Consider the following SAS output.Regression Y on X3, X₂, and X₁SourceModelErrorCorrected TotalDF31619ANALYSIS OF VARIANCESum ofSquares1518.14494337.057061855.20200MeanSquare506.0483121.06607Root MSEDependent MeanCoeff VarF Value24.02Pr> F<.00014.5897820.5700022.31297Calculate the value of R₁x₁x2x3 and interpret it.Calculate the value of the sample multiple correlation coefficient and interpret it.

Answered: Image /qna-images/answer/c0fbb7c3-c0f6-4750-9b94-89b1f4b9f288.jpg

Answered: Consider the following SAS output.…

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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Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis? (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0: Ha: Find the value of the test statistic. (Round your answer to three decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = What is your conclusion? .
After you estimate a simple linear regression you obtain the following sample regression function: Y₁ = 8.8 +0.7091 Xį With an r² of 0.784. The observed dependent variable used for the regression is: Y 9 675 40 40 40 6 5 5 5 5 1 1 Compute the sample variance of X? 8.45 10.79 9.17 7.19 Cannot be computed with the provided information.
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer termina Analysis of Variance SOURCE Regression Error Total Predictor Constant X DF Adj SS 1 1575.76 8 349.14 9 1924.90 Regression Equation Y = 6.1092 +0.8951 X O Ho: B₁ * 0 H₂: B₁ = 0 Coef SE Coef 0.9361 0.1490 (a) Write the estimated regression equation. ý =| 6.1092+ 0.8951r O Ho: B₁ 20 H₂: B₁ <0 |0 Ho: Boo Hà Bo=0 |0 Ho: Bo=0 = 0 Ha: Bo #0 6.1092 0.8951 Ho: B₁ = 0 H₂: B₁ * 0 (b) Use a t test to determine whether monthly maintenance expense (dollars per month) is related to usage (hours per week) at the 0.05 level of significance. State the null and alternative hypotheses. Adj MS 1575.76 43.64 Find the value of the test statistic. (Round your answer to two decimal places.) 36.11 X
Sarah has some data and wants to run a linear regression model on it. Before she runs the test, she knows she needs to check to make sure all conditions are met. Based only on the plots below, what condition is not met? Data Scatterplot Normal Probability Plot Residual Scatterplot 25 100 Regression Standardoed Predcted Vale Observed Cum Prob Linearity O Normality Equal Variances O Independence O The plots do not show a problem with any of the listed conditions.
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =
1. Test whether the following expression is a valid CDF. The range of validity of the variable is 0 to ∞. If it is a valid CDF, obtain the corresponding pdf and its mean. If it is not a valid CDF, test whether it is a valid pdf? If it is a valid pdf, obtain the corresponding CDF and the mean. 2x x² A(x)-[1-exp(-) ++ [¹-exp(-²3)] -+-)] = exp 5 16 x² 8
Which of the following statements is the CORRECT expression in APA format of the SPSS simple linear regression results provided below? Model Summary Adjusted R Square R R Square 817 .668 627 a. Predictors: (Constant), Experience years (year) ANOVA Regression Residual Sum of Squares 92.844 46.118 138.962 df Std. Error of the Estimate 2.401 1 Total a. Dependent Variable: Income (10 million VND/month) b. Predictors: (Constant), Experience years (year) Mean Square 92.844 5.765 Coefficients Unstandardized Coefficients B Std. Error (Constant) -.651 Experience years (year) 7856642.174 37841636.85 a. Dependent Variable: Income (10 million VND/month) .162 Standardized Coefficients Beta -.817 F 16.105 -4.013 .208 Sig. 004 Sig. 004 .841 A regression analysis was conducted with Income as criterion variable and Experience in Years as the predictor. Experience in Years was NOT a significant predictor of Income, 3= -0.82, t(9)= -4.013, p>0.05, and accounted for 66.80% of the variance in Income…
Given the Analysis of Variance (ANOVA) for the simple linear regression as below: Table Q5: ANOVA Source df MS F Regression 1 252378 E Error A 5482 D Total 24 в (a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.
Below is the output from regression. Use the p-value and decide if the equation is good to use for forecasting.

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