QUESTION 5The following is the ANOVA summary table for a multiple regression model with two independent variables:Analysis of VarianceSourceDFRegression 21820ErrorTotalAdj SS60120180Adj MSF-ValueP-ValueIf SSR(x1) = 45 and SSR(x2) = 25a) To test whether the model is a significant at the 0.05 level of significance.1. th test statistic =2. the critical value =3. the model is significant(yes or no)?b) At the 0.05 level of significance, to test whether the independent variable x1 makes a significant contribution to the regression model.1. th test statistic =2. the critical value =3. the model is significant(yes or no)?c) Compute the coefficients of partial determination,r² r, 1,2r²y,2,1

The ANOVA table is: Source DF SS MS F-value P-value Regression 2 60 60/2 = 30 30/6.667 = 4.50…

Answered: a) To test whether the model is a…

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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4. A study was conducted to investigate the relationship between the size of a house (in square feet) and the selling price of a house (in dollars). The response variable is price in dollars, and we want to study if the covariate of the square footage helps explain the response. A random sample of 522 houses was used, and the linear regression output from R is below. price sqft, data = house) 1m (formula = Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -81432.946 11551.846 -7.049 5.74e-12 *** sqft 158.950 4.875 32.605 <2e-16 *** Residual standard error: 79120 on 520 degrees of freedom Multiple R-squared: 0.6715, Adjusted R-squared: 0.6709 a. Write out the estimated linear equation. What is the estimated expected selling price of a house that is 2000 square feet? b. Does the intercept have a useful interpretation in this study? Why or why not. c. Interpret the slope estimate in context of the model.
1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o
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The individual residual scores from a sample of participants regarding the difference between the predicted Y values from a regression equation and the actual Y from the data are provided here. Y - Ŷ = 3,8,1,2,2. What is the value for the standard error of estimate?
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
1. If we choose the significance level α=0.05, is Gender a significant factor in explaining Salary? Explain. 2. Use Excel’s Data Analysis tool to conduct a multiple regression, using Salary as the dependent variable, and all the rest of variables as independent variables.(Choose confidence level at 95 %.) 3. Based on the multiple regression analysis output, write the estimated regression equation. (Keep 2 decimal places) Salary Gender Years Of Service Years Of Education 52,100 0 5 13 55,015 0 20 13 55,520 0 23 12 43,430 0 7 12 43,813 0 5 12 51,903 0 6 12 54,580 0 24 14 54,913 0 18 12 52,100 0 29 12 50,693 0 14 12 47,878 0 10 12 46,555 0 6 14 47,878 0 7 16 47,878 0 9 12 54,913 0 16 13 50,123 0 4 13 50,693 0 20 12 48,933 0 29 12 58,270 0 12 12 50,538 0 5 13 48,933 0 12 12 45,078 0 5 12 47,878 0 5 14 53,508 0 24 12 42,250 0 1 16 53,508 1 8 15 42,250 1 3 15 47,808 1 1 13 50,180 1 11 13 44,163 1 9 10 41,718 1 3 10 47,878…
What is the purpose of testing whether B, = 0? O The purpose of testing whether 8, = 0 is to determine whether or not the mean of the x values is equal to the mean of the y values. O The purpose of testing whether 8, = 0 is to determine whether or not the regression line provides a good fit for the data. O The purpose of testing whether 8, = 0 is to determine whether or not there is a cause-and-effect relationship between x and y. O The purpose of testing whether 8, = 0 is to determine whether or not there is a significant relationship between x and y. If we reject 8, = 0, does it imply a good fit? O Rejecting 8, = 0 does not necessarily imply a good fit. For example, if 8, = 0 is rejected and r2 is high, there is a statistically significant relationship between x and y but the fit is not very good. O Rejecting 8, = 0 always implies a good fit. If 8, = 0 is rejected, there is a statistically significant relationship between x and y which always implies a good fit. O Rejecting 8, = 0…

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