Singapore University of Social Sciences

Statistical Methods

Applications Open: 01 April 2019

Applications Close: 31 May 2019

Next Available Intake: July 2019

Course Types: Modular Undergraduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: $1312 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C)

Funding: SkillsFuture


Synopsis

ANL321 Statistical Methods is an intermediate course in Statistics that will cover statistical theory in greater depth beyond those covered in BUS105 Statistics such as multiple comparisons in ANOVA and model adequacy checking in Regression models. It will cover foundational topics such as probability theory and random variables, concepts of summary statistics such as expectations, variances, covariances, large sample theory such as law of large numbers and central limit theorem. The course will pay attention least squares regression, the models of linear regression, the asymptotic properties of the least squares estimator, and other issues concerning the use of linear regression analysis. We will cover elements of nonparametric statistics and regression such as kernel density estimation and kernel regression, and the basics of maximum likelihood estimation. Specific software will be used intensively in the course to provide hands-on applications of the topics covered.

Level: 3
Credit Units: 5
Presentation Pattern: Every July

Topics

  • Probability
  • Random Variables
  • Measures of Central Tendency, Dispersion and Association
  • The Conditional Expectation
  • Elements of Finite Sample Properties and Asymptotic Theory
  • Confidence Intervals and Hypothesis Testing
  • The Linear Regression and the Method of Least Squares
  • The Assumptions and Properties of Least Squares Estimators
  • Regression through the Origin, Omitted Variable Bias, Multicollinearity
  • Inference in Linear Regression
  • Further Issues in Regression Analysis
  • Introduction to Maximum Likelihood Estimation

Learning Outcome

  • Explain relevant concepts used in the various statistical methods
  • Describe the relevant data and assumptions to be used for the various statistical models
  • Determine the relevant statistical methods to use for a given business problem and data structure
  • Appraise the advantages and disadvantages of using various statistical methods
  • Implement the various statistical methods using appropriate statistical software
  • Interpret the results of using the various statistical methods
  • Evaluate the results of using the various statistical methods
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