SE ST X₂ 7 e estimated regression equation for these data is = 7.6 +0.9a. mpute SSE, SST, and SSR (to 1 decimal). 000 2 6 9 13 20 18 9 26 23 ∞ SR hat percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % hat is the value of the sample correlation coefficient (to 3 decimals)?

SE ST X₂ 7 e estimated regression equation for these data is = 7.6 +0.9a. mpute SSE, SST, and SSR (to 1 decimal). 000 2 6 9 13 20 18 9 26 23 ∞ SR hat percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % hat is the value of the sample correlation coefficient (to 3 decimals)?

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
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The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
6 9 13 Yi 7 18 9. 26 23 a. Which of the following scatter diagrams accurately represents the data? A. 24+ 24+ 20- 20- 16+ 16+ 12+ 12+ 8+ 8+ 4- -4 4 12 16 24 28 x -4 4 8. 12 16 20 24 28 x -4t -4t С. D. 24 -----24+ 20- 20- 16- 16- 20 B. 20 00 00 O.
Mita, the manufacturer of copiers, has been spending increasing amounts of money on radio and television advertising in recent years. An analyst employed by Mita wanted to estimate a simple linear regression of the company's annual copier sales versus advertising dollars. Th regression results included SSE = 12593 and SSR = 87663. What is the coefficient of determination for this regression? 0.874 0.935 0.144 0.126
This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…
In the regression equation, what is B0? Group of answer choices the population slope the sample y-intercept the sample slope the population y-intercept
INSUR A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INCOME 97 38 141 29 y = x = Let INSUR 280 75 INCOME 303 81 453 137 The numerator of the slope coefficient formula for the estimated regression equation is: 357 77 a 400,201.10 199 43 b 392,354.02 251 53 384,660.80 807 184 d 377,118.44 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 280 69 507 140 464 136 321 71 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51
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You estimated a regression with the following output. Source | SS df MS Number of obs = 335 -------------+---------------------------------- F(1, 333) = 69555.83 Model | 211169628 1 211169628 Prob > F = 0.0000 Residual | 1010979.01 333 3035.97301 R-squared = 0.9952 -------------+---------------------------------- Adj R-squared = 0.9952 Total | 212180607 334 635271.28 Root MSE = 55.1 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.15183 .1674102 263.73 0.000 43.82251 44.48114 _cons | 31.63715 16.49849 1.92 0.056 -.8172452 64.09155…
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