Further information on Spring 2020 Courses

 

Sandy Johnson: Numbers in the News–How to Understand Data Analytics Even if You Hate Math

 

Course text

The text, Charles Wheelan’s Naked Statistics: Stripping the Dread from the Data, will be available at Content Books and online for about $13.

Schedule (subject to modification)

Week One: How Much? How Many? How Long?
Reading: Wheelan Chapter 2: Descriptive Statistics
Discussion: How do we count or measure what we are interested in? And when we have the data, what do we compare our number to?

Week Two: What’s the Difference?
Reading: Wheelan Chapter 9: Inference
Discussion: How do two groups or two events differ? What differences are “significant” (and what do we mean by “significant”?)?

Week Three: What’s the Connection?
Reading: Wheelan Chapter 4: Correlation
Discussion: Lots of events and characteristics are linked together; how do we tell which linkages are important and which ones are not? And which linkages demonstrate a causal relationship? (spoiler: none of them)

Week Four: Who Did we Ask?
Reading: Wheelan Chapter 10: Polling
Discussion: We can’t gather data from everyone (every event, every time) – so how do we decide who and when to do our research? What factors are likely to be especially important in our choices?

Week Five: What If It Is More Complicated?
Reading: Wheelan Chapter 11: Regression Analysis and Chapter 12: Common Regression Mistakes
Discussion: Most events, personal traits, and accomplishments have many contributing causes. How do we consider them simultaneously?

Week Six: What about algorithms? (and what are they?)
Reading: tba
Discussion: Why are algorithms developed? What can they do and not do? Are they fair? Who benefits from their use?

Week Seven: What If They are Trying to Fool Me?
Reading: Selections from How To Lie with Statistics (available online)
Discussion: How can the same data give different answers? What are common ways that data can be misrepresented? Who decides what will be reported?

Week Eight: How Do We Make Real Decisions?
Reading: Wheelan Conclusion
Discussion: How do statistics help us make good decisions about important questions? When are they likely to distort information or confuse our reasoning?

Potential sources for Real-life data:

            Sports Teams (especially Twins)
            Minnesota Zoo
            Public Schools (testing data)
            Retailers (Target?)
            UCC Research Office
            DNR (wildlife surveys)
            Political Polling (reported in the media)