Regression Modelling
STAT2008/4038/6038
R Output for Final Examination, Semester 2 2022
PRINTOUT FOR QUESTION 2
c(summary(Effort), sd(Effort))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00000 26.25000 50.50000 50.40000 75.75000 97.00000 28.45476
c(summary(Reward), sd(Reward))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.828172 29.886097 52.460727 48.655263 66.437648 95.839993 24.361503
cov(Reward, Effort)
## [1] 633.1174
1
names(bird)
## [1] “name” “extinct” “nest.pair” “size” “mig.status”
levels(bird$size)
## [1] “S” “L”
levels(bird$mig.status)
## [1] “R” “M”
bird.lm <- lm(extinct ~ nest.pair + size + mig.status, data = bird) summary(bird.lm) ## Call: ## lm(formula = extinct ~ nest.pair + size + mig.status, data = bird) ## ## Residuals: ## Min 1Q Median 3Q Max ## -14.056 -5.578 -2.485 2.942 48.313 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9062 3.2696 -0.277 0.782898
## nest.pair 2.2534 0.6361 3.543 0.000921 ***
## sizeL 6.1217 2.9401 2.082 0.042923 *
## mig.statusM
-4.9136 3.2467 -1.513 0.137017
## —
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
vcov(bird.lm) # Variance-covariance matrix
## (Intercept) nest.pair sizeL mig.statusM
## (Intercept) 10.690611 -1.50199817 -4.43874764 -4.4050888
## nest.pair -1.501998 0.40457802 0.06605802 0.3360600
## sizeL -4.438748 0.06605802 8.64447634 0.2193219
## mig.statusM -4.405089 0.33606004 0.21932190 10.5409041
anova(bird.lm)
## Analysis of Variance Table
##
## Response: extinct
## Df Sum Sq Mean Sq F value Pr(>F)
## nest.pair 1 1534.4 1534.42 14.2451 0.0004581 ***
## size 1 482.9 482.95 4.4835 0.0396600 *
## mig.status 1 246.7 246.71 2.2904 0.1370167
## Residuals 46 4954.9 107.72
## —
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1
par(mfrow = c(2, 2))
plot(bird.lm, which = 1)
plot(bird.lm, which = 2)
plot(hatvalues(bird.lm), type = “h”)
2
PR I N T OU T FOR QU EST I ON 5
abline(h = 8/length(bird$name), col = “red”)
plot(cooks.distance(bird.lm), type = “h”)
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