The owners of a franchise of fast food restaurants want to study the relationship between annual revenue (S000) andthe number of chairs in each fast food restaurant. They took a random sample and used EXCEL to create the followingsimple linear regression.Regression StotisticsMultiple RR SquareAdjusted R Square0.5460.2980.285Standard Error5428.348Observations56ANOVAofSSMSSignificance FRegressionResidual1 6.76E+08 6.76E+0822.9311.34807E-0554 1.59E+09 29466959Total55 2.27E+09Coefficients andard Emi tStatP.valueLower 95%Upper 95%Intercept8183.151 2265.2693.612 0.0006657333641.559 12724.743chairs109.74022.9174.789 1.34807E-0563.795155.686a) Write down the regression equation.b) Interpret the correlation coefficient.C) Interpret the slope.

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Answered: The owners of a franchise of fast food…

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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Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom
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An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?

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