# 3 Quantitative Data

## Overview

In this lesson, we’ll cover some basic tools for describing quantitative (or continuous) data, using Chapter 3 of Essential R Course Notes.

Objectives

Upon completion of this lesson, you should be able to:

- describe quantitative (continuous) variables based on center and spread,
- test hypotheses about the mean of a quantitative variable,
- use resistant descriptors of center and spread,
- visualize quantitative variables in several ways, and
- create a quantitative variable from a qualitative variable.

## Data and R Code Files

The R code file and data files for this lesson can be found on the Essential R - Notes on learning R page.

## 3.1 Quantitative Variables – Center and Spread

Here we’ll demonstrate basic measures of center and spread (mean, median, sd, quantiles) for quantitative variables.

## 3.2 Hypothesis Tests with Quantitative Data

In this screencast, we’ll demonstrate testing hypotheses about the mean by calculating a t-statistic - both by hand, and via the built-in function `t-test()`

. Note that when we use `pt()`

to calculate p-values, the wrong tail gives us 1-p rather than p - the choice of upper or lower tail is important, and easy to get wrong. (Note the use of the tab key for hints on function arguments).

## 3.3 Resistant Descriptors of Quantitative Data

Here we’ll consider some tools that are resistant to the presence of outliers in the data.

## 3.4 Visualizing Quantitative Data: Histograms - Part I

Here we introduce histograms and kernel density estimates (kde) as tools for visualising distributions. Note that the kde is somewhat sensitive to the bandwidth used to generate it - a wider bandwidth yields a smoother distribution.

## 3.5 Visualizing Quantitative Data: Histograms - Part II

Here we explore how we can alter histograms by changing the bin width.

## 3.6 Visualizing Quantitative Data: Boxplots & Stripcharts

In this screencast we’ll demonstrate the boxplot and the stripchart, both of which can be useful tools for visualizing qualitative variables.

## 3.7 Making Qualitative Variables from a Numeric Variable

This screencast will demonstrate the stem-and-leaf plot and will also show how to group values from a quantitative variable using the function `cut()`

to create a qualitative variable.