Last year, TimeWise conducted a study that aimed at predicting the waiting time (in minutes) at thecheckout line of the Ranch 99 Supermarket in South Jakarta based on the number of customers in oneparticular month. Using the simple regression method on a dataset with a sample size of 30, theregression output tables are given below:SUMMARY OUTPUTRegression StatisticsMultiple R0.88728417R Square0.7872732Adjusted R Square 0.77967582Standard Error0.59521532Observations30ANOVAMSSignificance FRegression36.712124136.7121241 103.62422716.48782E-11Residual289.919875920.35428128Total2946.632StandardCoefficientsErrort StatP-valueLower 95%Upper 95%Intercept-0.44797910.2782914-1.60974840.118670876 -1.018033233 0.12207495Customers0.128471880.0126205310.1795986 6.48782E-11 0.102619905 0.15432385a. Determine the simple regression model to predict the waiting timeb. Interpret the meaning of the regression coefficient of the independent variablec. What is the coefficient of determination? Interpret its meaning.d. Do you think this regression model is useful in predicting the waiting time at the Farmers'Market?e. On a particular day with 37 customers coming into the supermarket, what is the estimatedaverage waiting time in the check-out line?

It is given that Y-intercept = -0.4479791 Coefficient of customers = 0.12847188 Note : According to…

Answered: Determine the simple regression model…

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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Refrigerator prices are affected by characteristics such as whether or not the refrigerator is on sale, whether or not it is listed as a Sub-Zero brand, the number of doors (one door or two doors), and the placement of the freezer compartment (top, side, or bottom). The table below shows the regression output from a regression model using the natural log of price as the dependent variable. The model was developed by the Bureau of Labor Statistics. Variable Coefficient Standard Error t Statistic Intercept 5.3500 0.12169 43.96 Sale price −0.0787 0.0235 −3.35 Sub-Zero brand 1.0646 0.1468 7.25 Total capacity (in cubic ft) 0.07027 0.00472 14.89 Two-door, freezer on bottom 0.04871 0.08926 0.55 Two-door, side freezer Base Two-door, freezer on top −0.4226 0.03710 −11.39 One door with freezer −0.6730 0.1319 −5.10 One door, no freezer −0.8520 0.1358 −6.27 (a) Write the…
Based on the regression equation, we can Multiple Choice measure cause and effect predict the value of the independent variable given a value of the dependent variable measure the association between two variables predict the value of the dependent variable given a value of the independent variable
Example The marketing manager of Cadbury wants to test the effect of price on sales. Regression model: Sales = β0+ β1Price + error 1) In addition to price, the marketing researcher would like to find out whether there is a systematic trend in the sales. In other words, are sales increasing or decreasing over time? In order to test whether there is a systematic time trend in sales, in addition to the effects of price, please: Tip: the dependent variable is still sales. The independent variables would include price and weeknr (to capture time trend). Write the regression model for question 1 use the examle above to guide you?
Refrigerator prices are affected by characteristics such as whether or not the refrigerator is on sale, whether or not it is listed as a Sub-Zero brand, the number of doors (one door or two doors), and the placement of the freezer compartment (top, side, or bottom). The table below shows the regression output from a regression model using the natural log of price as the dependent variable. The model was developed by the Bureau of Labor Statistics. Variable Coefficient Standard Error t Statistic Intercept 5.4000 0.13415 40.25 Sale price −0.0759 0.0283 −2.68 Sub-Zero brand 1.1471 0.1459 7.86 Total capacity (in cubic feet) 0.06929 0.00595 11.64 Two-door, freezer on bottom 0.05567 0.07292 0.76 Two-door, side freezer Base Two-door, freezer on top −0.3352 0.03537 −9.48 One door with freezer −0.6144 0.1362 −4.51 One door, no freezer −0.8573 0.1491 −5.75 Find the p-value for each coefficient, using 315 degrees of freedom. Using an α = .10, which predictor…
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What is a residual? Explain when a residual is positive, negative, and zero. OA. A residual is the sum of the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is below the line, negative when it is above the line, and zero when the observed y-value equals the predicted y-value. OB. A residual is the difference between the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is above the line, negative when it is below the line, and zero when the observed y-value equals the predicted y-value. OC. A residual is the difference between the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is below the line, negative when it is above the line, and zero when the observed…
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Determine the simple regression model to predict the waiting time Interpret the meaning of the regression coefficient of the independent variable What is the coefficient of determination? Interpret its meaning.