Using StatCrunch in a Large Enrollment Course

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Using StatCrunch in a Large Enrollment Course

Roger Woodard

Department of Statistics

NC State University

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ST311

• Introductory Statistics– 700 students per semester– Majors from social and biological sciences– Not for business or engineering

• Sections– Taught by graduate students– 10 to 12 per semester– 65 students per section

• No computer labs

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GAISE

• Guidelines

• Assessment and

• Instruction in

• Statistics

• Education

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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;

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Use software?

• Requirements– Good graphics– Good statistics

• Full range of procedures up through multiple regression

– Minimal technical overhead – Easy to use– Low cost

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Software?

• R – Free for everyone– Reasonable graphics– Must be installed, command line

• JMP – free for NCSU students– Good graphics– Must be installed

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Software?

• Excel – everyone has it– Doesn’t do statistics well– Graphics good for some but not others– Not interactive

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Statcrunch

• Does not need to be installed– Runs from within a web browser.– www.statcrunch.com

• Low cost – $12 per 6 months– Site license also possible

• Point and click interface, menu driven– Ease of use

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Statcrunch

• Look at Statcrunch:

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Statcrunch

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Good Graphics

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Interactive features

• Allow better understanding of statistical issues

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Interactive features

• Interactive examination of outliers

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Linked graphics

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Linked graphics

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Standard statistical methods

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Web based advantages

• Opens data from variety of sources– Computer– Websites– Paste

• Can be shared to Facebook, twitter, etc

• Can administer surveys

• Direct Data Link

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Direct data link

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Direct data link

• Instructors do not need to reload data sets

• Accessible from all computers across campus

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Link of data from within homework.

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Recommendations

• Concentrate on the statistics– Minimize the amount of work students need to do

to use software– Avoid busy work– Easy links to get data into software– Build up techniques over several assignments– Avoid giving software a “bad reputation”

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Recommendations

• Use video instructions– Students don’t read text documentation– Videos are easy– www.youtube.com– Segment video in small amounts. (30 seconds to 3

minutes) – Give videos based on tasks to perform.

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Video instructions

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Recommendations

• Ask questions that matter– Ask what real world conclusions can be gleaned

• Get students involved in the data– Use data sets that are understandable– Why would distribution look like it does?– Why would there be outliers?– What other sources of variability are there?

• Survey of students

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Survey of students

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Textbooks

• Why is the average around $400?

• Why would some be around $0?

• Why would some be up near $1000

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Survey of students

• Car Age?– How old are students cars?– Do males or females have older cars?

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Survey of students

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Height and Shoe size

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Outliers?

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Outliers?

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Assessment

• Assessment should match what we want students to do.– Think about real world question– Use software to explore data

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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.

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Data exploration problem

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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.

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• Lets explore the data:

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Web links

• GAISE report:– http://www.amstat.org/education/gaise/

• Statcrunch:– www.statcrunch.com

• Video instructions:– Youtube.com– http://www4.stat.ncsu.edu/~woodard/statcrunch/