The Use of Computation and Computational Techniques for Data Analysis
by Michelle L. Isenhour, PhD
mlisenho@nps.edu
After reading all of the participant essays, what word do you think was the most commonly used word?
Background
As a service course for other departments, I teach basic (undergraduate) statistics and data analysis to students at the graduate level.
The purpose of my course is to prepare students for computational data analysis they will see in their downstream courses, as well as in their future careers as military officers and US Department of Defense civilians.
Computational Learning Goals
 At the completion of the course, students will be able to:
 Acquire data, perform basic data cleaning, and transform variables to facilitate analysis.
 Perform exploratory and inferential methods for analyzing data and apply the methods in realworld contexts.
 Formulate simple algorithms to solve problems and code them using statistical software.
 Fit and assess the adequacy of databased models using statistical software.
Modules
Students are exposed to the following topics:
 Exploratory Data Analysis: Plots and Descriptive Statistics
 Statistical Inference: Parameter Estimation
 Statistical Inference: Hypothesis Testing
 Statistical Inference: Analysis of Variance
 Statistical Inference: Regression Analysis
The "Evolution"
Last year was my first year teaching with MATLAB.
Incorporated MATLAB Live Scripts and MATLAB Grader.
This year, migrated towards selfpaced materials.
Incorporating MATLAB
 MATLAB Live Script (.mlx)
 Incorporate lesson material with code examples
Incorporating MATLAB
 MATLAB Script (.m)
 Code examples with comments
Incorporating MATLAB
 MATLAB Grader
 Instant feedback on student coding attempts
MATLAB Live Scripts
 MATLAB Live Scripts (.mlx)
 Confidence Intervals for Mean
 Confidence Intervals for Variance
 Confidence Intervals for Population Proportion
 One Sample Hypothesis Tests
 Two Sample Hypothesis Tests
 Paired Sample Hypothesis Tests
 Measures of Linear Relationships
 Simple Linear Regression
 More on Simple Linear Regression
 Modeling with Simple Linear Regression
 Multiple Linear Regression
 Multiple Linear Regression with Categorical Variables
 Using Regression Models to Make Predictions
 Assessing Model Adequacy
 Regression with Transformed Variables
 Logistic Regression
 Analysis of Categorical Data
Teaching Activity
MATLAB Resources
Linear Regression:
 Interpret Linear Regression Results > Documentation
 Linear Regression > Documentation
 Linear Regression with Interaction Effects > Documentation
 Summary of Output and Diagnostic Statistics > Documentation
 Fstatistic and tstatistic > Documentation
 Coefficient of Determination (RSquared) > Documentation
 Coefficient Standard Errors and Confidence Intervals > Documentation
 Residuals > Documentation
 Generalized Linear Models > Documentation
Hypothesis Testing:
 OneSample zTest > Documentation
 OneSample tTest > Documentation
 TwoSample tTest > Documentation
 Sample Size and Power for Hypothesis Tests > Documentation > Example
Analysis of Variance:
 Analysis of Variance and Covariance > Documentation

anova1
> Documentation 
anova2
> Documentation 
anovan
> Documentation 
multcompare
> Documentation
PreClass SelfAssessment
 Preclass student selfassessment:
 Read/review the material and attempt a preclass assignment.
 Review the solution, along with a MATLAB script file (or Live Script).
 Identify deficiencies prior to attending the class lecture.

Preclass selfassessment via individual student blog:
 Map the content to realworld examples and explain how they would utilize the techniques.
 Answer specific questions to assess understanding.
PostClass Assessment
 Postclass computerbased assessment:
 Retake the computational assessment at the end of each lesson an unlimited number of times.
 Open book, open notes and the use of any statistical software package is authorized.
 Immediate feedback provided to the student.
 Postmodule laboratory exercise:
 Handson computational assessment, beginning in class and continuing outofclass.
 Culminates in a twopage report, executive summary, or web page.
 Postcourse final exam:
 Two week outofclass (take home) assessment.
 Approximately 30 questions, 20 True/False and Multiple Choice and 10 computational problems.
Questions?
The Use of Computation and Computational Techniques for Data Analysis
By Michelle L. Isenhour
The Use of Computation and Computational Techniques for Data Analysis
This presentation, prepared for the 2019 Teaching Computation in the Sciences Using MATLAB workshop, describes how computation and computational techniques are incorporated in a graduatelevel course on data analysis at the Naval Postgraduate School.
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