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Version 1.7.1  (2003-06-16)

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> invisible(options(echo = TRUE))
> data.approval <-
+ read.table(file="http://www.courses.fas.harvard.edu/~gov1000/Data/approval.asc",header=T)
> 
> #what are the variables in the approval data set?
> names(data.approval)
 [1] "Date"           "administration" "approval"       "lagApproval"   
 [5] "unrate"         "inflation"      "kennedy"        "johnson"       
 [9] "nixon"          "ford"           "carter"         "regan"         
[13] "bush"           "clinton"       
> attach(data.approval)
> 
> postscript(file="diagnostics1.ps",width=10.5,height=8,horizontal=T)
> 
> lm4  <- lm(approval~unrate+inflation+lagApproval)
> summary(lm4)

Call:
lm(formula = approval ~ unrate + inflation + lagApproval)

Residuals:
    Min      1Q  Median      3Q     Max 
-15.804  -2.844  -0.131   2.092  41.978 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  9.86277    1.66288   5.931 5.24e-09 ***
unrate      -0.13254    0.14480  -0.915 0.360393    
inflation   -0.30789    0.08749  -3.519 0.000468 ***
lagApproval  0.85999    0.02082  41.308  < 2e-16 ***
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

Residual standard error: 4.931 on 566 degrees of freedom
Multiple R-Squared: 0.8316,	Adjusted R-squared: 0.8307 
F-statistic: 931.5 on 3 and 566 DF,  p-value: < 2.2e-16 

> lm5  <- lm(approval~unrate+inflation+lagApproval+kennedy+johnson+nixon+ford+carter+regan+bush+clinton)
> summary(lm5)

Call:
lm(formula = approval ~ unrate + inflation + lagApproval + kennedy + 
    johnson + nixon + ford + carter + regan + bush + clinton)

Residuals:
     Min       1Q   Median       3Q      Max 
-16.2758  -2.7445  -0.1872   2.3064  39.2390 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 15.06193    2.13347   7.060 4.97e-12 ***
unrate      -0.60416    0.21040  -2.872  0.00424 ** 
inflation   -0.35019    0.13055  -2.682  0.00753 ** 
lagApproval  0.82526    0.02288  36.064  < 2e-16 ***
kennedy      1.19652    1.00644   1.189  0.23500    
johnson     -2.16593    0.84941  -2.550  0.01104 *  
nixon       -1.58270    0.93370  -1.695  0.09062 .  
ford         1.39494    1.44502   0.965  0.33479    
carter      -0.16646    1.33620  -0.125  0.90090    
regan        0.62330    0.97021   0.642  0.52085    
bush         0.52829    0.98643   0.536  0.59248    
clinton     -1.30218    0.75310  -1.729  0.08435 .  
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

Residual standard error: 4.895 on 558 degrees of freedom
Multiple R-Squared: 0.8364,	Adjusted R-squared: 0.8331 
F-statistic: 259.3 on 11 and 558 DF,  p-value: < 2.2e-16 

> 
> plot(lm4$residual,ylab="Residuals",xlab="Observation Number")
> hist(lm4$residual,xlab="Residuals")
> plot(approval,lm4$residual,xlab="Approval",ylab="Residual")
> 
> #scale the residuals to have variance 1
> lm4.residual.scaled  <- lm4$residual/sd(lm4$residual)
> plot(lm4.residual.scaled,ylab="Scaled Residuals",xlab="Observation Number")
> hist(lm4.residual.scaled,xlab="Scaled Residuals")
> plot(approval,lm4.residual.scaled,xlab="Approval",ylab="Scaled Residual")
> 
> qqnorm(lm4$residual)
> qqline(lm4$residual)
> 
> plot(lm5$residual,ylab="Residuals",xlab="Observation Number")
> hist(lm5$residual,xlab="Residuals")
> plot(approval,lm5$residual,xlab="Approval",ylab="Residual")
> 
> #scale the residuals to have variance 1
> lm5.residual.scaled  <- lm5$residual/sd(lm5$residual)
> plot(lm5.residual.scaled,ylab="Scaled Residuals",xlab="Observation Number")
> hist(lm5.residual.scaled,xlab="Scaled Residuals")
> plot(approval,lm5.residual.scaled,xlab="Approval",ylab="Scaled Residual")
> 
> qqnorm(lm5$residual)
> qqline(lm5$residual)
> 
> 
> dev.off()
null device 
          1 
> proc.time()
[1] 1.21 0.04 1.62 0.00 0.00
>