assignment

Strength and Distance Data

library(readr)
StrengthandDistanceData <- read_csv("/Users/juliaye/Downloads/StrengthandDistanceData.csv")

## Parsed with column specification:
## cols(
##   weightlifted = col_double(),
##   distancethrown = col_double()
## )

cor.test(StrengthandDistanceData$weightlifted,StrengthandDistanceData$distancethrown,method = "pearson", conf.level = 0.99)

## 
##  Pearson's product-moment correlation
## 
## data:  StrengthandDistanceData$weightlifted and StrengthandDistanceData$distancethrown
## t = 10.117, df = 26, p-value = 1.663e-10
## alternative hypothesis: true correlation is not equal to 0
## 99 percent confidence interval:
##  0.7265302 0.9604491
## sample estimates:
##       cor 
## 0.8929919

library(car)

## Loading required package: carData

scatterplot(StrengthandDistanceData$weightlifted,StrengthandDistanceData$distancethrown)

plot(density(StrengthandDistanceData$weightlifted))

plot(density(StrengthandDistanceData$distancethrown))

model <- lm(distancethrown~weightlifted,StrengthandDistanceData)

par(mfrow=c(2,2))
plot(model)

ncvTest(model)

## Non-constant Variance Score Test 
## Variance formula: ~ fitted.values 
## Chisquare = 1.822118    Df = 1     p = 0.1770614

qqnorm(model$residuals)
qqline(model$residuals)

shapiro.test(model$residuals)

## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.95813, p-value = 0.3146

Sugar Chewy Data

library(readxl)
SugarChewyData <- read_excel("/Users/juliaye/Downloads/SugarChewyData.xlsx")
cor.test(SugarChewyData$sugar, SugarChewyData$chewiness, method = "pearson", conf.level = 0.99)

## 
##  Pearson's product-moment correlation
## 
## data:  SugarChewyData$sugar and SugarChewyData$chewiness
## t = -6.6025, df = 88, p-value = 2.951e-09
## alternative hypothesis: true correlation is not equal to 0
## 99 percent confidence interval:
##  -0.7315074 -0.3624002
## sample estimates:
##        cor 
## -0.5755643

scatterplot(SugarChewyData$sugar, SugarChewyData$chewiness)

plot(density(SugarChewyData$sugar))

plot(density(SugarChewyData$chewiness))

Model <- lm(chewiness~sugar, SugarChewyData)
summary(Model)

## 
## Call:
## lm(formula = chewiness ~ sugar, data = SugarChewyData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4557 -0.5604  0.1045  0.5249  1.9559 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.662878   0.756610  10.128  < 2e-16 ***
## sugar       -0.022797   0.003453  -6.603 2.95e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9178 on 88 degrees of freedom
## Multiple R-squared:  0.3313, Adjusted R-squared:  0.3237 
## F-statistic: 43.59 on 1 and 88 DF,  p-value: 2.951e-09

par(mfrow=c(2,2))
plot(Model)

ncvTest(Model)

## Non-constant Variance Score Test 
## Variance formula: ~ fitted.values 
## Chisquare = 5.403637    Df = 1     p = 0.02009483

qqnorm(Model$residuals)
qqline(Model$residuals)


shapiro.test(Model$residuals)

## 
##  Shapiro-Wilk normality test
## 
## data:  Model$residuals
## W = 0.98668, p-value = 0.4935

##