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Course 2022-2023 a.y.

20678 - STATISTICS - ON-LINE PREPARATORY COURSE

Department of Decision Sciences

Course taught in English

Insegnamento offerto in modalita' e-learning


Go to class group/s: 1 - 2 - 3

M (I sem. - P) - IM (I sem. - P) - MM (I sem. - P) - ACME (I sem. - P) - GIO (I sem. - P) - PPA (I sem. - P)
Course Director:
RAFFAELLA PICCARRETA

Classes: 1 (I sem.)
Instructors:
Class 1: RAFFAELLA PICCARRETA


Suggested background knowledge
PREREQUISITES

The course has not specific prerequisites.


Mission & Content Summary
MISSION

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.

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
At the end of the course student will be able to...
  • 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
At the end of the course student will be able to...
  • 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 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.


Assessment methods
  Continuous assessment Partial exams General exam
  • No formal assessment
  • 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). 
    Last change 01/08/2022 14:41

    DES-ESS (I sem. - P) - DSBA (I sem. - P) - FIN (I sem. - P)
    Course Director:
    IGOR PRUENSTER

    Classes: 2 (I sem.)
    Instructors:
    Class 2: MARCO UGO BOELLA

    Last change 09/05/2022 19:10

    ACME (I sem. - P)
    Course Director:
    RAFFAELLA PICCARRETA

    Classes: 3 (I sem.)
    Instructors:
    Class 3: REBECCA GRAZIANI


    Suggested background knowledge
    PREREQUISITES

    The course has not specific prerequisites.


    Mission & Content Summary
    MISSION

    The course aims at providing students with the basic knowledge of statistics and data analysis, as acquired in the basic course of Statistics for all Bachelor programs at Bocconi University, necessary to successfully attend the course Applied Research in Cultural Industries and Institutions – module I (Quantitative Methods). The course focuses on methods for analyzing sample data, based on the main concepts of statistical thinking, both descriptive and inferential. In order to understand inferential tools, basic concepts of probability theory are introduced.

    CONTENT SUMMARY

    The course is articulated as follows:

    • Descriptive analysis
      • Describing one variable through tables, charts and synthetic measures
      • Describing bivariate association through cross-tabs 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: chi-square test of independence, test on bivariate correlation

    Intended Learning Outcomes (ILO)
    KNOWLEDGE AND UNDERSTANDING
    At the end of the course student will be able to...
    • 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
    At the end of the course student will be able to...
    • 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 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.


    Assessment methods
      Continuous assessment Partial exams General exam
  • There is no formal assessment
  • 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). 
    Last change 01/08/2022 14:45