Consider the following data on production volume $ (x) $ and total cost $ (y) $ for a particular manufacturing operation.**Excel File:** data14-29.xlsx| Production Volume (units) | Total Cost ($) ||---------------------------|----------------|| 400 | 4,000 || 450 | 5,000 || 550 | 5,400 || 600 | 5,900 || 700 | 6,400 || 750 | 7,000 |The estimated regression equation is $\hat{y} = 1246.67 + 7.6x$.Use $\alpha = 0.05$ to test whether the production volume is significantly related to the total cost.Complete the ANOVA table. Enter all values with nearest whole number, except the $ F $ test statistic (to 2 decimals) and the $ p $-value (to 4 decimals).| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | $ F $ | $ p $-value ||---------------------|--------------------|----------------|-------------|-------|-------------|| Regression | | | | | || Error | | | | | || Total | | | | | |What is your conclusion?- Select your answer -

Answer: Given that; The estimated regression equation is y^= 1246.67 + 7.6x Sample size =6…

Answered: The estimated regression equation is ŷ…

Business

Economics

The estimated regression equation is ŷ = 1246.67 +7.6.

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Suppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?
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
QUESTION 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…
q11-
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.
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 month
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*…
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute Time (minutes), x Well-Being Index Score, y 5 72 105 20 25 35 60 69.2 68.0 67.5 67.1 65.9 66.0 63.8 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. ŷ=x+ (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. OA. For every unit increase in commute time, the index score falls by (Round to three decimal places as needed.) OB. For every unit increase in index score, the commute time falls by (Round to three decimal places as needed.) 1 D. For an index score of zero, the commute time is predicted to be (Round to three decimal places as needed.) on average. on average. OC. For a commute time…
An example of a cubic regression model is Yi= 30 + B1X + 32x2 + 33x³ + ui Yi = 30 + B1X + 32x² + ui. Yi = 30 + ß1ln(X) + ui Yi= 30 + 31X + B2Y2 + ui.
Given the estimated multiple regression equation ŷ = 6 + 5x1 + 4x2 + 7x3 + 8x4 what is the predicted value of Y in each case? a. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 b. x1 = 23, x2 = 18, x3 = 10, and x4 = 11 c. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 d. x1 = -10, x2 = 13, x3 = -8, and x4 = -16
please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly
QUESTION 2 Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u]x] #0 and that z is a valid instrument for r. Knowing that Cov(y, z) = 0.5 and Cov(z, x) = 0.5, the IV estimate of 3 is 1. %3D O True O False