1. Teaching Introductory Statistics
Roger Woodard

2. ST311 Introductory Statistics Sections 700 students per semester
Majors from social and biological sciences
Many other majors
Not for business or engineering
Sections
10 to 12 per semester
65 students per section
Most taught by graduate students

3. GAISE
Guidelines
Assessment and
Instruction in
Statistics
Education

4. GAISE Recommendations
Stress conceptual understanding rather than mere knowledge of procedures;
Emphasize statistical literacy and thinking
Use real data;
Foster active learning in the classroom;
Use assessments to improve and evaluate student learning;
Use technology for developing conceptual understanding and analyzing data;

5. GAISE in the course?
Emphasis on literacy, thinking and conceptual course
Detailed learning objectives=> spell out content for instructors and students.
Order of topics to maximize conceptual links
Discussion of big picture during initial workshop
Ongoing discussion of connections during weekly meetings
Emphasis on big ideas (6 months after the course)

6. Conceptual approach
Stress conceptual understanding rather than mere knowledge of procedures;
Reduce total content
Improve retention
Emphasis on big ideas (6 months after the course)

7. Big ideas:
How data are collected or generated is important if we want to make inference from it. In both experiments and surveys, randomization is necessary to insure appropriate inference.

8. Big ideas:
Statistical inference is possible because statistics have a predictable distribution called a sampling distribution.
The sampling distribution allows us to quantify the variability in sample statistics. including how they differ from the parameter and what type of variability would not be expected to happen by random chance.

9. Topics and order built around big ideas
Detailed learning objectives
Over 100 task level items
Spell out exact content for instructors and students.
Order of topics to maximize conceptual links
Not textbook order
Arrange the topics to maximize connections

10. Order of Topics Traditional Summaries and graphics Regression
Surveys and sampling
Experiments
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing
Regression inference

11. Order of Topics Traditional Revised Summaries and graphics Regression
Surveys and sampling
Experiments
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing
Regression inference
Surveys and sampling
Summaries and graphics
Normal distribution

12. Order of Topics Traditional Revised Summaries and graphics Regression
Surveys and sampling
Experiments
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing
Regression inference
Surveys and sampling
Summaries and graphics
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing

13. Order of Topics Traditional Revised Summaries and graphics Regression
Surveys and sampling
Experiments
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing
Regression inference
Surveys and sampling
Summaries and graphics
Normal distribution
Sampling distribution
Confidence intervals
Hypothesis testing
Regression
Regression inference
Experiments

14. Order of topics

15. Order of topics

16. Order of topics

17. Order of topics

18. Order of topics

19. Order of topics

20. Foster Active Learning
Active learning as a constant theme
Get students involved in the course
Many activities specifically designed to get students involved
M&Ms for confidence intervals, grip meters, etc
Learn by discovery, “what happens if?”
Points specifically set aside for activities
Each instructor can design their own

21. Foster Active Learning
Where do we get the time?
Reduce lecture time with guided note outlines
Cut out minor topics that don’t relate to the big ideas.

22. Use real data Many data sets available Incorporate real data
Home prices
Car characteristics
NBA and NFL rosters
Price of textbooks
Incorporate real data
Not as easy as we thought
Just using real data in a meaningless way does not help

23. BAC Data Study conducted at Ohio State by intro stat students
16 student volunteers
Randomly assigned number of beers
BAC was recorded

24. A question for you.

25. BAC Example

26. Use Technology Software Online software (Statcrunch)
Allows analysis and exploration of data.
Avoid extensive overhead
Free, installation issues, difficult programming
Online software (Statcrunch)
Incorporated in homework
Problems that require data exploration
Incorporated in classes activities
Written instructions don’t work but video does

27. Use Technology Applets and simulations
Many concepts are hard to grasp
Applets and simulations can demonstrate concepts
Students need real world connection
Black box simulations are not meaningful
Concentrate on the connections with physical simulations

28. Distribution of the sample mean

29. Assessment Homework Electronic, timely feedback
Feedback linked to textbook sections and practice problems.
Common across sections =>universal support, well tested
Incorporates data analysis problems

30. Assessment In class activities Midterm exams
More challenging and thought provoking
Get at deeper levels of thought
Midterm exams
Specific time in the class set aside to return and review exams
Instructors hand out learning objective key

31. Statistical thinking
Emphasize statistical literacy and develop statistical thinking;
Carpenter example
Capstone activity
Whole process from start to finish
Design and carry out experiment, analyze the data, interpret the results
Also acts as a review

32. Questions?

33. Web links GAISE report: Statcrunch:
Statcrunch:
Course page with learning objectives

34. Learning Objectives
Given a study, identify population, sample, parameter, sampling frame and statistic.
Given a study, recognize typical forms of biases such as potential undercoverage, nonresponse, and response bias.
Given side-by-side boxplots, contrast key features of the groups represented by the boxplots.
Given a study, interpret the results of a test of significance in context.

36. Data exploration problem
John is a new college graduate working at his first job. After years of living in an apartment he has decided to purchase a home. He has found a great neighborhood from which he can walk to work. Before buying a home in the area he has decided to collect some data on the homes in this neighborhood. A data set has been compiled that represents a sample of 100 homes in the neighborhood he is considering. The variables included in this data set include: Value: the current value of the home as determined by the county tax assessor.
Size: the size of the home in square feet.
Year: the year the homes were built.
Basement: does the home have a basement (y=yes, n=no).
Fireplace: does the home have a fireplace (y=yes, n=no).
Type: the structure a single family house or a townhouse. (house or townhouse).
Create histograms for each of the numeric variables and create bar charts for each of the categorical variables. Use these variables to explore the data and determine which of the following best fits this situation.

37. Data exploration problem

38. Data exploration problem
The histogram for value is clearly bimodal. The reason it is bimodal appears to be because the homes in the neighborhood have higher priced single family houses and lower priced town homes.
The histogram for value is clearly bimodal. The reason it is bimodal appears to be because the neighborhood was built in two phases, the newer phase consists of larger more expensive homes and the older phase consists of smaller less expensive homes.
The histogram for value is clearly bimodal. The reason it is bimodal appears to be because the neighborhood was built in two phases, the older phase consists of larger more expensive homes and the newer phase consists of smaller less expensive homes.
The histogram for value is clearly bimodal. The reason it is bimodal appears to be because the neighborhood has some homes that have basements that tend to be larger in size with another group of homes that do not have basements and tend to be smaller.

39. Homes Data