You can find the hw-01 repo in the sta210-sp21 organization on GitHub. This repo contains the starter documents and dataset needed to complete the lab.
See the Lab 01 instructions for more details about cloning the repo, starting a new RStudio project, and configuring git.
Here are some tips as you complete HW 01:
We will use the following packages in this assignment:
library(tidyverse)
library(broom)
library(knitr)For this assignment, we will analyze characteristics of bookcases and shelves sold at Ikea furniture. This data is a subset of the Ikea data obtained from TidyTuesday, including only furniture pieces categorized as “Bookcases & shelving units” that have reported values for height and width.
The data is in the file ikea-bookcases-shelves.csv in the data folder.
The variable definitions are as follows:
| variable | class | description |
|---|---|---|
| item_id | double | item id which can be used later to merge with other IKEA data frames |
| name | character | the commercial name of items |
| category | character | the furniture category that the item belongs to (Sofas, beds, chairs, Trolleys,…) |
| sellable_online | logical | Sellable online TRUE or FALSE |
| link | character | the web link of the item |
| other_colors | character | if other colors are available for the item, or just one color as displayed in the website (Boolean) |
| short_description | character | a brief description of the item |
| designer | character | The name of the designer who designed the item. this is extracted from the full_description column. |
| depth | double | Depth of the item in Centimeter |
| height | double | Height of the item in Centimeter |
| width | double | Width of the item in Centimeter |
| price_usd | double | the current price in US dollars as it is shown in the website by 4/20/2020 |
This section of homework contains short answer questions focused on the concepts discussed in class. Some of these questions may also include short computing tasks.
All narrative should be written in complete sentences, and all visualizations should have informative titles and axis labels.
The objective of this analysis is to examine the relationship between the width and height of bookcases and shelves sold at Ikea. More specifically, we’d like to fit a model that could be used to predict the height of an Ikea bookcase or shelf given its width.
Create a visualization for the distribution of the response variable height and calculate the appropriate summary statistics. Use the visualization and summary statistics to describe the distribution.
Create a visualization displaying the relationship between the height and width. Interpret the visualization in the context of the data.
Write the equation of the statistical model using mathematical notation.
Fit the regression model. (You can use the R functions to fit the model; you do not need to calculate the slope and intercept by hand).
Does it make sense to interpret the intercept? If so, write the interpretation in the context of the data. Otherwise, briefly explain why not.
Interpret the slope in the context of the data.
Interpret the 95% confidence interval for the slope in the context of the data.
We want to test the following hypotheses: \(H_0: \beta_1 = 0 \text{ vs. } H_a: \beta_1 \neq 0\).
Part of a statistician’s / data scientist’s job is being able to communicate technical results to a general (often non-technical) audience. This section of the homework will focus specifically on communication and writing some of the results from Part 1 for a general audience.
We’ll start by focusing on how to display visualizations and tables in a clear and professional format suitable for a report or presentation.
Sometimes you need to change the look of a plot to make it fit with the theme, color scheme, etc. of a report or presentation. Use the visualization from Question 2 and change it’s look by doing the following:
When we write results in a report or presentation, we want to display tables in a way that’s easily readable. To do so, we can use the kable function from the knitr R package. Use the kable function to format the regression output from Question 4. In your output, include the following:
See Chapter 10.1: The function knitr::kable() for more details about the kable function.
Knit, commit, and push your final changes to your GitHub repo. Then, submit the PDF on Gradescope. See the Lab 01 for more detailed submission instructions.
| Total | 50 |
|---|---|
| Part 1: Conceptual questions | 35 |
| Part 2: Communicating results | 10 |
| Document neatly formatted with clear question headers | 3 |
| At least 3 informative commit messages | 2 |
Note there is a 10% penalty if the .pdf file is incomplete and the .Rmd file has to be knitted for grading.