1. Give 2 examples of this type of variable.
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What type of variables can be displayed using pie charts and bar graphs?
Give 2 examples of this type of variable.

2. Chapter 4 Displaying Quantitative Data
Histograms: Def: display quantitative variables using bars of equal widths with counts on the vertical axis **no spaces between bars (spaces mean gaps in the data) **don’t show data values **bars are called bins **If value falls between two bins, place it in the one on the right.

3. Differences between bar graphs and histograms:
Bar charts are for categorical variables, while histograms are for quantitative variables
Bar charts have spaces between bars while histograms do not
Bars must be in order for a histogram (therefore we can look for skewness)

4. Relative Frequency Histograms:
**Displays precentages of cases in each bin (not counts) **spaces are gaps in data

5. Make a Histogram On Calculator:
1) By entering data individually 2) By entering data when knowing counts of each

6. Stem and Leaf Displays:
Def: show the distribution of a quantitative variable, like a histogram, while preserving the individual values **contain all the information found in a histogram and when carefully drawn it satisfies the area principle and shows the distribution

7. Which histogram goes with the stem and leaf plot shown?

8. Back to Back Stem and Leaf Plot:
A stem and leaf plot that compares two sets of data

9. Dot Plots:
Def: display where a dot is placed along an axis for each case in the data **Can be horizontally or vertically

10. Time Plot:
Displays data that change over time **often successive values are connected with lines to show trends more clearly

11. Before making a histogram, stem and leaf plot, or dotplot check the:
Quantitative Data Condition: The data are value of a quantitative variable whose units are known 

12. Shape, Center, and Spread:
When describing a distribution, make sure to always tell about three things: shape, center, and spread
S C S

13. Shape:
Does the histogram have a single, central hump or several separated humps?
Humps in a histogram are called modes
Unimodal: one main peak
Bimodal: two peaks
Multimodal: three or more peaks
Uniform: no apparent mode

15. Is the histogram symmetric?
Can you fold along a vertical line through the middle and have the edges match pretty closely?

16. Tails: thinner ends of a distribution
Skewed: if one tail stretches out farther than the other
Left(negatively)/right(positively) skewed: by which side of mode is skewed towards

17. Do any unusual features stick out?
-Sometimes these tell us something interesting or exciting about the data
-Always mention any stragglers, or outliers
-Are there any gaps in the distribution?
-Are there any clusters of data?

18. Center:
If you had to pick a single number to describe all the data what would you pick?
It’s easy to find the center when a histogram is unimodal and symmetric (it’s in the middle)
Not so easy with a skewed histogram or bimodal/multimodal histograms
In the next chapter we will discuss some ways to locate the centers numerically(median/mean), for now we will just eyeball it

19. Spread:
Are the values of the distribution tightly clustered around the center or more spread out? Variation **less variability makes it easier to predict **talk more about thnext chapter numerically using range/interquartile range

20. Draw three histograms (one left skewed, one symmetric, and one right skewed) Then, estimate where the median and mean are.

21. What can go wrong? Don’t make a histogram of a categorical variable.
Don’t look for shape, center, and spread of a bar chart.
Choose a bin appropriate for the data.
**Changing the bin width changes the appearance
of the histogram