Results of Regressions of Average Hourly Earnings on Gender andEducation Binary Variables and Other Characteristics Using Datafrom the Current Population SurveyDependent variable: average hourly earnings (AHE).Regressor(1)(2)(3)5.595.55College (X,)5.57Female (X2)-2.69-2.67-2.670.300.30Age (X3)0.70Northeast (X4)0.61Midwest (Xs)-0.28South (X)Intercept12.944.493.83Summary StatisticsSER6.406.346.330.1800.1940.1980.193ok970.180410041004100Using the regression results in column (2):On average, a worker eans $ per hourfor each year that he or she ages.

The regression equation for the second column is Y = 4.49 + 5.59(X1) - 2.67(X2) + 0.30 (X3) taking…

Results of Regressions of Average Houry Earning Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) 5.57 5.59 5.55 College (X,) Female (X2) -2.69 -2.67 -2.67 0.30 0.30 Age (X) 0.70 Northeast (X,) 0.61 Midwest (Xs) South (X) -0.28 12.94 4.49 3.83 Intercept Summary Statistics SER 6.40 6.34 6.33 0.180 0.194 0.198 0.180 0.193 O 97 4100 4100 4100 Using the regression results in column (2) On average, a worker eams $ per hour V for each year that he or she ages.

Results of Regressions of Average Houry Earning Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) 5.57 5.59 5.55 College (X,) Female (X2) -2.69 -2.67 -2.67 0.30 0.30 Age (X) 0.70 Northeast (X,) 0.61 Midwest (Xs) South (X) -0.28 12.94 4.49 3.83 Intercept Summary Statistics SER 6.40 6.34 6.33 0.180 0.194 0.198 0.180 0.193 O 97 4100 4100 4100 Using the regression results in column (2) On average, a worker eams $ per hour V for each year that he or she ages.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

See similar textbooks

Similar questions

A regression of average weekly earnings (AWE, measured in dollars) on age(measured in years) using a random sample of college-educated full-timeworkers aged 25–65 yields the following: AWE = 696.7 + 9.6 X Age, R2 = 0.023, SER = 624.1.a. Explain what the coefficient values 696.7 and 9.6 mean.b. The standard error of the regression (SER) is 624.1. What are the unitsof measurement for the SER? (Dollars? Years? Or is SER unit-free?)c. The regression R2 is 0.023. What are the units of measurement for theR2? (Dollars? Years? Or is R2 unit-free?)d. What does the regression predict will be the earnings for a 25-year-oldworker? For a 45-year-old worker?e. Will the regression give reliable predictions for a 99-year-old worker?Why or why not?f. Given what you know about the distribution of earnings, do youthink it is plausible that the distribution of errors in the regressionis normal? (Hint: Do you think that the distribution is symmetric orskewed? What is the smallest value of earnings,…
In exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.
Regression Statistics Multiple R 0.971 R-Square A Adjusted R-Square .942 Standard Error 30.462 Observations 51 ANOVA df SS MS F Significance F Regression C 747851.57 373925.79 402.98 9.89E-31 Residual 48 D 927.91 Total 50 792391.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept E 62.13 26.79 1.60E-30 1539.66 1789.51 Price of Roses −6.68 F −1.41 1.64E-01 −16.16 2.81 Disposable Income (M) 9.73 0.34 G 1.23E-31 9.04 10.42…
Describe the “overall” regression F-statistic & F-statistic when q=1?
a simple linear regression equation shows the relationship between-
An OLS regression should be used when the independent variable is nominal. A. True B. False
Using the regression results in column (1):a. Is the college–high school earnings difference estimated from thisregression statistically significant at the 5% level? Construct a 95%confidence interval of the difference.b. Is the male–female earnings difference estimated from this regressionstatistically significant at the 5% level? Construct a 95% confidenceinterval for the differenc(answer for me part please)
determine the regression line equation plot the line on a graph and summarize the results( reject or do not) is there enough evidence?
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…
Y=16.8-3.9x (3.8) (1.2) The numbers 16.8 and -3.9 are the realized values for the intercept and slope (respectively) of the regression equation describing the sample, which are consistent estimators for what population parameters? The intercept and slope (respectively) of the sample determining function. The intercept and slope (respectively) of the regression equation that best fits the population. Instructions: Enter your responses rounded to three decimal places. If you are entering any negative numbers be sure to include a negative sign (-) in front of those numbers: provide a 99% confidence interval for each estimator's corresponding population parameter Intercept (----------) (----------) Slope (---------) (-------------)
1. Explain the meaning of: R-SQUARED F-TEST MULTICOLLINEARITY 2. Why are these considered as more important in multiple regression analysis? 3. How to detect problems in multicollinearity?
the regression R2 is 0.98.This mean