Lab 8

library(tidyverse)
library(openintro)
library(tidyverse)
library(openintro)
data('hfi', package='openintro')

Exercise 1

There is 123 columns in this dataset with glimpse(hfi). (note: trust me)

Exercise 2

I would use a scatter plot to show the relationship between two or more numeric values. For the example,Pf_score has a positive linear increase with the Pf expression control

ggplot(hfi, aes(x=pf_expression_control, y=pf_score)) + geom_point()+theme_classic()

Exercise 3

There is a strong positive relationship between the pf score and the pf expression control. As pf score increase, the pf expression control increased as well. There are a few outliers between 0.0 and 2.5, but unsure of the circumstances of those areas.

Exercise 4

the line y=.4914x+ 4.6171 is the best fit for the line. The smallest sum of squares was 952.153

Exercise 5

y=5.153687+0.349862*hf_score

m1 <- lm(hf_score ~ pf_expression_control, data = hfi)
summary(m1)

## 
## Call:
## lm(formula = hf_score ~ pf_expression_control, data = hfi)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6198 -0.4908  0.1031  0.4703  2.2933 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.153687   0.046070  111.87   <2e-16 ***
## pf_expression_control 0.349862   0.008067   43.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.667 on 1376 degrees of freedom
##   (80 observations deleted due to missingness)
## Multiple R-squared:  0.5775, Adjusted R-squared:  0.5772 
## F-statistic:  1881 on 1 and 1376 DF,  p-value: < 2.2e-16

Exercise 6

They might guess the 4.6 on the pf expression control if they based it on the least squares regression line. There a point where it can be 4.6-4.606060= the residual of the

ggplot(data = hfi, aes(x = pf_expression_control, y = pf_score)) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE)

## `geom_smooth()` using formula 'y ~ x'

Exercise 7

All the points are form in a line and have no direction. It is all linear.

ggplot(data = m1, aes(x = .fitted, y = .resid)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  xlab("Fitted values") +
  ylab("Residuals")

Exercise 8

yes, the residuals meet the conditions. The histogram is unimodal but just one bucket, no clue if it’s a concern.

ggplot(data = m1, aes(x = .resid)) +
  geom_histogram(binwidth = 25) +
  xlab("Residuals")

ggplot(data = m1, aes(sample = .resid)) +
  stat_qq()

Exercise 9

The conditions are met with both plots.

Bonus questions

1. Lets see the conditions were pf expressions are killed. There seems to be a trend in the positive direction of people being killed as the pf_score increased. However, there is a lot of noise in the plot.

ggplot(data = hfi, aes(x = pf_expression_killed, y = pf_score)) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE)

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 80 rows containing non-finite values (stat_smooth).

## Warning: Removed 80 rows containing missing values (geom_point).