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How do we determine the rest of the relevant results

Assignment Help Basic Statistics
Reference no: EM132069453

After reviewing the Stata data, the current political climate, I was interested to identify a relationship (if any) between federal spending on border security and vote choice, the premise being that republicans and democrats view border security differently.

Run the initial linear probability model regression:

reg who2012 fedspend_bordercongress_therm male fedspend_terrorismfedspend_schools independent republican, r

Linear regression

 

Number of obs

= 719

 

F(7, 711)

= 231.88

 

Prob > F

= 0.0000

 

R-squared

= 0.6108

 

Root MSE

= .29704

 

 

 

 

Robust

who2012

Coef.

Std. Err.

t P>t

[95% Conf. Interval]

 

 

 

fedspend_border

.0147129

.0074025

1.99 0.047

.0001795 .0292464

congress_therm

.0021285

.0005851

3.64 0.000

.0009798 .0032773

male

.0471352

.0231848

2.03 0.042

.0016162 .0926541

fedspend_terrorism

.0188195

.0062908

2.99 0.003

.0064687 .0311702

fedspend_schools

-.0334393

.0093583

-3.57 0.000

-.0518125 -.015066

independent

-.2529824

.0633581

-3.99 0.000

-.3773738 -.128591

republican

-.7149579

.033041

-21.64 0.000

-.7798275 -.6500883

_cons

.7257504

.0561219

12.93 0.000

.615566 .8359348

 

 

 

Based on these results, it looks like there is a positive increase in the probability of voting for Obama (democrat) as the value of fedspend_border increases, which actually corresponds to a decrease in spending. However, the magnitude is small.

To test an LPM predicted value, we'll use the following characteristics:
Male, independent, rates Congress at 50, believes terrorism spending and school spending should remain constant, and believes border security spending should be increased a great deal
To find the z value:
scalar z = _b[_cons] + _b[fedspend_border]*1 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z
This particular example gives us the percentage probability that the voter will select Obama.
Now we can find the probability of someone with similar characteristics, BUT a change in his opinion of border spending, in this case, someone who believes spending should remain the same (fedspend_border = 4):
scalar z = _b[_cons] + _b[fedspend_border]*4 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z
Now we can find the probability of someone with similar characteristics, BUT a change in his opinion of border spending, in this case, someone who believes spending should decrease a great deal (fedspend_border = 7):
scalar z = _b[_cons] + _b[fedspend_border]*7 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z

These are all of our Linear Probability Model values.

Now, let's determine the probit model results:

probit who2012 fedspend_bordercongress_therm male fedspend_terrorismfedspend_schools independent republican, r

Iteration 0: log pseudolikelihood = -460.59874

Iteration 1: log pseudolikelihood = -216.29088

Iteration 2: log pseudolikelihood = -212.61059

Iteration 3: log pseudolikelihood = -212.59664

Iteration 4: log pseudolikelihood = -212.59663

Probit regression Number of obs

= 719

Wald chi2(7)

= 313.68

Prob > chi2

= 0.0000

Log pseudolikelihood = -212.59663 Pseudo R2

= 0.5384

 

 

Robust

who2012 Coef. Std. Err. z P>z

[95% Conf. Interval]

 

fedspend_border .0765934 .0458889 1.67 0.095

-.0133472 .1665339

congress_therm .0111291 .0035811 3.11 0.002

.0041103 .018148

male .2450341 .1430008 1.71 0.087

-.0352423 .5253105

fedspend_terrorism .1208896 .0412771 2.93 0.003

.039988 .2017912

fedspend_schools -.1820228 .0476661 -3.82 0.000

-.2754467 -.0885988

independent -.9743602 .20477 -4.76 0.000

-1.375702 -.5730184

republican -2.39355 .1491906 -16.04 0.000

-2.685958 -2.101142

_cons .4506491 .3076945 1.46 0.143

-.1524211 1.053719

 

We can now use the same Stata scalar command, however, to determine the probit value, we must use:
display normprob(z)

How do we determine the rest of the relevant results and what does the final table look like?

Verified Expert

In order to determine which variables provided significant results, we need to look into the coefficients table, where all the variables, fedspend_border, congress_therm, male, fedspend_terrorism, fedspend_schools, independent and republican are significant predictors of the dependent variable who2012.

Reference no: EM132069453

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inf2069453

10/7/2018 9:05:57 PM

Perfect solution.Best service !! Gave them a timeline I couldn't meet but they could, thanks again Thank you team

inf2069453

10/7/2018 9:05:07 PM

Based on these results, it looks like there is a positive increase in the probability of voting for Obama (democrat) as the value of fedspend_border increases, which actually corresponds to a decrease in spending. However, the magnitude is small.To test an LPM predicted value, we’ll use the following characteristics: Male, independent, rates Congress at 50, believes terrorism spending and school spending should remain constant, and believes border security spending should be increased a great deal

inf2069453

10/7/2018 9:04:31 PM

After reviewing the Stata data, the current political climate, I was interested to identify a relationship (if any) between federal spending on border security and vote choice, the premise being that Republicans and Democrats view border security differently.Run the initial linear probability model regression

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