a. Estimate and report OLS parameter estimates for the equation Mr=α+β1exec+β2south+β3 ue+β4capital+β5pcy+ε b. Plot mr on...

a. Estimate and report OLS parameter estimates for the equation Mr=α+β1exec+β2south+β3 ue+β4capital+β5pcy+ε

b. Plot mr on exec, mr on ue, and mr on pcy.

c. Plot the fitted values for mr on exec, mr on ue, and mr on pcy.

d. Plot the errors for errors on exec, errors on ue, and errors on pcy.

Please download data from the link below as csv file

https://docs.google.com/spreadsheets/d/1gjvCRmxkIu7vxlGGB5ej7qMhiNzHnGSz5yX2O-TYJdI/edit?usp=sharing data is provided on link, please download as csv file

Expert Solution

The Data

mr	exec	south	ue	capital	pcy
11.6	0	1	7.5	1	13.53
9	0	0	7.6	0	18.223
8.6	2	0	6.2	1	14.285
10.2	0	1	6.2	1	12.634
13.1	1	0	9.2	1	17.295
5.8	0	0	5.2	1	16.981
6.3	0	0	6.2	1	22.236
5	0	1	5.3	1	17.261
78.5	0	1	8.5	1	23.302
8.9	3	1	7	1	16.311
11.4	2	1	5.8	1	15.205
3.8	0	0	4.2	0	18.566
2.9	0	0	6.1	1	13.833
11.4	0	0	7.4	1	17.82
7.5	0	0	5.3	1	15.176
2.3	0	0	4	1	14.435
6.4	0	0	5	1	15.679
6.6	0	1	6.2	1	13.34
20.3	1	1	7.4	1	13.122
1.6	0	0	7.9	0	14.834
12.7	0	1	6.2	1	18.885
3.9	0	0	6.9	1	19.281
9.8	0	0	7	0	16.259
3.4	0	0	5.1	0	16.571
13.5	0	1	6.3	1	11.647
11.3	4	0	6.4	1	15.448
3	0	0	6	1	13.725
11.3	0	1	4.9	1	14.747
1.7	0	0	4.3	0	13.485
3.9	0	0	2.6	1	15.539
10.4	0	0	7.2	1	18.084
2	0	0	6.6	0	17.66
5.3	0	0	7.4	1	21.229
8	0	0	7.5	1	12.912
13.3	0	0	7.7	1	19.608
6	0	0	6.5	1	15.558
8.4	0	1	6	1	13.449
4.6	0	0	7.2	1	15.353
6.8	0	0	7	1	16.8
3.9	0	0	7.7	0	16.78
10.3	0	1	7.5	1	13.318
3.4	0	0	3.5	1	14.122
10.2	0	1	5.7	1	14.565
11.9	17	1	7	1	15.122
3.1	0	0	3.9	1	12.746
3.6	5	0	5.4	1	15.353
8.3	1	1	5	1	17.103
6.9	0	1	10.8	1	12.772
5.2	0	0	7.5	1	17.199
4.4	0	0	4.7	0	15.645
3.4	0	0	5.4	1	15.576

The regression analysis (Done in Excel... Data > Data Analysis > Regression)

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.638242393
R Square	0.407353352
Adjusted R Square	0.341503724
Standard Error	8.697075215
Observations	51

ANOVA
	df	SS	MS	F	Significance F
Regression	5	2339.561	467.9123	6.186115	0.000191555
Residual	45	3403.76	75.63912
Total	50	5743.322

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-35.00801648	9.41774	-3.71724	0.000555	-53.97631801	-16.0397
exec	-0.145458912	0.494027	-0.29444	0.769779	-1.14047937	0.849562
south	9.768244592	2.898035	3.370644	0.001548	3.93130301	15.60519
ue	1.278281389	0.86191	1.48308	0.145023	-0.457694173	3.014257
capital	2.444519824	3.390438	0.721004	0.474636	-4.384173815	9.273213
pcy	1.920234313	0.521169	3.684474	0.000613	0.87054571	2.969923

1. The regression equation

Mr = -35.01 - 0.15 (Exec) + 9.77 (South) + 1.28 (Ue) + 2.44 (Capital) + 1.92 (Pcy)

2.

orchestra answered 2 years ago

a)Develop an estimated regression equation for the data of the form ŷ = b0+b1x. Plot the standardized residuals againstŷand the normal probability plot. Do the plots support the assumptions about ε? Explain.

x y 9 10 32 20 18 21 15 16 26 22 a)Develop an estimated regression equation for the data of the form ŷ = b0+b1x. Plot the standardized residuals againstŷand the normal probability plot. Do the plots support the assumptions about ε? Explain. b)Develop an estimated regression equation for the data of the form ŷ =b0+b1x+b2x2. Plot the standardized residuals againstŷand the normal probability plot. Do the plots support the assumptions about ε? Explain. c)Use the estimated regression equation...

Let b be a parameter we want to estimate. We collect the data, and calculate an...

Let b be a parameter we want to estimate. We collect the data, and calculate an estimator for b using the data. What does it mean for this estimator to be unbiased? choose one correct answer. 1. The variance of the estimator decays to zero as we collect more and more data in the calculation. 2. The covariance between b and the estimator equals zero. 3. Th estimator becomes closer and closer to b as we collect more and more...

Which is false? A. A statistic is used to estimate an unknown parameter B. Non sampling...

Which is false? A. A statistic is used to estimate an unknown parameter B. Non sampling errors can be present even when a census is taken C. Increase sample size to reduce bias D. Margin of error will get smaller when you increase sample size E. Variability describes how spread out the values of the sample statistic are when we take many samples.

2. The Drake Equation: (a) What, exactly, does the Drake Equation estimate? (b) If you are...

2. The Drake Equation: (a) What, exactly, does the Drake Equation estimate? (b) If you are like Frank Drake or Carl Sagan, you are an optimist who believes that the development of life, intelligence, and technological civilizations are not "vanishingly small", but are typical. That is, that what happened on earth is not rare. Explain them why Drake and Sagan would say that "N=L" is a good approximation to the Drake Equation.

Parameter estimates: intercept: 14179.871 3298.484 4.30 0.0003* (estimate Std Error tRatio Prob>t ) InCostAid: 0.7768753 0.254022...

Parameter estimates: intercept: 14179.871 3298.484 4.30 0.0003* (estimate Std Error tRatio Prob>t ) InCostAid: 0.7768753 0.254022 3.06 0,0058* (estimate Std Error tRatio Prob>t ) Baruch College has substantially less average debt compared to the other schools with similar in-state costs. Figure 10.13 contains JMP output for the simple linear regression of AveDebt on InCostAid with this case removed. a) State the least-squares regression line. b) The University of North Florida is one school in this sample. It has an in-state...

a. Estimate the regression equation and determine the predicted values of y b. Use the predicted...

a. Estimate the regression equation and determine the predicted values of y b. Use the predicted values and the actual values of y to calculate the residuals c. Plot the residuals (vertical axis) against the predicted values (horizontal axis). d. Does it appear that heteroscedasticity is a problem? Explain e. Draw a histogram of the residuals. Does it appear that the errors are normally distributed? Explain. f. Use the residuals to identify possible outlier(s) Weight Price 27 12.02 28.5 12.04...

Question