Q2: Three regressions are fitted. The corresponding least Square estimatesand MSE are given below. The numbers in parentheses are correspondingstandard errorsY regressed onBMSEB₂B3X₁, X2, X3, X₁ X₂135 8.752.51.2531.25(1.98)(1.98)(1.98)(1.98)X1, X₂1358.752.527.5(1.85) (1.85)(1.85)X₁1358.7531.25(1.98)(1.98)If the adjusted R² is used to select the optimal set of predictors from the three candidatesX1,(X1, X2), (X1, X2, X3)1- Which candidate (s) would be selected? Please explain briefly.2- Which of the three candidates in part (1) has or have the largest R²3- Do you think there is a multicollinearity? Please explain briefly.Bo

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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 least squares regression was used to estimate the relationship between the dollar price P of 45 used 2001 Audi A4 2.8L sedans with manual transmission and the number of miles M the car had been driven: R = 0.70 P = 16,958 – 0.0677M, (2,553) (0.0233) The standard errors are in parentheses. a. Does the value 16,958 seem reasonable? b. Does the value –0.0677 seem reasonable? c. Does the value 0.70 seem reasonable? d. Is the estimated relationship between M and P statistically significant at the 5 percent level? e. Should the riables be reversed, wi M on the left-hand side and P on the right- hand side? Why or why not?
Use the given information about sums of squares and sample size for a linear model to fill in all values in the analysis of variance for regression table below. SSModel = 250 with SSTotal = 3000 and a sample size of n = Round your answer for the p-value to four decimal places, and all other answers to three decimal places, if necessary. Source Model Error Total i df 100. SS MS F-statistic i p-value
Find the least-squares regression line treating square footage as the explanatory variable. y = (Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
A recent experiment included measuring the weight of a package of raw almonds. This type of data would be considered a: Discrete variable or Continuous variable?
Which one of the following numbers for the standard error of estimate would indicate the best regression fit? 100 50 1000 10
x1 x2 x3 x4 x5 x6 bond width on bond pull strength loop height wire post height observation die height width on length the die the post 8.0 5.2 17.0 28.6 83.0 1.9 1.6 2. 8.0 5.2 19.6 29.6 94.9 2.1 2,3 3 8.3 5.8 19.8 32.4 89.7 2.1 1.8 4. 8.5 6.4 19.6 31.0 96.2 2.0 2.0 8.8 5.8 19.4 32.4 95.6 2.2 2.1 6 9.0 5.2 18.6 28.6 86.5 2.0 1.8 7 9.3 5.6 18.8 30.6 84.5 2.1 2.1 9.3 6.0 20.4 32.4 88.8 2.2 1.9 9.5 5.2 19.0 32.6 85.7 2.1 1.9 10 9.8 5.8 20.8 32.2 93.6 2.3 2.1 11 10.0 6.4 19.9 31.8 86.0 2.1 1.8 12 10.3 б.0 18.0 32.6 87.1 2.0 1.6 13 10.5 6.2 20.6 33.4 93.1 2.1 2.1 14 10.8 6.2 20.2 31.8 83.4 2.2 2.1 15 11.0 6.2 20.2 32.4 94.5 2.1 1.9 16 11.3 5.6 19.2 31.4 83.4 1.9 1.8 17 11.5 6.0 17.0 33.2 85.2 2.1 2.1 18 11.8 5.8 19.8 35.4 84.1 2.0 1.8 19 12.0 6.5 19.4 32.2 88.5 2.1 1.9 20 12.3 5.6 18.8 34.0 86.9 2.1 1.8 21 12.5 18.6 18.6 34.2 83.0 1.9 2.0 22 12.5 18.6 20.8 35.4 96.2 2.3 2.3 From the sample data above, complete the following steps.
A scatter plot shows the relationship between two quantitative variables. Each of the six scatter plots pictured shows the least-squares regression line for the corresponding data set. Classify each scatter plot according to whether the least-squares regression line has a meaningful ?y-intercept value.
The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. Find the explained variation. Redshift Distance OA, 0.157904 OB. 0.444904 OC. 0.000582 OD. 0.219582 0.0231 0.34 0.0536 0.76 0.0715 0.99 0.0391 0.56 0.0438 0.63 0.0109 0.15
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + ß₂hwrte + Â¸priGPA+ Â¹ ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 11929.9465 Residual Total final SS atndrte hwrte priGPA ACT _cons 15024.6543 df 773.676939 4 669 17.832506 MS 673 22.324895 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| ||||||||||||| 674 [95% Conf. Interval] Approximate the p-value from the null hypothesis that all of the slope parameters are equal to zero.
Draw a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Then draw the least-squares regression line on the graph.
The ANOVA summary table to the right is for a multiple regression model with six independent variables. Complete parts (a) through (e). a. Determine the regression mean square (MSR) and the mean square error (MSE). (Round to four decimal places as needed.) (Round to four decimal places as needed.) MSR= MSE = b. Compute the overall FSTAT test statistic. FSTAT (Round to two decimal places as needed.) C Source Regression Error Total Degrees of Sum of Freedom Squares 240 190 430 6 26 32

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Three regressions are fitted. The corresponding least Square estimates and MSE are given below. The numbers in parentheses are corresponding standard errors