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
).
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Write one paragraph describing the results of your analysis as
they relate to your initial hypothesis...
Given the following regression output in Stata Indicate what is the effect of x2 on Y...
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
· regress y xl x2 Source SS dEMS Model Residual 2.6644e+092 1.3322e+09 26878436.6 12 2239869.72 Number of obs = F( 2, 12) = Prob > F R-squared Adj R-squared = Root MSE 15 594.76 0.0000 0.9900 0.9883 1496.6 Total 2.6912e+0914 2.6912 192231167 Coef. Std. Err. t >It (95% Conf. Intervall x1 2.44061 .3440342 -32137.37 6.125326...
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
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...
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...
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,...
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...
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,...
Suppose SAT score and high school graduating class size (hsize, scaled in terms of 100 students)...
Suppose SAT score and high school graduating class size (hsize, scaled in terms of 100 students) are correlated and the unobserved factors denoted by u. Then SAT = B + Bhsize+u. ss df - • reg sat hsize Source Model 324242.826 Residual 1 80049603.5 Total 1 80373846.3 1 4.135 MS 324242.826 19359.0335 19432.7481 Number of obs F(1, 4135) Prob > F R-squared Adj R-squared Root MSE 4.137 16.75 0.0000 4,136 0.0038 139.14 sat hsize _cons Coef. 5.098593 1016.056 Std. Err....
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,...
. Question 3 (p) egresion analyis regress weight length mate () sS 23 Number of obs...
. Question 3 (p) egresion analyis regress weight length mate () sS 23 Number of obs Fi 2,20 38.s Prob R-squared Source 1S Model 4688 63921 2 244.31511 Residuat1537.1958720 76.8597937 e,7s32 Total 6225.8269 22 282 Root PtSE weight 195s Conf. Intervati Coef sd ErI 6791132 .995297ธ 1515773 6.57 0.00 . ele8.953177 3.8878 2.38 .932 cons -108.9868 25.66558-4 . 25 ล.900-162 , 5242-55.449,1 . In this question you are discuss how the regression ontput is to he interpreted, providing foraulas for...
Model 1 Model 2 Countries have a keen interest in exploring the drivers of their sectoral...
Model 1
Model 2
Countries have a keen interest in exploring the drivers of
their sectoral energy consumption, including TRANSPORTATION
energy use. These models will examine the log of
final energy use by TRANSPORTATION
“ln_tranpc” across 128 countries.
All variables with names beginning “ln” are measured in natural
logarithms. The variable oecd is a dummy variable
equal to 1 for countries in the OECD and equal to zero otherwise.
The variables are described below:
Lntran_pc = log of transportation
energy...
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
· regress y xl x2 Source SS dEMS Model Residual 2.6644e+092 1.3322e+09 26878436.6 12 2239869.72 Number of obs = F( 2, 12) = Prob > F R-squared Adj R-squared = Root MSE 15 594.76 0.0000 0.9900 0.9883 1496.6 Total 2.6912e+0914 2.6912 192231167 Coef. Std. Err. t >It (95% Conf. Intervall x1 2.44061 .3440342 -32137.37 6.125326...
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...
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,...
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,...
Suppose SAT score and high school graduating class size (hsize, scaled in terms of 100 students) are correlated and the unobserved factors denoted by u. Then SAT = B + Bhsize+u. ss df - • reg sat hsize Source Model 324242.826 Residual 1 80049603.5 Total 1 80373846.3 1 4.135 MS 324242.826 19359.0335 19432.7481 Number of obs F(1, 4135) Prob > F R-squared Adj R-squared Root MSE 4.137 16.75 0.0000 4,136 0.0038 139.14 sat hsize _cons Coef. 5.098593 1016.056 Std. Err....
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,...
. Question 3 (p) egresion analyis regress weight length mate () sS 23 Number of obs Fi 2,20 38.s Prob R-squared Source 1S Model 4688 63921 2 244.31511 Residuat1537.1958720 76.8597937 e,7s32 Total 6225.8269 22 282 Root PtSE weight 195s Conf. Intervati Coef sd ErI 6791132 .995297ธ 1515773 6.57 0.00 . ele8.953177 3.8878 2.38 .932 cons -108.9868 25.66558-4 . 25 ล.900-162 , 5242-55.449,1 . In this question you are discuss how the regression ontput is to he interpreted, providing foraulas for...
Model 1
Model 2
Countries have a keen interest in exploring the drivers of
their sectoral energy consumption, including TRANSPORTATION
energy use. These models will examine the log of
final energy use by TRANSPORTATION
“ln_tranpc” across 128 countries.
All variables with names beginning “ln” are measured in natural
logarithms. The variable oecd is a dummy variable
equal to 1 for countries in the OECD and equal to zero otherwise.
The variables are described below:
Lntran_pc = log of transportation
energy...
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