```
# Reading csv file into R environment
mydata <- read.csv("example1.csv", header=TRUE)
# Attaching dataset to global environment
attach(mydata)
```

## The following objects are masked from mydata (pos = 3): ## ## Case, Height_in_Inches, Weight_in_Pounds ## ## The following objects are masked from mydata (pos = 6): ## ## Case, Height_in_Inches, Weight_in_Pounds ## ## The following objects are masked from mydata (pos = 7): ## ## Case, Height_in_Inches, Weight_in_Pounds ## ## The following objects are masked from mydata (pos = 8): ## ## Case, Height_in_Inches, Weight_in_Pounds

```
# Displaying header names
names(mydata)
```

## [1] "Case" "Height_in_Inches" "Weight_in_Pounds"

```
# Summarizing the data
summary(mydata)
```

## Case Height_in_Inches Weight_in_Pounds ## Min. : 1 Min. :60.3 Min. : 78 ## 1st Qu.: 6251 1st Qu.:66.7 1st Qu.:119 ## Median :12500 Median :68.0 Median :127 ## Mean :12500 Mean :68.0 Mean :127 ## 3rd Qu.:18750 3rd Qu.:69.3 3rd Qu.:135 ## Max. :25000 Max. :75.2 Max. :171

```
# Plotting data
plot(Height_in_Inches, Weight_in_Pounds, main="Weight VS Height")
```

```
# The scatterplot shows a fairly strong and reasonably linear relationship between the two variables.
# A Pearson correlation coefficient can be calculated using the cor( ) function
cor(Height_in_Inches, Weight_in_Pounds)
```

## [1] 0.5029

```
# Pearson's r = 0.5028585 means positive correlation exists among the variables
# We can also perform correlation test using:
cor.test(Height_in_Inches, Weight_in_Pounds)
```

## ## Pearson's product-moment correlation ## ## data: Height_in_Inches and Weight_in_Pounds ## t = 91.98, df = 24998, p-value < 2.2e-16 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## 0.4935 0.5121 ## sample estimates: ## cor ## 0.5029

```
# The function does a t-test with a 95% confidence interval for the population correlation
# You can set "conf.level= " to change the confidence level, e.g.
cor.test(Height_in_Inches, Weight_in_Pounds, conf.level=0.99)
```

## ## Pearson's product-moment correlation ## ## data: Height_in_Inches and Weight_in_Pounds ## t = 91.98, df = 24998, p-value < 2.2e-16 ## alternative hypothesis: true correlation is not equal to 0 ## 99 percent confidence interval: ## 0.4906 0.5149 ## sample estimates: ## cor ## 0.5029

```
# Since p-value is less than <0.05, the test suggests a strong positive correlation
# Performing simple linear regression using lm() function
# In R, lm() function for simple regression takes the form Weight ~ Height, which means something like "Weight as a function of Height" or "Weight as predicted by Height" etc.
fit <- lm(Weight_in_Pounds ~ Height_in_Inches)
# Viewing the results
fit
```

## ## Call: ## lm(formula = Weight_in_Pounds ~ Height_in_Inches) ## ## Coefficients: ## (Intercept) Height_in_Inches ## -82.58 3.08

```
# Better results can be viewed using:
summary(fit)
```

## ## Call: ## lm(formula = Weight_in_Pounds ~ Height_in_Inches) ## ## Residuals: ## Min 1Q Median 3Q Max ## -40.30 -6.71 -0.05 6.81 39.09 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -82.5757 2.2802 -36.2 <2e-16 *** ## Height_in_Inches 3.0835 0.0335 92.0 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 10.1 on 24998 degrees of freedom ## Multiple R-squared: 0.253, Adjusted R-squared: 0.253 ## F-statistic: 8.46e+03 on 1 and 24998 DF, p-value: <2e-16

```
# Clearly we can see that the coefficients are highly significant (three stars)
# Also p-value being <0.05 (in fact, p-value is less than 2.2e-16), so we would reject the hypothesis that the slope is zero.
# Framing the equation:
# Weight_in_Pounds = -82.57574 + 3.08348* Height_in_Inches
# Plotting regression line
plot(Weight_in_Pounds ~ Height_in_Inches, main="Weight Vs. Height")
abline(fit, col="red")
```

```
# Plotting residuals
par(mfrow=c(2,2))
plot(fit)
```

```
# Predicting from the model fits (regression)
head(predict(fit))
```

## 1 2 3 4 5 6 ## 120.3 137.9 131.4 127.8 126.4 129.3

```
head(predict(fit, interval = "confidence"))
```

## fit lwr upr ## 1 120.3 120.1 120.5 ## 2 137.9 137.7 138.2 ## 3 131.4 131.3 131.6 ## 4 127.8 127.6 127.9 ## 5 126.4 126.3 126.6 ## 6 129.3 129.1 129.4

```
# Using prediction equation to predict weight for a given height (e.g. 80 Inches)
newheight <- data.frame(Height_in_Inches = 80)
predict(fit, newheight, interval="predict")
```

## fit lwr upr ## 1 164.1 144.3 183.9

```
# The 95% prediction interval of the weight for the given height of 80 inches is between 144.3298 and 183.875 Pounds
```