ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN: 9780190931919
Author: NEWNAN
Publisher: Oxford University Press
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What does the value for the coefficient mean in the regression analysis of both the company ?
P-Values - how its related to customer satisfaction in the regression analysis of both the company ?
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- I need Explaination based on the answer key answer is D but how. It is econometricsarrow_forwardwages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]arrow_forwardIn an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable? A. 2.53 B. 16.22 C. .001 D. 0.53arrow_forward
- You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…arrow_forwardAn 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 0.41arrow_forwardQUESTION 10 Answer questions 10 to 16 based on the regression outputs given in Table 1& 2. Table 1 DATA4-1: Data on single family homes in University City community of San Diego, in 1990. price - sale price in thousands of dollars (Range 199. 9 505) sqft - square feet of living area (Range 1065 - 3000) Table 2 Model 1: OLS, using observations 1-14 Dependent variable: price coefficient std. error t-ratio p-value 52. 3509 0.138750 37. 2855 0.0187329 0. 1857 8. 20e-06 *** const sqft 7. 407 Me dependent var Sun squared resid R-squared F(1, 12) Log-likelihood Schwarz criterion 317. 4929 18273. 57 0. 820522 54. 86051 -70. 08421 145. 4465 Hannan-Quinn S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion 88. 49816 39. 02304 0. 805565 8. 20e-06 144. 1684 144. 0501 There are observations included in this dataset. It is a. data. O 12; cross-sectional 13; time-series data 14; cross-sectional In this regression model, sale price of a single-family house is the. the…arrow_forward
- The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per montharrow_forwardIn the regression equation, what is B0? Group of answer choices the population slope the sample y-intercept the sample slope the population y-interceptarrow_forwardWhen running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?arrow_forward
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