KidsFeet Regression Analysis Code The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following lines of code resulted in the following plot and regression analysis? Hint: More than one choice may be correct. ## ##          Simple Linear Regression ## ## Correlation coefficient r =  0.6411 ## ## Equation of Regression Line: ## ##   length = 9.817 + 1.658 * width ## ## Residual Standard Error: s   = 1.025 ## R^2 (unadjusted):        R^2 = 0.411   ( ) KidsFeetMod<-lmGC(length~width,data=KidsFeet)      KidsFeetMod   ( ) lmGC(width~length,data=KidsFeet,graph=TRUE) ( ) KidsFeetMod<-lmGC(width~length,data=KidsFeet)      KidsFeetMod ( ) lmGC(length~width,data=KidsFeet,graph=TRUE)   ( ) lmGC(width~length,data=KidsFeet) ( ) KidsFeetMod<-lmGC(width~length,data=KidsFeet,graph=TRUE)      KidsFeetMod ( ) lmGC(length~width,data=KidsFeet) ( ) KidsFeetMod<-lmGC(length~width,data=KidsFeet,graph=TRUE)      KidsFeetMod

Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter10: Sequences, Series, And Probability
Section10.3: Geometric Sequences
Problem 79E
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KidsFeet Regression Analysis Code

The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following

Research Question: How is the width of a fourth-grade student's foot related to the length?

Which of the following lines of code resulted in the following plot and regression analysis? Hint: More than one choice may be correct.

##
##          Simple Linear Regression
##
## Correlation coefficient r =  0.6411
##

## Equation of Regression Line:
##
##   length = 9.817 + 1.658 * width
##
## Residual Standard Error: s   = 1.025
## R^2 (unadjusted):        R^2 = 0.411

 

( ) KidsFeetMod<-lmGC(length~width,data=KidsFeet)

     KidsFeetMod

 

( ) lmGC(width~length,data=KidsFeet,graph=TRUE)

( ) KidsFeetMod<-lmGC(width~length,data=KidsFeet)

     KidsFeetMod

( ) lmGC(length~width,data=KidsFeet,graph=TRUE)
 
( ) lmGC(width~length,data=KidsFeet)

( ) KidsFeetMod<-lmGC(width~length,data=KidsFeet,graph=TRUE)

     KidsFeetMod

( ) lmGC(length~width,data=KidsFeet)

( ) KidsFeetMod<-lmGC(length~width,data=KidsFeet,graph=TRUE)

     KidsFeetMod

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