Factors Affecting Occupational Prestige Score Paper

Question Description

Project Guidelines on page 1
Exercise: Law School regression and prediction on page 3
Directions: Please read project guidelines for 50% of final exam. Then use Stata 16 to predict a law school candidates probability of acceptance to her school. Answer is at end of file ===> Answer: .9329047

Project Guidelines for Econ2300 Spring,

New data from 2018 is now available for the General Social Survey. This update features continuation of many long-running GSS trends, as well as modules on mental health stigma, working life, abortion attitudes, the natural environment, and pets. The data also features two ISSP modules, on Religion and Social Networks.

Directions:

1. Use the General Social Survey for 2018 for your data

2. Briefly examine the n = 1,065 variables in the dataset.

You may go to the Codebook for GSS 2018 for more information on a particular

variable.

3. Based on your variable searching, select a topic of your own unique choosing.

Example: You may be interested in what factors would be associated with gun

ownership. There are several variables in the gss2018 which are related to this

issue. For illustration purposes I placed owngun at the top of the list of

variables using the Stata command:

. order owngun

Next: judiciously select the appropriate variables which you believe have an

effect on gun ownership such as age, education level etc, and formulate

and run a regression equation like:

owngun = f( age, education, …..)

using the correct Stata 16 syntax: .reg y x1 x2 x3 … xk

4. Next explain using regression diagnostics how well the regression model

fits the data: R-squared, t – statistics, F statistic, p-values, variance

inflation factor ( = vif ) etc. from Daniels chapters 12 and 13.

5. Please send: An email to me with an attached 3 pages or less

picture either from a smartphone or pdf copy, of your Stata printout and

additional information illustrating your understanding of Fisher’s 5-step

procedure.

Note – merely displaying a regression equation alone will earn only a satisfactory grade. To receive maximum points the presentation should address the background of the problem, the theory you are testing, empirical results, possible problems with the study, additional comments, etc.

Your project will be judged based primarily on your demonstrated understanding of the regression technique as illustrated in Chapter 15 of Daniels/Minot: Writing a Research Paper.

____________

Exercise: Law School Prediction Model: Predict the probability of being accepted at a particular law school based on the following:

person = 41 gpa = 3.3 lsat = 170 school = 1

Use the Stata 16 syntax below:

. reg admit gpa lsat school

Source | SS df MS Number of obs = 40

————-+———————————- F(3, 36) = 17.36

Model | 5.89825131 3 1.96608377 Prob > F = 0.0000

Residual | 4.07674869 36 .113243019 R-squared = 0.5913

————-+———————————- Adj R-squared = 0.5572

Total | 9.975 39 .255769231 Root MSE = .33652

——————————————————————————

admit | Coef. Std. Err. t P>|t| [95% Conf. Interval]

————-+—————————————————————-

gpa | .3926845 .1314067 2.99 0.005 .1261793 .6591897

lsat | .0179389 .0074419 2.41 0.021 .0028462 .0330317

school | .5055331 .1411676 3.58 0.001 .2192319 .7918342

_cons | -3.918106 1.258275 -3.11 0.004 -6.470005 -1.366207

——————————————————————————

. predict prob_of_admit

person gpa lsat school admit prob_of_admit

1 3.4 152 0 0 .1437394

2 3.8 164 1 1 1.021613

3 3 168 0 0 .2736885

4 2.8 175 1 1 .8262572

5 3.92 156 0 1 .419691

6 3.1 178 1 1 .9978793

7 2.5 148 0 0 -.2814325

8 2.95 155 1 0 .5263812

9 3.51 161 0 0 .348385

10 3.5 154 0 0 .2188856

11 3.4 152 0 0 .1437394

12 3.8 164 1 1 1.021613

13 3 168 0 0 .2736885

14 3.2 172 1 1 .9295142

15 3.92 156 0 1 .419691

16 3.1 178 1 1 .9978793

17 2.75 149 0 0 -.1653224

18 2.95 155 1 0 .5263812

19 3.47 159 0 0 .2967998

20 3.77 158 0 0 .3966662

21 3.4 152 0 0 .1437394

22 3.8 164 1 1 1.021613

23 3 168 0 1 .2736885

24 2.8 175 1 1 .8262572

25 3.92 156 0 1 .419691

26 3.1 178 1 1 .9978793

27 2.5 148 0 0 -.2814325

28 2.95 172 1 1 .8313431

29 3.51 161 0 0 .348385

30 3.5 154 0 0 .2188856

31 3.4 152 0 0 .1437394

32 3.8 164 1 1 1.021613

33 3 168 0 0 .2736885

34 3.2 172 1 1 .9295142

35 3.98 156 0 1 .4432521

36 3.1 178 1 1 .9978793

37 2.75 149 0 0 -.1653224

38 2.95 155 1 1 .5263812

39 3.47 159 0 0 .2967998

40 3.77 158 0 0 .3966662

________________________________________________

Predicted Value with: person 41 gpa = 3.3 lsat = 170 school = 1

Now add the values above, and re-run the following regression:

. reg admit gpa lsat school

. predict newpredict

__________

person gpa lsat school admit prob_of_admit newpredict

35 3.98 156 0 1 .4432521 .4432521

36 3.1 178 1 1 .9978793 .9978793

37 2.75 149 0 0 -.1653224 -.1653224

38 2.95 155 1 1 .5263812 .5263812

39 3.47 159 0 0 .2967998 .2967998

40 3.77 158 0 0 .3966662 .3966662

41 3.3 170 1 .9329047

Thinking ===> Examine the predicted probabilities. Do you see anything amiss? Possibly the limits of the predicted probabilities are no longer 0 to 1?

We address this problem next week in Chapter 14 of Daniels and Minot: Regression Analysis with Categorical Dependent Variables.We will introduce an important new technique called logistic regression.

April 24 Activity will discuss Regression Diagnostics.





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