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The formula used to compute a large-sample con- fidence

Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition | ISBN: 9780495118732 | Authors: Roxy Peck, Chris Olsen, Jay L. Devore ISBN: 9780495118732 197

Solution for problem 9.12 Chapter Chapter 9

Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition

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Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition | ISBN: 9780495118732 | Authors: Roxy Peck, Chris Olsen, Jay L. Devore

Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition

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Problem 9.12

The formula used to compute a large-sample con- fidence interval for p is What is the appropriate z critical value for each of the following confidence levels? a. 95% d. 80% b. 90% e. 85% c. 99%

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#data analysis using hamilton data to predict y data=read.csv("hamilton.csv") #wewill check the performance of first-order model m1=lm(y~x1+x2, data) summary(m1) par(mfrow=c(1,3)) plot(data[,1], m1$res) plot(data[,2],m1$res) plot(fitted(m1),m1$res) #I did not observe an obvious pattern in the residual plots #we will present the partial residual plots on x1 partial=m1$res+coef(m1)[2]*data[,1] plot(data[,1], partial,xlab='x1',ylab="partial residual") #partial residual plot show strict linear line #there must be violation on the assumptions to do linear regression install.packages("car") install.packages("MASS") library("car") library('MASS') vif(m1) cor.test(data[,1], data[,2])$p.value #x1 and x2 are significantly correlated #we will apply rid

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Chapter Chapter 9, Problem 9.12 is Solved
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Textbook: Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW)
Edition: 3
Author: Roxy Peck, Chris Olsen, Jay L. Devore
ISBN: 9780495118732

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The formula used to compute a large-sample con- fidence