# Lesson 3: Quantitative Data

Lesson 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.

## Objectives

- 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
- Create a quantitative variable from a qualitative variable

## R

## 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 I – Center and Spread

3.1 - Quantitative Variables I – Center and SpreadHere we'll demonstrate basic measures of center and spread (mean, median, sd, quantiles) for quantitative variables.

# 3.2 - Hypothesis Tests with Quantitative data

3.2 - Hypothesis Tests with Quantitative dataIn 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 calculalte 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

3.3 - Resistant Descriptors of Quantitative DataHere we'll consider some tools that are resistant to the presence of outliers in the data.

**NOTE!**Statistic "MAD" is actually "Median

*Absolute*Deviation", not "Median

*Average*Deviation" as mis-spoken here.

# 3.4 - Visualizing Quantitative Data: Histograms - Part i

3.4 - Visualizing Quantitative Data: Histograms - Part iHere 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

3.5 - Visualizing Quantitative Data: Histograms - Part iiHere we explore how we can alter histograms by changing the bin width.

# 3.6 - Visualizing Quantitative Data: Boxplots & Stripcharts

3.6 - Visualizing Quantitative Data: Boxplots & StripchartsIn 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

3.7 - Making Qualitative Variables from a Numeric VariableThis 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