20678  STATISTICS  ONLINE PREPARATORY COURSE
Department of Decision Sciences
RAFFAELLA PICCARRETA
Lezioni della classe erogate in presenza
Suggested background knowledge
PREREQUISITES
Mission & Content Summary
MISSION
CONTENT SUMMARY
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
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
 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.
Teaching methods
 Online lectures
 Exercises (exercises, database, software etc.)
DETAILS
The course is articulated into online asynchronous classes (slides and videos) on different modules. Ex ante selfevaluation 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 selfevaluation 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.
Assessment methods
Continuous assessment  Partial exams  General exam  


x 
ATTENDING AND NOT ATTENDING STUDENTS
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
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).
IGOR PRUENSTER
Lezioni della classe erogate in presenza
RAFFAELLA PICCARRETA
Lezioni della classe erogate online
Suggested background knowledge
PREREQUISITES
Mission & Content Summary
MISSION
CONTENT SUMMARY
The course is articulated as follows:
 Descriptive analysis
 Describing one variable through tables, charts and synthetic measures
 Describing bivariate association through crosstabs and correlation analysis
 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 on the population mean
 Fundamentals of Hypothesis Testing. Tests on the population mean
 Inference on bivariate association: chisquare test of independence, test on bivariate correlation
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
 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.
Teaching methods
 Online lectures
 Exercises (exercises, database, software etc.)
DETAILS
The course is articulated into online asynchronous classes (slides and videos) on different modules. Ex ante selfevaluation 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 selfevaluation 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.
Assessment methods
Continuous assessment  Partial exams  General exam  


x 
ATTENDING AND NOT ATTENDING STUDENTS
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
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).