drop _all
capture log close
capture log using linear1.log, replace

use nhanesrh2
* look only at adults
drop if age < 18
sum sbp
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***************************************
*       ANALYSIS PLAN
*          Y   = sbp
*  Key IndVars = age & female
*   Covariates = educ3 bmi dm
***************************************
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global all "sbp age female educ3 bmi dm"

global x "age female i.educ3 bmi dm"
global x2 "age i.educ3 bmi dm"

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pwcorr $all
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* 
ttest sbp, by(female)
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* Model 1 (Sex)

reg sbp female, beta
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* Model 2 (Age)
reg sbp age, beta
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* Model 3 (Age & Sex)
reg sbp age female, beta
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* Full Model (Age & Sex plus all covariates)
xi: reg sbp $x, beta
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* Reformat command
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reformat
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* Review of Dummy variables
* Which variable to omit?
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tab educ3

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char educ3[omit] 3
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xi: reg sbp i.educ3
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*
* Posterior Predictions using the predict & adjust commands
* 
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reg sbp age female
predict yhat if e(sample)
sum sbp yhat
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list age female yhat sbp in 1/10

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rvfplot

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* What does the adjust command do?
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reg sbp age female
adjust age if e(sample), by(female)
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adjust age = 40 if e(sample), by(female)
adjust age = 60 if e(sample), by(female)
adjust age = 80 if e(sample), by(female)

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* Review of Interaction (Conditional) Effects
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* Stratified Analysis to see if effects vary by sex?
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xi: reg sbp $x2 if female ==0, beta
xi: reg sbp $x2 if female ==1, beta

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* Interaction term to see if effect varies by sex?
gen agefem = age*female

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reg sbp age female agefem

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adjust age = 30 agefem=0 if e(sample) & female==0, by(female)
adjust age = 30 agefem=30 if e(sample) & female==1, by(female)

adjust age = 70 agefem=0 if e(sample) & female==0, by(female)
adjust age = 70 agefem=70 if e(sample) & female==1, by(female)

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* Check for multicollinearity
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pwcorr $all
quietly xi: reg sbp $x, beta
vif
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