XFIN-657 Business Statistics
The objective of this course is to introduce students to the basic concepts of probability and statistics, and their application in managerial decision-making. This course focuses on descriptive statistics, probability, sampling distributions, interval estimation, hypothesis testing, and regression. Particular emphasis is placed on the effectiveness of statistical modeling in guiding managerial decision-making. Students will study and understand the steps of data collection, probability, uncertainty, and statistical inference. Students will also examine statistical model building via simple regression analysis and discuss multivariate regression. Spreadsheets will be used throughout the course.
Upon completion of this course, the successful student will able to:
- Understand the importance and limitations of statistics, its methodology, and its application to realistic business problems.
- Explain probability theory, including venn diagrams, combinatorics, and Bayes' theorem.
- Compare and contrast discrete random variables such as the Bernoulli, binomial, and Poisson distributions with continuous random variables, including normal, lognormal, uniform, and beta distributions.
- Compute and interpret descriptive statistics.
- Work through sampling distributions, biases, interval estimation, hypothesis testing, and regression.
- Determine confidence intervals and perform hypothesis testing, including how to interpret and communicate results.
- Run a simple linear regression and understand and visualize the difference with multivariate regression.
- Understand advanced statistical tests such as the partial-correlation and difference in means, and the χ2 (Chi-square)
XBUS-504 Data Analysis I: Statistics
The fields of statistics and probability were founded on empirical analysis of data (e.g. human height). Data scientists must possess a strong foundation in statistics and probability to uncover patterns and build models, algorithms, and simulations. This course reviews the basics of descriptive and inferential statistics, distributions, probability, and regression with a specific focus on application to real data sets.
Upon successful completion of the course, students will:
- Explain descriptive and inferential statistics
- Compute measures of central tendency, variance, and probabilities
- Produce and interpret meaningful and accurate summary statistics for a given data set
- Conduct hypothesis tests and understand the difference between Type I and Type II errors
- Develop single and multivariate regression models
- Differentiate between correlation and causation