University of South Carolina

• STAT 530

• --- title: "Foundations for inference - Sampling distributions" output: statsr:::statswithr_lab runtime: shiny ---

  Complete all **Exercises**, and submit answers to **Questions** on the Coursera platform.

  ## Getting Started ### Load packages In this lab we will explore the data using the `dplyr` package and visualize it using the `ggplot2` package for data visualization. The data can be found in the companion package for this course, `statsr`. Let's load the packages. ```{r load-packages, message=FALSE} library(statsr) library(dplyr) library(shiny) library(ggplot2) ``` ### The data We consider real estate data from the city of Ames, Iowa. The details of every real estate transaction in Ames is recorded by the City Assessor's office. Our particular focus for this lab will be all residential home sales in Ames between 2006 and 2010. This collection represents our population of interest. In this lab we would like to learn about these home sales by taking smaller samples from the full population. Let's load the data. ```{r load-data} data(ames) ``` We see that there are quite a few variables in the data set, enough to do a very in-depth analysis. For this lab, we'll restrict our attention to just two of the variables: the above ground living area of the house in square feet (`area`) and the sale price (`price`). We can explore the distribution of areas of homes in the population of home sales visually and with summary statistics. Let's first create a visualization, a histogram: ```{r area-hist} ggplot(data = ames, aes(x = area)) + geom_histogram(binwidth = 250) ``` Let's also obtain some summary statistics. Note that we can do this using the `summarise` function. We can calculate as many statistics as we want using this function, and just string along the results. Some of the functions below should be self explanatory (like