data(women)
head(women, 10)

##    height weight
## 1      58    115
## 2      59    117
## 3      60    120
## 4      61    123
## 5      62    126
## 6      63    129
## 7      64    132
## 8      65    135
## 9      66    139
## 10     67    142

The formula for a Simple Linear Regression is:

\[\hat{y} = b_0 + b_1X_1\]

Where:

\(\hat{y} = Dependent Variabl (Weight)\)

\(b_0 = y-intercept\)

\(b_1 = Slope Coefficient\)

\(X_1 = Independent Variable (Height)\)

The formula for mean is:

\[\bar{x} = \frac{\sum x_i}{n}\]

Where:

\(\bar{x}\) = The sample mean or average

\(\sum x_i\) = The sum of all values

\(n\) = The total number of observations

txhousing %>% filter(year == 2000) %>% group_by(month) %>% 
summarize(total_sales = sum(sales, na.rm = TRUE)) %>% 
ggplot(aes(x=factor(month), y = total_sales)) + geom_col() + 
labs(x = "Month", y = "Total Number of Sales", 
title = "Total Number of Home Sales in Texas for Each Month (2000)")