APPLIED BUSINESS STATISTICS

By the end of this course, students will be able to: 1. Understand and apply basic statistical concepts and principles to business problems. 2. Exploit regression analysis to analyze and interpret data in a business context. 3. Understand, use and interpret simple, multiple, and logistic regression. Use the Python programming language to perform data analysis.

Completion of an introductory course in statistics, covering basic concepts of probability, descriptive statistics, hypothesis testing. Familiarity with basic algebra and calculus concepts, including functions, derivatives, and integrals (I want to stress basic). Basic knowledge of computer programming, including familiarity with Python or another statistical software package. These prerequisites are intended to ensure that students have a solid foundation in the concepts and tools necessary to succeed in the course. At the beginning of the course we will review briefly all these topics.

Understand the basic principles of applied business statistics and how they are used to make decisions in a variety of business settings. Apply techniques of regression analysis to quantify the relationships between variables and make predictions based on data. Apply logistic regression analysis to make predictions based on binary outcomes, such as yes/no decisions. Understand the principles of clustering and principle component analysis. Develop proficiency in using Python or other statistical software packages to analyze and interpret data. Communicate effectively about statistical analyses and their results, both in writing and in oral presentations. Think statistically. By the end of the course, students should be able to use these techniques to analyze real-world data, draw appropriate conclusions, and communicate their findings effectively to others.

Topics may undergo changes 1. Introduction Basic concepts of probability and statistics Exploratory data analysis Confidence Interval and hypothesis tests 2. Simple Linear Regression Intro to simple linear regression Estimating the regression parameters Evaluating the goodness of fit Making predictions and inferences 3. Multiple Linear Regression Intro to multiple linear regression Estimating the regression parameters Evaluating the goodness of fit Making predictions and inferences 4. Logistic Regression Logistic regression to say "yes" or "no" Examples of logistic regression in business settings Interpreting logistic regression coefficients The logistic regression in the decision-making process 5. Cluster Analysis: pills 6. Principal Component Analysis: pills Each week, we use the statistical software Python to analyze topics covered in class that week. Data visualization will play a key role in this course.

Course material

Students will utilize various teaching methods, including class lectures, computer exercises, and take-home assignments.

Continuous assessing: mid-term, group project, final exam

TBD