Given the following regression output in Stata
Indicate what is the effect of x2 on Y by testing the hypothesis
that x2 determines Y given 
Homework Answers
From the output, it can be seen that the p-value for x2 is 0.00 < 0.05. Hence, we reject the null hypothesis which is H0: regression coefficient corresponding to x2 is 0.
Hence, x2 has a significant effect on y. Also, a one-unit increase in the value of x2 implies an increase of 0.3440342 in the value of y.
Hope this helps!
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Given the following regression output in Stata
Indicate what is the effect of x2 on Y...
You are given different sets of Stata output below. Please use the appropriate Stata output to...
You are given different sets of Stata output below. Please use the appropriate Stata output to answer questions below.log price is the natural log of price. a. Write the estimated equation from a regression of log price on mpg, weight, headroom, and trunk. Interpret each coefficient. b. Test if headroom and trunk have no effect on price. Please show your work. Source | SS df MS Model Residual 3.82089653 7.40263655 4 69 .955224132 .107284588 Number of obs = FC 4,...
What does the coefficient estimate for lnNumHH tell you? Do you think there is a problem...
What does the coefficient estimate for lnNumHH tell you?
Do you think there is a problem with the regression, if so what
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please interpret the regress result findings (sign, coefficient, statistical significance, R^2, Adjusted R^2) for each independent...
please interpret the regress result findings (sign, coefficient,
statistical significance, R^2, Adjusted R^2) for each independent
variable in the NBA salary model
regress salary laggaterevenue lagwp48 Source SS df MS Model Residual 1.1647e+15 8.0148e+15 2 423 5.8236e+14 1.8947e+13 Number of obs F(2, 423) Prob > F R-squared Adj R-squared Root MSE 426 30.74 0.0000 0.1269 0.1228 4.4e+06 = Total 9.1795e+15 425 2.1599e+13 = salary Coef. Std. Err. t P>|t| [95% Conf. Interval] laggaterevene lagwp48 _cons .0044275 1.34e+07 3448595 .0109924 1732419...
Write one paragraph describing the results of your analysis as they relate to your initial hypothesis...
Write one paragraph describing the results of your analysis as
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Question 1 First run the regression: EARNINGSi = β1 + β2ASVABCi + β3Si + ui Then...
Question 1
First run the regression:
EARNINGSi = β1 + β2ASVABCi + β3Si + ui
Then run the regression with experience:
EARNINGSi = β1 + β2ASVABCi + β3Si + β4EXPi + ui
Compare the results from these two regressions, do you get an
indication that the previous estimate of schooling without EXP was
biased? If so, in which direction? And why is that?
Question 2
Add gender dummy variable to the regression (the one running
regression of EARNINGS on ASVABC,...
. There is Stata output from a second OLS regression model with the variables defined as...
. There is Stata output from a second OLS regression model with the variables defined as above. This time we include an interaction term "ageXgender" for the independent variables "age" and "gender." Use this output to answer parts g through i. regress casp age gender married agexgender df Number of obs- Source | 4,849 137.04 0.0000 0.1017 0.1009 6.0079 MS +FC4, 4844) 4,844 36.0944447 R-squared Model 19785.9491 4 4946.48728 Prob > F Residual 174841.49 Adj R-squared + Total 194627.439 4,848...
In the solution proposal DF = 21 when testing this hypotheses, but when doing a f test for significant regression DF is 24. I need help understanding this:) Regards Richard df MS Source I Number of o...
In the solution proposal DF = 21 when testing this hypotheses,
but when doing a f test for significant regression DF is 24.
I need help understanding this:)
Regards Richard
df MS Source I Number of obs 27 2. 24) - 200.25 - 0.0000 О. 9435 Adj R-squared 0.9388 .18837 2 7.10578187 Residual! .85163374 24.035484739 Model 14.2115637 Prob F R-squared Total 15.0631975 26.57935375 Root MSE Coef. Std. Err. [95% Conf. Interval] 125954 085346 .326782 1nLI.6029994 1nK I.3757102 cons1.170644 2.790.000 4.40...
A researcher is examining the effect of number of years in a particular job (tenure) on...
A researcher is examining the effect of number of years in a
particular job (tenure) on the hourly wage (USD) earned. She
estimates two regression models using data on 124 young women
surveyed in 1988. In the first regression, she regressed logged
hourly wage on logged tenure in the current job (years in current
job). In the second regression she regressed logged hourly wage on
logged tenure in the current job, as well as age, total years of
schooling completed,...
Consider the STATA output below, in which the outcome variable is wage (rate of pay in...
Consider the STATA output below, in which the outcome variable is wage (rate of pay in dollars per hour) and the independent variables are total years of experience (ttl_exp)total years in current position (tenure), and number of years of school completed (grade) regrens wage ttt_exp tenure prade Source 55 df MS Number of obs- 3. 2225) - 120.02 Madet 10363.7014 13654.56714 63124.263 22 20.3784588 Adj R-squared - . 1468 74887.68 2220 39.2531214 Rot MSE 3.148 Wage Std. Err. Coet. .232752...
A researcher is examining the effect of number of years in a particular job (tenure) on...
