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!!!!