Using the regression output, determine the regression equation that predicts the English diagnostic test score.
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a. Using the regression output, determine the regression equation that predicts the English diagnostic test score.
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- Both arm circumference and BMI measurements have been used as screening tools for being underweight, overweight, or obese. We want to determine if there is a significant correlation between arm circumference (in centimeters or cm) and body mass index or BMI (in kg.m2) among 10 participants. The results of a correlation and regression analysis are indicated in the Excel output below. The mean arm circumference (the independent variable) was 35.2 cm, and the mean BMI (the dependent variable) was 30.7 kg.m2. SUMMARY OUTPUT Regression Statistics Multiple R 0.855646 R Square 0.732129 Adjusted R Square 0.698646 Standard Error 3.806088 Observations 10 ANOVA df SS MS F Significance F Regression 1 316.7456 316.7456 21.86518 0.001590054 Residual 8 115.8904 14.4863 Total 9 432.636…2. These results come from the tscores_05.dta data set you used in your problem set. Below is a regression of school level test scores (tscores) on the percent of students in the school that are eligible for free meals (pfmeals). a. Interpret the coefficient on pfmeals (mathematically and statistically). b. Do you think the coefficient on the percent of students in the school that are eligible for free meals (pfmeals) is the “right” coefficient (i.e., is it unbiased or causal)? Why or why not? c. Calculate the Residual Sum of Squares.Based on the following output tables, write all the panel data regression models. Which is a better model, FEM or REM? Justify your answer. (200 words) Table A: Dependent Variable: PAYOUT_RATIO Method: Panel Least Squares Variable Coefficient Std. Error t-Statistic Prob. FOREIGN_DIRECTORS 158.8968 14.70223 10.80766 0.0000 FEMALE_DIRECTORS -4.463537 36.09961 -0.123645 0.9023 C 3.143324 5.364327 0.585968 0.5618 Effects Specification Cross-section fixed (dummy variables) R-squared 0.876583 Mean dependent var 38.31686 Adjusted R-squared 0.840284 S.D. dependent var 59.89422 S.E. of regression 23.93641 Akaike info criterion 9.397265…
- Based on the following output tables, write all the panel data regression models. Which is a better model, FEM or REM? Justify your answer. Table A: Dependent Variable: PAYOUT_RATIO Method: Panel Least Squares Variable Coefficient Std. Error t-Statistic Prob. FOREIGN_DIRECTORS 158.8968 14.70223 10.80766 0.0000 FEMALE_DIRECTORS -4.463537 36.09961 -0.123645 0.9023 C 3.143324 5.364327 0.585968 0.5618 Effects Specification Cross-section fixed (dummy variables) R-squared 0.876583 Mean dependent var 38.31686 Adjusted R-squared 0.840284 S.D. dependent var 59.89422 S.E. of regression 23.93641 Akaike info criterion 9.397265 Sum squared…The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.Which of the given independent variables is/are significant? * avil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 1 1 78.0 15500 81.3 16000 82.4 2 3 79.0 80.0 85.0 16200 83.4 3 17500 87.9 89.9 89.1 84.1 89.0 89.2 4 18000 90.3 5 16,300 84.2 3 17000 87.0 4 17900 88.1 GPA and years of experience GPA, years of experience and civil service ratings intercept, GPA, years of experience and civil service ratings O years of experience and civil service ratingsWhat kind of plot is useful for deciding whether finding a regression line for a set of data points is reasonable?
- Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…Please help me to interpret the attached chats. This is the ourput results of Regression (Scatter plots and Histograms) for Austria and United KingdomDefine errors of prediction in a scatter plot with a regression line?
- Consider the following Stata regression output (some values are deliberately removed). Variable | Obs Mean Std. Dev. Min Маx lwage points | rebounds | assists 269 6.952296 .8813761 5.010635 8.655214 269 10.21041 5.900667 1.2 29.8 269 4.401115 2.892573 2.092986 .5 17.3 269 269 2.408922 1682.193 12.6 3533 minutes | 893.3278 33 Source | SS df MS Number of obs F(, Prob > F Model | Residual | = R-squared Adj R-squared Root MSE %3D 0.4146 Total | lwage | Сoef. Std. Err. P>|t| [95% Conf. Interval .0795364 points rebounds .0277761 .0637763 .0204514 3.12 0.002 0.252 0.227 -.0230647 -.0000747 .087425 .0003133 assists .0321805 .0280576 1.15 minutes .0001193 .0000985 1.21 _cons Answer the following questions. Please round your answers to 2 decimal places. Model SS : ; Residual S : ; Total SS :The average height of a large group of children is 43 inches, and the SD is 1.2inches. The average weight of these children is 40 pounds, and the SD is 2pounds. The correlation between the two variables is r = 0.65.A scatter diagram is drawn, with height on the horizontal axis and weight on thevertical axis. The scatter diagram is football shaped. The regression line forpredicting weight based on height is drawn through the scatter.(a) Predict the weights and the typical size of the error for those predictions ineach of the following case:A child who is 43 inches tall is predicted to weigh _____________ pounds, give ortake _____________ pounds.A child who is 41.8 inches tall is predicted to weigh ____________ pounds, give ortake _____________ pounds.The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance. starting salary GPA Years of experience Civil Service Ratings 15000 80.1 1 79.5 15000 81.2 1 78.0 15500 81.3 2 79.0 16000 82.4 3 80.0 16200 83.4 3 85.0 17500 87.9 4 89.9 18000 90.3 5 89.1 16300 84.2 3 84.1 17000 87.0 4 89.0 17900 88.1 5 89.2 Based on the multiple regression output, if GPA and civil service ratings are held fixed, how much is the expected increase in the starting salary (pesos) for every one year increase in the years of experience?