A researcher is examining the effect of number of years in a particular job (tenure) on the hourly wage (USD) earned. She estimates two regression models using data on 124 young women surveyed in 1988. In the first regression, she regressed logged hourly wage on logged tenure in the current job (years in current job). In the second regression she regressed logged hourly wage on logged tenure in the current job, as well as age, total years of schooling completed,...
You are given different sets of Stata output below. Please use the appropriate Stata output to answer questions below.log price is the natural log of price. a. Write the estimated equation from a regression of log price on mpg, weight, headroom, and trunk. Interpret each coefficient. b. Test if headroom and trunk have no effect on price. Please show your work. Source | SS df MS Model Residual 3.82089653 7.40263655 4 69 .955224132 .107284588 Number of obs = FC 4,...
What does the coefficient estimate for lnNumHH tell you?
Do you think there is a problem with the regression, if so what
is the problem?
• regress lnMedInc in NumHH Source SS df MS Model Residual 1.92343603 8.48203352 1 364 1.92343603 02330229 Number of obs F(1, 364) Prob > F. R-squared Adj R-squared Root MSE IL L LLLL 366 82.54 0.0000 0.1848 0.1826 . 15265 Total 10.4054695 365 .028508136 InMedInc Coef. Std. Err. t P> [t] [95% Conf. Interval] InNumHH...
please interpret the regress result findings (sign, coefficient,
statistical significance, R^2, Adjusted R^2) for each independent
variable in the NBA salary model
regress salary laggaterevenue lagwp48 Source SS df MS Model Residual 1.1647e+15 8.0148e+15 2 423 5.8236e+14 1.8947e+13 Number of obs F(2, 423) Prob > F R-squared Adj R-squared Root MSE 426 30.74 0.0000 0.1269 0.1228 4.4e+06 = Total 9.1795e+15 425 2.1599e+13 = salary Coef. Std. Err. t P>|t| [95% Conf. Interval] laggaterevene lagwp48 _cons .0044275 1.34e+07 3448595 .0109924 1732419...
Write one paragraph describing the results of your analysis as
they relate to your initial hypothesis ( if you live in colder
areas, the rate of homelessness and unemployment are less
).
· regress HOMLSTOT UNEMRATE AVGTEMP Source SS df MS Model 160347072 2.2246e+09 2 80173535.9 32 69520191.8 Number of obs F(2, 32) Prob > F R-squared Adj R-squared Root MSE 35 1.15 0.3284 0.0672 0.0089 8337.9 Residual Total 2.3850e+09 34 70146859.1 HOMLSTOT Coef. Std. Err. t P> [t] [95%...
Question 1
First run the regression:
EARNINGSi = β1 + β2ASVABCi + β3Si + ui
Then run the regression with experience:
EARNINGSi = β1 + β2ASVABCi + β3Si + β4EXPi + ui
Compare the results from these two regressions, do you get an
indication that the previous estimate of schooling without EXP was
biased? If so, in which direction? And why is that?
Question 2
Add gender dummy variable to the regression (the one running
regression of EARNINGS on ASVABC,...
. There is Stata output from a second OLS regression model with the variables defined as above. This time we include an interaction term "ageXgender" for the independent variables "age" and "gender." Use this output to answer parts g through i. regress casp age gender married agexgender df Number of obs- Source | 4,849 137.04 0.0000 0.1017 0.1009 6.0079 MS +FC4, 4844) 4,844 36.0944447 R-squared Model 19785.9491 4 4946.48728 Prob > F Residual 174841.49 Adj R-squared + Total 194627.439 4,848...
In the solution proposal DF = 21 when testing this hypotheses,
but when doing a f test for significant regression DF is 24.
I need help understanding this:)
Regards Richard
df MS Source I Number of obs 27 2. 24) - 200.25 - 0.0000 О. 9435 Adj R-squared 0.9388 .18837 2 7.10578187 Residual! .85163374 24.035484739 Model 14.2115637 Prob F R-squared Total 15.0631975 26.57935375 Root MSE Coef. Std. Err. [95% Conf. Interval] 125954 085346 .326782 1nLI.6029994 1nK I.3757102 cons1.170644 2.790.000 4.40...
A researcher is examining the effect of number of years in a
particular job (tenure) on the hourly wage (USD) earned. She
estimates two regression models using data on 124 young women
surveyed in 1988. In the first regression, she regressed logged
hourly wage on logged tenure in the current job (years in current
job). In the second regression she regressed logged hourly wage on
logged tenure in the current job, as well as age, total years of
schooling completed,...
Consider the STATA output below, in which the outcome variable is wage (rate of pay in dollars per hour) and the independent variables are total years of experience (ttl_exp)total years in current position (tenure), and number of years of school completed (grade) regrens wage ttt_exp tenure prade Source 55 df MS Number of obs- 3. 2225) - 120.02 Madet 10363.7014 13654.56714 63124.263 22 20.3784588 Adj R-squared - . 1468 74887.68 2220 39.2531214 Rot MSE 3.148 Wage Std. Err. Coet. .232752...
A researcher is examining the effect of number of years in a particular job (tenure) on the hourly wage (USD) earned. She estimates two regression models using data on 124 young women surveyed in 1988. In the first regression, she regressed logged hourly wage on logged tenure in the current job (years in current job). In the second regression she regressed logged hourly wage on logged tenure in the current job, as well as age, total years of schooling completed,...
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