Assignments

Reading 1

Due: September 11 at 03:00 PM

Reading assignment 1 about using R, RStudio, and RMarkdown

Reading 2

Due: September 18 at 03:00 PM

Reading assignment 2 about the basics of ggplot2

Reading 3

Due: September 19 at 11:59 PM

Reading assignment 3 about additional options in ggplot2

Reading 4

Due: September 20 at 11:59 PM

Reading assignment 4 about RStudio workflow and data filtering

Reading 5

Due: September 25 at 11:59 PM

Reading assignment 5 about data transformation

Reading 6

Due: September 26 at 11:59 PM

Reading assignment 6 about tibbles and importing data

Reading 7

Due: October 02 at 03:00 PM

Reading assignment 7 about exploratory data analysis

Reading 8

Due: October 03 at 11:59 PM

Reading assignment 8 about tidy data

Reading 9

Due: October 04 at 03:00 PM

Reading assignment 9 about manipulating dates and times with the lubridate package

Reading 10

Due: October 10 at 03:00 PM

Reading assignment 10 about using the forcats package to handle the factor data type

Reading 11

Due: October 10 at 03:00 PM

Reading assignment 11 about programming in R and information about the pipe (%>%) operator.

Reading 12

Due: October 18 at 03:00 PM

Reading assignment 12 about programming in R and information about the pipe (%>%) operator.

Reading 13

Due: October 18 at 03:00 PM

Reading assignment 13 about defining your very own custom functions (commands) in R

Reading 14

Due: October 25 at 03:00 PM

Reading assignment 14 about experimental data collection and examining numerical data (from a statistics POV)

Reading 15

Due: October 31 at 11:59 PM

Reading assignment 15 about how to compare categorical and grouped numerical data, and new ways to represent and compare univariate data

Reading 16

Due: November 01 at 03:00 PM

Reading assignment 16 presents two simulation-based case studies about statistical inference

Reading 17

Due: November 06 at 03:00 PM

Reading assignment 17 discusses hypothesis testing, p-values, and statistical significance

Reading 18

Due: November 20 at 03:00 PM

Reading assignment 18 discusses the normal distribution

Reading 19

Due: November 27 at 03:00 PM

Reading assignment 19 discusses using the normal distribution as a model and *p*-hacking

Reading 20

Due: December 06 at 03:00 PM

Reading assignment 20 discusses how to create models the tidyverse way

Reading 21

Due: December 06 at 03:00 PM

Reading assignment 21 discusses the statistical foundations for linear regression