Chapter 4 Displaying Quantitative Data

1. Chapter 4 Displaying Quantitative Data *histograms *stem-and-leaf plots *dotplot *shape, center, spread

2. Histogram • displays the distribution of QUANTITATIVE data in bins • the height of each bin represents the count of data values • bins have to have equal size intervals • there should be NO spaces between the bins

3. Examples of Histograms

4. How to Make a Histogram • Slice up the entire span of values covered by the quantitative variable into equal width piles called bins (remember they need to be equal intervals) • Count the number of values that fall into each bin • data values that fall on the boarder of bins go in the higher bin • Be sure to label each axis (variable names and scales) • The bins and the count in each bin give the distribution of the quantitative variable

5. Stem-and-Leaf Plots Key: 5|3 = 5.3

6. Stem – and – Leaf Plots • Always make a key • Write numbers the same size and equally spaced (area principle) • More on stem-and-leaf plots coming Friday

7. Dotplots • simple display • place a dot along an axis for each case in the data

8. Quantitative Data Condition • The data are values of a quantitative variable whose units are known • Always check before making a histogram, stem-and-leaf plot, or a dotplot

9. Describing Data • Shape: • how many bumps are there? • Bumps are called MODES (unimodal (1 bump), bimodal (2), multimodal ( > 3) • are there no bumps? Flat tops are called uniform • Is there symmetry? • symmetric – fold in half • skewed – tails to one side (skewed in that direction) • Any thing unusual? • outliers– any points that stand away from the rest of the data • gaps

11. Describing Data (cont) • Center • If you had to pick a single number to describe all the data • for now these are just estimates • Spread • Is the data tightly clustered around the center? • for now this will be described informally VARIATION MATTERS!!!!