Discussion 13

Haiding Luo

2023 11 27

1.Pick any two quantitative variables from a data set that interests you.  If you are at a loss, look at the internal R datasets

Links to an external site. in base R and choose any.  EG df <- cars

library(ggplot2)
Lebron <- read.csv("C:/Users/pokem/OneDrive/文档/lebron_playoffs.csv")
y <- Lebron$pts
x <- Lebron$three
model <- lm(y ~ x, data = Lebron)
summary(model)

## 
## Call:
## lm(formula = y ~ x, data = Lebron)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.2134  -5.2134  -0.4116   4.7866  20.4849 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  24.5151     0.6819  35.954  < 2e-16 ***
## x             2.6982     0.3223   8.373 3.61e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.24 on 258 degrees of freedom
## Multiple R-squared:  0.2137, Adjusted R-squared:  0.2106 
## F-statistic: 70.11 on 1 and 258 DF,  p-value: 3.614e-15

print(model)

## 
## Call:
## lm(formula = y ~ x, data = Lebron)
## 
## Coefficients:
## (Intercept)            x  
##      24.515        2.698

The intercept value of 24 means that when the independent variable x is zero, the predicted value of the dependent variable y is 24. If the intercept is 24, it implies that when the independent variable is zero, the predicted value of the dependent variable is 24.

3.If I want to study the relationship between a basketball player’s scoring and the number of three-point shots made in a game, “PTS” would be the dependent variable, as I am interested in understanding how scoring varies with the change in the number of three-point shots. The independent variable, in this case, would be the number of three-point shots made.

4.

cov1 <- cov(x, y)
var1 <- var(x)
a <- cov1/ var1
a

## [1] 2.698226

plot(x, y, main = "Three-pointer and total points", xlab = "Three point shot made", ylab = "total points")
abline(model, col = 'red')

5.A positive slope indicates a positive correlation between the dependent variable yand the independent variable x. When the independent variable x increases, the dependent variable y also increases accordingly.