Skeleton for Final Project

This file contains the barebone structure of your final output. You can make as many aesthetic changes (e.g. colors, templates, CSS, etc.) to this file as you want. Please remove all the instructions from this file and write in a comprehensive manner. Show code only where required.

Exploratory Data Analysis

A few examples. You can find more about tabs in rmarkdown here

Univariate plots

ggplot(mt,
       aes(mpg)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Most cars have a mileage between 15 - 22 miles per gallon, and very few cars are very fuel-efficient.

ggplot(mt,
       aes(disp)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Most cars have an engine volume in the range of 70 - 300 cubic inches.

Bivariate plots

We show the data in this tab.

ggplot(mt,
       aes(mpg, disp)) +
  geom_point()

Quick description.

Summary

mt %>% 
  group_by(cyl) %>% 
  summarize(mean_mpg = mean(mpg))

## # A tibble: 3 x 2
##     cyl mean_mpg
##   <dbl>    <dbl>
## 1     4     26.7
## 2     6     19.7
## 3     8     15.1

Quick description.

Response Variable

mpg is the response variable in mtcars dataset. It is not normally distributed. We are transforming it using …. We consider all mileage values greater than ________ as outliers.

Modeling

Model 1 (Provide an appropriate heading)

# some code goes here

Model 2 (Provide an appropriate heading)

# some code goes here

Model 3 (Provide an appropriate heading)

# some code goes here

Model Assessment

library(modelsummary)

# your code goes here

Model Diagnostics

Show your code. Check all assumptions.

Data Transformation and Re-fitting the Best Model

Show your code in a single chunk.