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- Q.3. A random sample of ten families had the following income and food expenditure Families A B C D E F G H I J Income 18 28 31 38 15 13 24 36 33 40 Food Expenditure 7 10 8 10 6 4 7 10 9 10 Estimate the regression line of food expenditure on income.Describe the “overall” regression F-statistic & F-statistic when q=1?Given the following regression output, Predictor Coefficient SE Coefficient t p-value Constant 84.998 1.863 45.62 0.000 x1 2.391 1.200 1.99 0.051 x2 -0.409 0.172 -2.38 0.021 Analysis of Variance Source DF SS MS F p-value Regression 2 77.907 38.954 4.138 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 answer the following questions: Write the regression equation. (Round your answers to 3 decimal places. Negative values should be indicated by a minus sign.) If x1 is 4 and x2 is 11, what is the expected or predicted value of the dependent variable? (Round your answer to 3 decimal places.) How large is the sample? How many independent variables are there?
- Given the following regression output, Predictor Coefficient SE Coefficient t p-value Constant 84.998 1.863 45.62 0.000 x1 2.391 1.200 1.99 0.051 x2 -0.409 0.172 -2.38 0.021 Analysis of Variance Source DF SS MS F p-value Regression 2 77.907 38.954 4.138 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 answer the following questions: d-1. State the decision rule for 0.05 significance level: H0: β1 = β2 = 0; H1: Not all β's are 0. (Round your answer to 2 decimal places.) d-2. Compute the value of the F statistic. (Round your answer to 2 decimal places.) d-3. What is the conclusion? Use the 0.05 significance level.In exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.Suppose you have the following data: X Y 3 9 7 45 5 25 2 8 Using OLS methods: estimate the following regression model: Sq root of Yi = Beta0 + Beta1(Xi) + error
- Consider the regression model Yi = β0 + β1X1i + β2X2i + ui. Transform the regression so that you can use a t-statistic to testa) β1=β2.b) β1+aβ2=0.c) β1 + β2 = 1.Suppose you have a large, random sample of the variables Y and X. You then regress Y on X and get the following results (with standard errors in parentheses): Y=16.8-3.9x (3.8) (1.2) The numbers 16.8 and -3.9 are the realized values for the intercept and slope (respectively) of the regression equation describing the sample, which are consistent estimators for what population parameters? The intercept and slope (respectively) of the sample determining function. The intercept and slope (respectively) of the regression equation that best fits the population. Instructions: Enter your responses rounded to three decimal places. If you are entering any negative numbers be sure to include a negative sign (-) in front of those numbers: b. Provide a 99% confidence interval for each estimator's corresponding population parameter: Intercept: Slope:(------------) (-------------) Slope (-------------) (------------------)Regression Statistics Multiple R 0.971 R-Square A Adjusted R-Square .942 Standard Error 30.462 Observations 51 ANOVA df SS MS F Significance F Regression C 747851.57 373925.79 402.98 9.89E-31 Residual 48 D 927.91 Total 50 792391.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept E 62.13 26.79 1.60E-30 1539.66 1789.51 Price of Roses −6.68 F −1.41 1.64E-01 −16.16 2.81 Disposable Income (M) 9.73 0.34 G 1.23E-31 9.04 10.42…
- You have been presented with the following data and asked to fit statistical demand functions: PERIOD QUANTITY PRICE INCOME ADVERTISING 1 120 8.00 10 3 2 165 4.00 22 7 3 120 7.00 20 5 4 165 3.00 20 8 5 180 4.00 30 8 6 90 10.00 19 6 7 150 4.00 18 10.2 8 190 1.60 25 9.3 9 160 5.00 30 8 10 200 2.00 35 9.5 Linear Relationship Use any multiple regression packages to estimate a linear relationship between the dependent variable and the independent variables. Is the estimated demand function “good”? Why or why not? Discuss the economic implications of the various coefficients. Non-linear relationship. Select and estimate any form of non-linear relationship. Is the estimated demand function “good”? Why or why not? Compare with the linear form above. ElaborateSuppose you run a regression y=alpha + beta*x + u. You know that the estimated coefficient is 2.94 and the standard error is 1.09. What is the value of t-statistic for your estimated coefficient beta?Y=16.8-3.9x (3.8) (1.2) The numbers 16.8 and -3.9 are the realized values for the intercept and slope (respectively) of the regression equation describing the sample, which are consistent estimators for what population parameters? The intercept and slope (respectively) of the sample determining function. The intercept and slope (respectively) of the regression equation that best fits the population. Instructions: Enter your responses rounded to three decimal places. If you are entering any negative numbers be sure to include a negative sign (-) in front of those numbers: provide a 99% confidence interval for each estimator's corresponding population parameter Intercept (----------) (----------) Slope (---------) (-------------)