7) Below is a multiple regression in which the dependent variable is market value of houses andthe independent variables are the age of the house and square footage of the house. Theregression was estimated for 42 houses.SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotaldf239410.7454950.5557620.5329817211.84842SS253765017120284195914566069762Coefficients StandardErrorMS1.27E+0952010759F24.39544Significance F1.3443E-07Upper 95%t StatP-value Lower 95%Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.36608357A. What is the estimated regression equation for determining the market value of houses?7B. Discuss tests of significance of the regression7C. What percentage of the variation in the dependent variable, Market Value, is explained bythe regression model?75415.0958403.24374454.45605347D. If the age of a house increases by 1 year given that the square feet is held constant, what isthe impact on the house's market value?

Answered: 7) Below is a multiple regression in…

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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A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1= male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %3D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 The level of significance is 0.05. In the regression model, which of the following are dummy variables?
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 +4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between 4.028✔ dollars and 4.753 x dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
Describe a hypothetical study for which multiple regression with more than two predictor variables would be an appropriate analysis. Your description should include one dependent variable and three or more predictors.
Use the summary statistics to calculate the regression line for this data. Explain each coefficient. What is the total amount of weight would you expect a weightlifter with a body weight of 84kg to lift? At the 5% significance level, is there evidence that the body weight of the athlete is a useful predictor for the total amount an athlete can lift? Assume the assumptions can be verified.
Describe the difference between the visually fitted and the calculated regression lines you placed on the scatterplot. Describe any discrepancy in the scatterplot of the raw data, and how it might explain the difference between your fitted lines. Does the difference between the visual fit and the regression fit reveal a flaw or a strength in the regression prediction, and why?
What is the difference between a Multiple Regression model and a Multivariate Regression model? Suppose a researcher wants to predict the probability of a patient being diagnosed with breast cancer given their be used? age, family history, and smoking status. What type of regression model should alsboin sisiurviluM bae alqiluM alm?
What is the differed annual expenditures of two families if their annual net incomes are differed by 2000? The computed regression line has a value of a=4.32 and b=2.12.
See the attached image for the introduction. Question: Fill in a blank ANOVA table.
Predicting Percent Body FatThis problem uses the dataset BodyFat, which gives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (in pounds), Height (in inches), and circumference (in cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist.1Using Neck Circumference to Predict Body FatThe regression line for predicting body fat percent using neck circumference is BodyFat^=-47.9+1.75(Neck). Click here for the dataset associated with this question. (a) What body fat percent does the line predict for a person with a neck circumference of 35 cm?Round your answer to two decimal places.BodyFat^= %What body fat percent does the line predict for a person with a neck circumference of 40 cm?Round your answer to one decimal place.BodyFat^= % (b) One of the men in the study had a neck circumference of 38.7 cm and a body fat percent of 11.3. Find the residual for this man.Round your answer to three decimal places.residual= %…
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The coefficient of correlation in a simple regression analysis is = -0.6. The coefficient of determination for this regression would be 0.36 - 0.36 0.6 0.13 O 0.6 or + 0.6
In a multiple regression analysis involving an intercept, 15 independent variables and 200 observations, Total SS = 800 and Residual SS = 240. Adjusted R-squared is

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