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summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

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Section 1

This section will show you…

Section 2

This is the data set that we found

section 3

This analysis shows us that we had the right assumptions. We used google to find a lot of our data.

Here is a bolded word, an italic word, a R object, a superscript2, and a subscript2

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.0      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
mtcars %>% ggplot(aes(x = mpg, y = hp)) + geom_point()

xbar <- 2
se <- 1.3

The 95% confidence interval for the mean is (-0.548, 4.548)

# Nicely format column names
my.table <- mtcars[1:5, 1:4]
colnames(my.table) <- c("MPG", "Cylinders", 
                        "Displacement", "Horsepower")
knitr::kable(my.table, digits = c(0, 1, 0, 0, 0), 
             align = c('l', 'r', 'r', 'r', 'r'), 
             row.names = TRUE)
MPG Cylinders Displacement Horsepower
Mazda RX4 21 6 160 110
Mazda RX4 Wag 21 6 160 110
Datsun 710 23 4 108 93
Hornet 4 Drive 21 6 258 110
Hornet Sportabout 19 8 360 175 
flextable::flextable(my.table, cwidth = 1.15)