Applied Statistics and Data Analysis for Business (Honors)
BUSN 3000H
3 hours
Applied Statistics and Data Analysis for Business (Honors)
Analytical Thinking
Course Description
Analysis of data with appropriate software. Develop data exploration and visualization skills. Apply concepts of random sampling, probability, probability distributions, and expected value. Employ statistical inference in business decision making. Use linear regression for describing and predicting empirical relationships.
Athena Title
App Stat and Data Analy Bus H
Equivalent Courses
Not open to students with credit in BUSN 3000, BUSN 3000E, BUSN 3001
Non-Traditional Format
Honors Program students meet for additional break-out sessions.
Prerequisite
Second year student standing and permission of Honors
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Student Learning Outcomes
Students who complete this course will be able to perform context-relevant exploratory data analysis.
Students who complete this course will be able to apply the basic concepts of probability theory, including random variables, probability distributions, and expected value.
Students who complete this course will be able to apply basic statistical estimation concepts to learn about population characteristics and differences in population groups.
Students who complete this course will be able to carry out basic statistical inference on potential group differences, including confidence intervals, hypothesis tests, and interpretation of statistical and practical significance.
Students who complete this course will be able to estimate simple linear regression models, interpret the estimated coefficients and standard errors, conduct basic tests of significance, assess overall fit, and use the fitted model for prediction.
Students who complete this course will be able to employ the techniques covered in the course to aid business decision-making.
Topical Outline
Exploratory data analysis
Random variables and probability
Expected value, variance, covariance, and correlation
Estimating population characteristics and group differences
RCTs and A/B tests
The Law of Large Numbers and Central Limit Theorem
Sampling distributions and the CLT
Confidence intervals and hypothesis tests
p values, statistical and practical significance
Simple linear regression
Institutional Competencies
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
