{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

SlidesRLec7

# 12 aircraft weight vs takeo distance normal qq plot

This preview shows page 1. Sign up to view the full content.

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

Unformatted text preview: ) with(spirit,plot(headwind,fit\$residuals)) abline(h=0) 11 6000 6000 Aircraft Weight vs Takeo Distance q 5000 5000 q q q 4000 q q q q 3000 q q 3000 q weight 4000 weight q q q q q q 2000 2000 q 500 1000 1500 2000 TO.distance 2500 3000 200 500 1000 2000 TO.distance Red dots were taken from Chief Engineer Hall's curve t plot. 12 Aircraft Weight vs Takeo Distance Normal Q−Q Plot 0.002 q 0.002 q q q q q q q −0.002 q −0.004 −0.002 q −1.0 −0.5 0.0 0.5 Theoretical Quantiles 13 0.000 fit\$residuals 0.000 q q −0.004 Sample Quantiles q 1.0 q 0 2 4 headwind 6 8 Comments log = "xy" # plots both axes on log10 basis, note ticks log10 (y ) = α + β log10 (x ) ⇐⇒ y = 10α · x β It appears that the residuals from the linear t show no structure relative to Given that both x headwind. and y are on a log10 basis one should account for uncertainty in extrapolating to the fully loaded takeo weight of 5135 lbs For more on the curve tting of yesteryear see http://www.stat.washington.edu/fritz/Reports/Daytonnew0.pdf 14 Reported Measurements The data set is Davis from the car package Companion for Applied Regression by John Fox. The idea is to check the reliability of self report. install.packages("car") library(car) per R session. followed by > names(Davis) [1] "sex" "weight" "height" "repwt" "repht" > attach(Davis) # allows using weight in place of Davis\$weight > Davis.model <- lm(weight~re...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online