 558.52 MSE Total 38 Find the Mean Squares for Regression (MSR) for this problem. Give your answer to 3 decimal places.
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…
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