Consider the output here from a regression in R. What is B₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758
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- You are given the following data: The regression equation is: A. -0.66 B. -1.20 (X'X)*¹ C. 1.12 O D. 2.06 = 1.3 2.1 0.8 -1.4 1.9 2.1 -1.4 s² = 0.86. T = 103 The correlation between ₁ and 3 (i.e., corr(Â₁, Â3)) is: -1.6] 1.9 (X'y) = 2.9 3.4 0.8 Yt = B₁ + B₂X2+ + B3X3t + Ut.The following question refers to this regression equation (standard errors for each of the estimated coefficients are in parenthesis). Q=8,400-8" P+5" A+ 4** Px +0.05**1, (1,732) (2.29) (1.36) (1.75) (0.15) Q = Quantity demanded P = Price 1,100 Advertising expenditures, in thousands = 20 P = price of competitor's good = 600/= average monthly income 10,000 What is the advertising elasticity of demand? Round your answer to two decimal places. Your Answer: The t-statistic is computed by dividing the regression coefficient by the standard error of the coefficient. dividing the regression coefficient by the standard error of the estimate. dividing the standard error of the coefficient by the regression coefficient. dividing the R2 by the F-statistic. none of the specified answers are correct.Q4. The Omantel firm has estimate the Sales of fibre internet connections in Oman with the related to advertising expenditure made by the company over the past 26 months. Following is the firm estimated results of the regression equation. DEPENDENT VARIABLE: Y R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 26 0.85121212 8.747 0.0187 PARAMETER STANDARD VARIABLE ESTIMATE ERROR T-RATIO P-VALUE INTERCEPT 7.6 6.33232 1.200 0.2643969 3.53 0.52228 ? 0.0001428 a. What is the dependent and independent variables in the above regression equation of Omantel firm? b. Calculate the estimated t-ratio. Test the slope estimates for statistical significance at the 10 percent significance level. d. Interpret the coefficient of determination.
- Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and so collects monthly data for 25 firms. He estimates the model: Sales 6g + 61 Advertising + e. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value 40.10 14.88 2.848 0.0052 Intercept Advertising 2.88 1.52 -1.895 0.0608 When testing whether Advertising is significant at the 10% significance level, the conclusion is to Multiple Choice reject Hg, we can conclude advertising is significant not reject He; we cannot conclude advertising is significant reject He; we cannot conclude advertising is significant not reject He; we can conclude advertising is significantYou estimated a regression with the following output. Source | SS df MS Number of obs = 268 -------------+---------------------------------- F(1, 266) = 23.48 Model | 668419.175 1 668419.175 Prob > F = 0.0000 Residual | 7572666.51 266 28468.6711 R-squared = 0.0811 -------------+---------------------------------- Adj R-squared = 0.0777 Total | 8241085.68 267 30865.4895 Root MSE = 168.73 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 1.014128 .2092916 4.85 0.000 .6020489 1.426207 _cons | 9.173163 21.13463 0.43 0.665 -32.43929 50.78561…
- Y 70 12 50 9 57 60 14 43 9 52 11 i. Find the estimators for Bi and B2 correct to decimal points and fit the regression equation for X and Y when X is the explanatory variable. Interpret the results from the obtained equation. calculate the sum of error squared. Find the variance of the sum square error ii. iii. iv. Find the standard error for B2 Find the coefficient of correlation and give its interpretation V. vi.You estimated a regression with the following output. Source | SS df MS Number of obs = 223 -------------+---------------------------------- F(1, 221) = 17592.99 Model | 182392130 1 182392130 Prob > F = 0.0000 Residual | 2291176.96 221 10367.3166 R-squared = 0.9876 -------------+---------------------------------- Adj R-squared = 0.9875 Total | 184683307 222 831906.786 Root MSE = 101.82 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.97037 .0902481 132.64 0.000 11.79252 12.14823 _cons | 74.40159 10.96696 6.78 0.000 52.78839 96.01479…Consider the following regression: Test Score, = 68.12 +2.52Hours Studied, - 0.04Hours Studied? %3D Without studying, an individual would average a test score of
- calculate slope coefficient for a regression of Y on X calculate the constant of a regression of Y on X calculate the residual for the first observation in the table5- zero correlation does not necessarily imply independence between the two variables. This statement is Please select one; a) true www b) depends on mean value of X and Y c) depends on r wwww w d) falseConsider the following formula: y i - ( β 0 ^ + β 1 ^ x i ) . What does this formula describe? OLS slope estimator Error term Causal effect of x on y residual