Using the data on consumption expenditure Y, Income X2, and wealth X3, following output isgenerated:Dependent Variable: YMethod: Least SquaresDate: 11/22/21 Time: 11:44Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb.X20.9415370.8228981.1441720.2902X3-0.0424350.080664-0.5260620.615124.774736.7525003.6689720.0080Mean dependent varS.D. dependent varR-squared0.963504111.0000Adjusted R-squared0.95307731.42893S.E. of regression6.808041Akaike info criterion6.917411Sum squared residLog likelihood324.4459Schwarz criterion7.008186-31.58705Hannan-Quinn criter.6.817830F-statistic92.40196Durbin-Watson stat2.890614Prob(F-statistic)0.000009a) State the estimated OLS regression. Identify the slope coefficients that may not besignificant and provide justifications for your answer.b) Specify and interpret the value of R² for the regression.

The result of a OLS model is given for the dependent variable expenditure Y and the independent…

Answered: Using the data on consumption…

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 multiple regression was estimated using the SEK method and the results are given below. Based on these estimates alone, multiple Explain the level of linearity, varying variance and cascading dependency problems. Bi: 4,1234 B2: -0,2189 B3: -1,7862 sh:3,1223 R?: 0,9820 R*: 0,9750 F: 10,89 F tablo:3,07 sh:4,4420 sh:0,3179 D.W.: 0,2558 d1:0,385 dy:1,685
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the results, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 01/07/16 Time: 11:22 Sample: 2000 2015 Included observations: 16 Variable Coefficient Std. Error t-Statistic Prob. C -0.241942 2.452237 -0.098662 0.9228 X 0.363176 0.013890 26.14674 0.0000 R-squared 0.979933 Mean dependent var 55.43750 Adjusted R-squared 0.978499 S.D. dependent var 33.17221 S.E. of regression 4.864079 Akaike info criterion 6.118100 Sum squared resid 331.2297 Schwarz criterion 6.214674 Log likelihood -46.94480 Hannan-Quinn criter. 6.123046 F-statistic 683.6522 Durbin-Watson stat 0.632113…
A study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
On minitab or excel Calculating the coefficient of determination of a set of data, it was obtained that r^2 = 0.845 . Based on this value, it can be concluded that: Options: The least squares line provided a good fit as a large proportion of the variability in "y" has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a small proportion of the variability in "y" has been explained by the least squares line. The least squares line did not provide a good fit since a large proportion of the variability in "y" has been explained by the least squares line.
The following scatterplot shows a company’s monthly sales, in thousands of dollars, versus monthly advertising dollars spent, in thousands of dollars.
The best depiction of a population characteristic is a(an): Dependent variable Statistic Independent variable Parameter
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
please answer the wrong ones. box answer
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the output table below, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 12/28/16 Time: 15:29 Sample: 2000 2010 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C 0.229334 2.938536 0.078044 0.9395 X 0.354833 0.024783 14.31783 0.0000 R-squared 0.957944 Mean dependent var 37.45455 Adjusted R-squared 0.953271 S.D. dependent var 21.01125 S.E. of regression 4.541976 Akaike info criterion 6.027567 Sum squared resid 185.6659 Schwarz criterion 6.099911 Log likelihood -31.15162 Hannan-Quinn criter. 5.981964…
3.Which model do you think is the “best” reduced model? Discuss why you choose this model. Analysis of Variance Table (Step back model) Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 17.1136 0.0003079 *** sqft 1 35364 35364 28.8196 1.134e-05 *** sd 1 5961 5961 4.8576 0.0362339 * unts 1 8678 8678 7.0722 0.0130049 * gar 1 33364 33364 27.1899 1.713e-05 *** cp 1 7641 7641 6.2269 0.0189934 * Residuals 27 33131 1227 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step forward model: Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 16.6255 0.000406 *** sqft 1 35364 35364 27.9976 1.757e-05 *** sd 1 5961 5961 4.7191 0.039517 * unts 1 8678 8678 6.8705 0.014697 * gar 1 33364 33364 26.4144 2.599e-05 *** cp 1 7641 7641 6.0493 0.021176 *…
This table shows the average heights for American boys in 1990. Age (years) Height (cm) birth 50.8 2 83.8 3 91.4 5 106.6 7 119.3 10 137.1 14 157.5 Decide which variable should be the independent variable and which should be the dependent variable. Draw a scatter plot of the data. Does it appear from inspection that there is a relationship between the variables? Why or why not? Calculate the least-squares line. Put the equation in the form of: ŷ = a + bx Find the correlation coefficient. Is it significant? Find the estimated average height for a one-year-old. Find the estimated average height for an eleven-year-old. Does it appear that a line is the best way to fit the data? Why or why not? Are there any outliers in the data? Use the least squares line to estimate the average height for a sixty-two-year-old man. Do you think that youranswer is reasonable? Why or why not? 10. What is the slope of the least-squares (best-fit) line?…

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