20678 - STATISTICS - ON-LINE PREPARATORY COURSE

The course has not specific prerequisites.

The course aims at providing students with the basic knowledge of statistics and data analysis acquired by students who attended the basic course of Statistics for (most of the) Bachelor programs at Bocconi University, necessary to successfully attend some of the courses taught at M, IM, MM, GIO, and PPA Masters of Sciences. The course focuses on techniques for collecting and analyzing data, and on the main concepts of statistical thinking, both descriptive and inferential. In order to understand inferential tools, basic concepts of probability theory are presented.

The course is articulated as follows:
 

• Descriptive analysis of a data set.
  • Data collection, organizing data in tables, graphical presentation methods.
  • Measures of central and non central tendency, measures of variation.
  • Shape of a distribution. Outliers and extreme values.
  • Tabulating and graphing bivariate data.
  • Relationships between two variables (both categorical, or both numerical or of mixed type)
     
• Probability theory and Random variables.
  • Fundamentals of probability
  • Random Variables
  • Discrete and continuous probability distributions.
     
• Inferential statistics
  • Sample and Sampling distribution. Descriptive versus Inferential Statistics.
  • Point and confidence interval estimation
  • Fundamentals of Hypothesis Testing. Tests for the mean or the proportion. Test on the means of dependent or independent samples
     
• Simple linear regression
  • The model at the population level
  • Estimation of the linear model
  • Assessing the model
  • Model assumptions
  • Inference on parameters
  • Prediction
  • Validating model assumptions

• Recognize different types of data.
• Understand the difference between the tools of descriptive and inferential statistics, and identify the most suitable approach for the problem at hand.
• Recognize simple statistical models.

• Properly summarize a dataset.
• Estimate and test hypotheses on the unknown parameters of a population based on sample data.
• Interpret the results obtained by applying simple statistical models, as regression models, to study the relationships between variables of interest.

• Online lectures
• Exercises (exercises, database, software etc.)

The course is articulated into online asynchronous classes (slides and videos) on different modules. Ex ante self-evaluation tests are available for each module, to allow understanding whether knowledge on the topics is enough to skip the module. If the ex ante test is not passed, students are warmly invited to improve their knowledge, using the provided material (slides and video tutorials). A final ex post self-evaluation test can be taken to verify the improvements.

In addition, some online synchronous sessions are planned (in September) to allow students to discuss about their doubts and to have clarifications on specific topics.

The slides and videos available on Bboard are exhaustive and offer a short but complete description of the topics. For a more detailed discussion, students can refer to
 

• P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 9th global edition (2019).