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For expert using R sorry I post this question without data before?

Simple Linear Regression and Polynomial Regression

# HW 2

#

# Read data from csv file

data <- read.csv("C:dataSweetPotatoFirmness.csv",header=TRUE, sep=",")

head(data)

str(data)

# scatterplot of independent and dependent variables

plot(data$pectin,data$firmness,xlab="Pectin, %",ylab="Firmness")

par(mfrow = c(2, 2)) # Split the plotting panel into a 2 x 2 grid

model <- lm(firmness ~ pectin , data=data)

summary(model)

anova(model)

plot(model)

shapiro.test(resid(model))

# examine histogram and boxplot of residuals

par(mfrow = c(1, 1))

hist(resid(model))

boxplot(resid(model))

# predict dependent variable for specified value of independent variable

predict(model, data.frame(pectin = 1.5))

# Estimated regression line and scatterplot of data

plot(data$pectin,data$firmness,xlab="Pectin, %", ylab="Firmness",

ylim=c(45,75),xlim=c(0,3),main="Simple Linear Regression",

pch=19,cex=1.5)

lines(sort(data$pectin),fitted(model)[order(data$pectin)], col="blue", type="l")

par(mfrow = c(2, 2))

# fit a second degree polynomial

# create quadratic term for pectin

data$pectinSq <- data$pectin^2

model2 <- lm(firmness ~ pectin + pectinSq , data=data)

summary(model2)

anova(model2)

plot(model2)

shapiro.test(resid(model2))

par(mfrow = c(1, 1))

hist(resid(model2))

boxplot(resid(model2))

# predict dependent variable for specified value of independent variable

predict(model2, data.frame(pectin = 1.5, pectinSq=2.25))

# Estimated regression line and scatterplot of data

plot(data$pectin,data$firmness,xlab="Pectin, %", ylab="Firmness",

ylim=c(45,75),xlim=c(0,3),main="Simple Linear Regression",

pch=19,cex=1.5)

lines(sort(data$pectin),fitted(model2)[order(data$pectin)], col="blue", type="l")

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