{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

ch2problem29-rcode

# ch2problem29-rcode - Chapter 2 Problem 29 The data for this...

This preview shows pages 1–6. Sign up to view the full content.

Sheet1 Page 1 # Chapter 2, Problem 29 # The data for this problem are in from Chp 1, problem 27 names(ch1pr27.dat) <- c("mass","age") attach(ch1pr27.dat) # The fitted regression line is fit.1.27 fit.1.27 <- lm(mass~age) # Part a. par(mfrow=c(2,2)) plot(age,mass-fit.1.27\$fit,pch=16,xlab="Age",ylab="y-y.hat",ylim=c(-23,25)) abline(h=10,lty=3) abline(h=-10,lty=3) abline(h=5,lty=2) abline(h=-5,lty=2) title("Evaluating elements of SSE") plot(age,fit.1.27\$fit-mean(mass),pch=16,xlab="Age",ylab="y.hat-mean(y)",ylim=c(-23,25)) abline(h=10,lty=3) abline(h=-10,lty=3) abline(h=5,lty=2) abline(h=-5,lty=2) title("Evaluating elements of SSR") # The deviations (or residuals) have an overall smaller magnitude # than the deviations of y.hat-mean(y). This means that the # SSR will be larger than SSE # difference is quite substantial and, hence, R^2 will be large. # Part b. anova(fit.1.27) # Analysis of Variance Table # Response: y # Df Sum Sq Mean Sq F value Pr(>F) # age 1 11627.5 11627.5 174.06 < 2.2e-16 *** # Residuals 58 3874.4 66.8 # --- # Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # Note that the row for "Total" is not given on this output. # Make sure you include this row when you write up your homework! # Part c. # Hypotheses: # H0: beta_1 = 0 # Ha: beta_1 != 0 # Test Statistic # F* = 174.06 (from above)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}