Data Analysis 2: Foundations of Statistics

Course Description: 


Group 1: programming in STATA, full-time schedule, for MA and PhD students primarily. Group 2: programming in R, full-time schedule, for MA, PhD and full-time MSc in Business Analytics students primarily. Group 3: programming in R, part-time schedule, for part-time MSc in Business Analytics and part-time MSc in Finance students primarily. Group 4: programming in R, full-time schedule, for MSc in Finance students and all for whom the course is not offered as an elective in their program.

This introductory course focuses on classic statistics methods which we will discuss with applications. It
focuses on summary statistics, probabilities, type of variables and their distributions. We will cover confidence
intervals, standard errors and how to perform hypothesis testing.
To understand the patterns of the data we deal with, we will look at frequency tables, joint, marginal, and
conditional probabilities.
We will also discuss the meaning and implication of key theories such as the Central Limit and Bayes'

Learning Outcomes: 

*Understand and interpret basic statistics and figures to summarize data
Understand and interpret probabilities and distributions
Being able to tell apart independent events, clustered observations
Formulate hypothesis and test them
Understand how biases or dealing carelessly with data can misrepresent findings


Quizzes 15%. Assignments 25%. Exam: 60%. Passing the course requires a passing score of
over 50% on the written exam.



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