1. Warm Up:
What is Statistics?

2. Intro: What is Stats? Statistics: Data:
The science (and art) of learning from data.
Data:
Numbers with a contextual meaning.
We use data and Statistics to draw conclusions about a population based on sample information.
Data/Statistics
Population
Inference/Conclusions
Sample

3. Important Terms Population – the group we wish to study
Sample – a portion or small group from the population
Census – data on everyone in a population
Anecdote – limited information about a population, often misleading

4. 4 Themes of Stats Part I: Exploratory Data Analysis
The tools and strategies for organizing, displaying, describing, and analyzing data.
Part II: Producing Data
Designing surveys, experiments, and observational studies that will yield the data necessary to answer a question of interest.
Part III: Probability
The study of chance behavior. How likely are certain outcomes?
Part IV: Inference
Draw conclusions about the population based on samples. Test claims and compute estimates.

5. Traditional Math vs. Stats
Focused on calculation/process
Usually one correct answer
Statistics:
Focused on understanding what the answer means
A lot of reading and writing

6. Chapter 1 - Important Terms
Individuals – Objects described by a set of data. Could be people, animals, computer chips, etc.
Variable – Characteristics of an individual
Categorical variable – places individual into one of several groups
Quantitative variable – numerical value describing an individual

7. Example
A sample of cars was taken from those parked in the student parking lot. The following characteristics were recorded for each car:
Model, mileage on odometer, color, length of wheelbase, age, and gender of the owner
What are the individuals in this data set?
b) Which variables are categorical and which are quantitative?

8. Displays of Categorical Data – Bar Graph
A Zagat survey from 2014 asked Americans their favorite type of pizza.
NY Thin Crust 39%
Neapolitan 17%
Chicago Deep Dish 9%
Chicago Thin Crust 7%
California 5%
Other 23%
1) Plot counts or percentages.
2) Always label axes and show scales.
3) Leave a gap between the bars.

9. Displays of Categorical Data – Pie Chart
A Zagat survey from 2014 asked Americans their favorite type of pizza.
NY Thin Crust 39%
Neapolitan 17%
Chicago Deep Dish 9%
Chicago Thin Crust 7%
California 5%
Other 23%
1) Only plot percentages on a pie chart.
2) You need 100% of the data to make a pie chart.
3) We won’t make pie charts by hand.

10. Example
A Gallup Poll in 2016 asked 1015 adults the following question:
“What do you think is the best long-term investment?”
The table below lists the proportion of adults that chose each category.
Bonds 0.07 Real Estate 0.35
Gold 0.17 Savings 0.15
Other 0.04 Stocks & Mutual Funds 0.22
1) Use the data to make a bar graph, arranged from largest to smallest percentage.
2) Could you make a pie chart with this data? Why or why not?

11. Other Displays of Categorical Data
If we measure 2 variables for each individual in our data set, we may create some additional displays:
1) Side-by-side Bar Graph
2) Segmented Bar Graph
3) Mosaic Plot
An on-line survey from 2017 asked 700 women and 300 men their favorite color:
Women Men
Blue
Green
Purple
Red
Orange
Yellow
Other

12. Categorical Data from this Class
On the board place a tally mark under the correct category to answer the following question:
Where did you travel to this summer?
a) I stayed in California.
b) I traveled to other states but remained in the U.S.
c) I traveled to another country.

13. Categorical Data from this Class
Using the data from the class make one of the following plots:
1) Side by side Bar Graph
2) Stacked Bar Graph
3) Mosaic Plot
You may make any type of plot that you wish, however at least one person from each table has to make each type of plot.
(Remember to graph proportions, not raw counts.)