Hi Dad, This is a test document that I am running to experiment if this webpage can be shared publicly. Rpubs is a tool to share your work and code with other people and make your research reproducible.

Can you let me know if you can see this on your side?

Point 1: The Data

head(cars)

Point 2: The Visual

cars %>% ggplot(
  aes(x=speed, y=dist)) + 
  geom_point(color = "darkblue") + 
  theme_light() + 
  labs(x= "Distance Traveled", 
       y = "Car Speed", 
       title = "The Affect of Car Speed on Distance Traveled")

Point 3: The Stats

model = lm(speed ~ dist, data= cars)
summary(model)
## 
## Call:
## lm(formula = speed ~ dist, data = cars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.5293 -2.1550  0.3615  2.4377  6.4179 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.28391    0.87438   9.474 1.44e-12 ***
## dist         0.16557    0.01749   9.464 1.49e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.156 on 48 degrees of freedom
## Multiple R-squared:  0.6511, Adjusted R-squared:  0.6438 
## F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12
  • Based on the 1.49e-12 P value: We see that distance is a significant predictor of speed. In other words, when the distance that a car travels increases, its speed often increases with it
  • A linear relationship between the two variables has been identified.