Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least - PowerPoint PPT Presentation

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Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least square regression line 2. Example of linear regression analysis with R 1. Review of linear regression analysis In last lecture, we learned correlation coefficient R : We also

Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least square regression line 2. Example of linear regression analysis with R
1. Review of linear regression analysis In last lecture, we learned correlation coefficient R : We also learned how to find linear regression line from summary statistics: by finding using formula Actually if we substitute R and into , is also equal to
2. Example of linear regression analysis with R Example 1 The data (“low_birth_weight_infants.txt”) is a sample of 100 low birth infants born in Boston. (1) Is there any correlation between gestational age and head circumference from a scatter plot? If there is, what is the correlation coefficient? (2) Find least squares regression line using summary statistics. (3) Find least squares regression line using R. Are they the same? (4) Plot the residuals. Do the residuals satisfy the conditions: linearity , nearly normal and constant variability? Answer: (1) > birth<-read.table("low_birth_weight_infants.txt",as.is=T,header=T,sep="\t") > head(birth) headcirc length gestage birthwt momage toxemia 1 27 41 29 1360 37 0 2 29 40 31 1490 34 0 3 30 38 33 1490 32 0 4 28 38 31 1180 37 0 5 29 38 30 1200 29 1 6 23 32 25 680 19 0
> plot(birth$headcirc~birth$gestage) 34 32 30 birth$headcirc 28 26 24 22 24 26 28 30 32 34 birth$gestage From the scatter plot, there is a strong correlation.
(2) > cor(birth$gestage, birth$headcirc) The correlation coefficient is 0.78. > mean(birth$gestage) > mean(birth$headcirc) > sd(birth$gestage) > sd(birth$headcirc) Using the results : we get The regression line from summary statistics is
(3) > birthmodel<-lm(birth$headcirc~birth$gestage) > summary(birthmodel) They are the same. (4) > residuals(birthmodel) > plot(residuals(birthmodel)) > qqnorm(residuals(birthmodel))
Normal Q-Q Plot 6 6 4 4 residuals(birthmodel) Sample Quantiles 2 2 0 0 -2 -2 0 20 40 60 80 100 -2 -1 0 1 2 Index Theoretical Quantiles The residuals qq plot looks fairly like a straight line. Also the residuals plot looks like constant variability. So the linear model fits quite well.
We can also use r to plot the least squares regression line. > plot(birth$headcirc, birth$gestage) > abline(birthmodel) 34 32 30 birth$headcirc 28 26 24 22 24 26 28 30 32 34 birth$gestage

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