QUESTION 7Consider the following regression model and corresponding output for a dataset with n=104 observations:y=₁+0+0+ 0x₂ +*VariableX2x3X4Std. Error-0.012 0.0060.596 0.0140.678 1.074Constant 8.860 1.766t Pr|t|-2.289 0.02241.139 0.0005.017 0.000What is the upper value of the 99% confidence interval for ? That is, if confidence interval is (a,b), what is the value of b? (approximate at least to 4 decimal places)

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Answered: QUESTION 7 Consider the following…

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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The following estimated regression equation based on 10 observations was presented. ŷ = 27.1270 +0.5104x₁ + 0.4980x₂ 1 Here, SST = 6,736.125, SSR = 6,222.375, 5b₁ = 0.0813, and = 0.0567. 562
Consider the following bivariate data: 6. 6. y -8 7 If a linear regression were fit to the data, how many residuals would there be? 4 Impossible to tell without more information
The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significant
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The image contains two graphs typical of the analysis of a simple linear regression model. It can be stated that: a. The variance of the errors seems constant b. The errors do not come from a normal distribution c. The linear model does not seem appropriate d. The residuals are close to the line and therefore there is normality in the data. Why? Thanks
1. Question 1: The Public Utility Commission is interested in describing the relationship between household monthly utility bills and the size of the house. A recent study of 30 randomly selected household resulted in the following regression results: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.149769088 0.02243078 -0.012482407 16.72762259 Observations 30 ANOVA df SS MS 1 179.7725274 179.7725 0.642473 Regression Residual 28 7834.774007 279.8134 Total 29 8014.546534 t Stat 13.44691911 4.941135 3.26E-05 0.006560753 0.801544 0.429567 P-value Coefficients 66.44304169 0.005258733 Standard Eror Intercept Square Feet (a) Based on the information provided, indicate what, if any, conclusions can be reached about the relationship between utility bill and the size of the house in square feet. (b) Construct a 95% confidence interval for the true regression coefficient of Square Feet. How do you interpret this confidence interval? (c) What is the…
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 - 102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error 95.65 17.99 10.89 19.98 t Stat P-value 9.754 0.0000 9.335 0.0000 -9.424 0.0000 -9.969 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Coefficients Coefficients Standard Error t Stat P-value Intercept 956 97.23 9.832 0.0000 Car Accidents 172.08 18.24 9.434 0.0000 (In last 3 years) Credit Score Safety Rating 105.16 201.03 0.523 0.6030 -207.81 20.46 - 10.157 0.0000 Does the sign of the coefficient for the variable credit score make sense? Answer No, because it is expected that as the credit score increases then the cost should decrease. ○ No, because it is expected that as the credit score increases then the cost should also increase. ○ Yes, because it is expected that as the credit score increases then the cost should decrease. ○ Yes, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts
Consider the following information about variable X: Mean = 4, standard deviation = 2. Consider the following information about variable Y: Mean = 5, standard deviation = 3. If there is a correlation of r = 0.52 between variable X and variable Y, what is the intercept for a simple linear regression equation predicting Y on the basis of X? Please provide your answer as a raw score (not a z score) with a minimum of two decimal places.
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.
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805

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