Basic probabilities
Descriptive probabilities
Inferential Statistics (confidence intervals, hypotheses testing, normal and Student distributions)
At the end of the course, the student should be able to:
– Understand how econometrics are used in each functional area of business, select a relevant research question or thesis statement and choose a relevant model.
– Use the simple or multiple regression analysis to predict the value of a dependent variable, evaluate assumptions of the regression analysis and understand advantages and drawbacks of the Ordinary Least Squares method.
– Identify outliers or influential points, use a dummy variable.
– Use statistical software or an Excel statistical package.
– Build a relevant model: being able to linearize a model, select the most relevant variables and understand multicolinearity.
– Assess model quality using R², Fisher Test. Check OLS assumptions.
Chapter I Simple Linear Regression: basics on sampling, graphs, correlation and linearizing, the OLS, assess model quality: SCE, R², hypothesis of the SLR, checking assumptions using graphs, inference about the slope, confidence Intervals on the forecasted value
Chapter II: Multiple Linear Regression: the multiple regression model, F Test for overall significance, multiple Regression Assumptions, inference about the slope, Dummy variables
Chapter III: Multiple Regression Model Building: quadratic Regression Model, introduction to Logistic Models, model Building: stepwise, best subset, VIF…
