R Tutorial: Inference for Numerical Data in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/inference-for-numerical-data at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello, and welcome to Inference for Numerical Data! My name is Mine Cetinkaya-Rundel, and in this course, you will learn concepts that are essential for conducting inference on numerical data and the associated R code for doing so. We'll begin by using bootstrapping techniques to conduct inference on a single parameter of a numerical distribution. Let's get to it! On a given day, twenty 1 BR apartments were randomly selected on Craigslist Manhattan from apartments listed as "by owner", as opposed to by a rental agency. First, let's take a look at the distribution of these rents. The distribution is unimodal and right-skewed. Then, is the mean or the median a better measure of typical rent in Manhattan? Since the distribution is right-skewed, the median is a better measure of typical rent. Assuming that this sample is representative of the population of all one-bedroom apartments in Manhattan, which is a bit unlikely since these data come from only one classifieds website, we can use bootstrapping techniques to estimate the median rental price of one-bedrooms apartments in Manhattan. Remember that the term bootstrapping comes from the phrase pulling oneself up by one's bootstraps, which is a metaphor for accomplishing an impossible task without any outside help. In this case, the impossible task is estimating the population parameter using data from only the given sample. Note that this is what statistical inference is all about -- we have a sample, and we use that sample to make inferences about the unknown population. Here's our original sample of 20 apartments and their rents. The sample median is two thousand three hundred and fifty dollars. Using this sample, we want to estimate the population median and we will do so via bootstrappin