Nil
Nil
This module provides students with the basic statistical knowledge and skills needed to analyse and interpret data for practical applications. Selected topics include descriptive statistics, estimation, hypothesis testing and regression analysis. Applications to real-life problems are provided. At the end of the module, students are also able to summarise, interpret and evaluate quantitative results and statistical graphs.
Upon completion of this module, students should be able to:
a. analyse data by using simple statistical measure and statistical graphs;
b. have a basic knowledge of estimation and hypothesis testing, and apply estimation and hypothesis tests in real-life problems;
c. have a basic knowledge of linear regression and multiple regression, and apply regression analysis in real-life problems; and
d. perform statistical analysis with computer software, and interpret and evaluate quantitative results and statistical graphs.
1. Introduction and descriptive statistics
1.1 Data type
1.2 Measures of central tendency and variation
1.3 Graphical representation
2. Estimation
2.1 Probability distribution
2.2 Sampling distribution
2.3 Confident intervals for mean, proportion and variance
2.4 Sample size estimation
2.5 Applications
3. Hypothesis testing
3.1 Basic concepts
3.2 Type I and type II errors
3.3 Tests for mean proportion and variance
3.4 ANOVA
3.5 Test for independence
3.6 Applications
4. Regression analysis
4.1 Simple linear regression
4.2 Multiple regression
4.3 Model selection
4.4 Applications
1. Participation (5%)
2. Assignments (45%)
3. Project (50%)
1. Conrad C. (2013). Statistical Analysis: Microsoft Excel 2013. Pearson.
2. David M. L., David F. S. & Kathryn A. S. (2013). Statistics for Managers using Microsoft Excel (7 th ed.). Pearson.
3. Robert H. C. and Jane G. N. (2012). Doing Data Analysis with SPSS (5 th ed.). Cengage. *
