Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in variousyears, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years. Isthe result within 5 years of the actual Best Actor winner, whose age was 45 years?Best Actress2730306130 32 46 2861224356 DBest Actor42393845 514959 5138574534Find the equation of the regression line.y =+(Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.)The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years isyearsold.(Round to the nearest whole number as needed.)Is the result within 5 years of the actual Best Actor winner, whose age was 45 years?the predicted age isthe actual winner's age.

Excel regression summary: SUMMARY OUTPUT Regression Statistics…

Answered: Find the equation of the regression…

Business

Economics

Find the equation of the regression line.

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

An OLS regression should be used when the independent variable is nominal. A. True B. False
8
A company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the regression equation and use it to forecast the next three periods. Click the icon to view the historical data for the previous 12 periods. The simple linear regression line is F₁ =+x. (Enter your responses rounded to two decimal places and include a minus sign if necessary.) Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places). X Period Forecast (F₁) fo Period (x) 1 2 3 4 5 6 7 8 9 10 11 12 at (y) 211 184 199 187 156 150 162 165 146 130 119 129 ㅁ - 13 14 15 Next
I need Explaination based on the answer key answer is D but how. It is econometrics
Find the degrees of freedom in a regression model that has 40 observations, 6 independent variables and one intercept. a. 33 b. 47 c. 7 d. 39
When the regression line passes through the origin then: O The intercept is zero. O The regression coefficient is zero. O The correlation is zero. O The association is zero. O All of the above.
In a regression model, if the variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y/x), is not constant, a. the t statistics are invalid and the confidence intervals are valid for small sample sizes. b. the t statistics are valid and the confidence intervals are invalid for small sample sizes. c. the t statistics and the confidence intervals are valid no matter how large the sample size. d. the t statistics and the confidence intervals are both invalid no matter how large the sample size. O a. a O b. b О с. C ○ d. d
You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA = 3.4 0.03 * HrsWrk - 0.7 * Frosh - 0.3 * Soph +0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. e) Suppose you rewrite the regression as: problem. GPA = ₁HrsWrk + ß2Frosh + B2Soph +…
Please help me with both the question. Answer there are incorrect
Help!
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
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.