(For students in NUS Business School)
Lecturer: Dr Chan Yiu Man, (Course Coordinator)
Office: S16, #07-110
Course web site: http://www.stat.nus.edu.sg/~stacym/st1131a
Lectures: Tuesday 12:00noon to 1:30pm and Thursday 12:00noon to 1:30pm at LT17
Tutorials: Start in the 3rd week of classes.
Monday 9-10, 10-11, 11-12 SR1 (BIZ2, 04-44);
Tuesday 3-4, 4-5, 5-6 SR3 (BIZ2, 04-42?);
Thursday 4-5, 5-6 SR1 (BIZ2, 04-44);
Friday 12-1, 1-2, 2-3 SR1 (BIZ2 04-44).
Find out any latest update about the module and the webpage.
The module introduces key ideas underlying statistical reasoning and fundamentals of probability. It serves to introduce students to the various data-analytic operations that can be used to inform leadership in making decisions.
Statistics for Managers Using Microsoft Excel, 3rd edition, Levine, Stephan, Krehbiel & Berenson, ISBN 0130970824, 2002, Prentice Hall, Call# HD30.215 Lev, HD30.215 Lev CD-ROM (Problems are taken from this textbook for tutorials.)
Business & Economic Statistics Using Microsoft Excel, 1st edition, Black K & Eldredge D L, ISBN 032401726X, 2002, Thomson Learning.
Business Statistics: Decision Making with Data, 1st edition, Johnson R A and Wichern D W, ISBN 0471592137, 1997, John Wiley, Call# HD30.215 Joh.
Statistical Tables for Students of Science, Engineering, Psychology, Business, Management and Finance, 4th edition, Murdoch J and Barnes J A, ISBN 033355859, 1998, Macmillan, Call# QA276.25 Mur.
1. Introduction and Data Collection
Population, sample, statistics, parameter. Types of data and their sources. Design of survey research. Types of sampling methods. Types of survey errors.
2. Presenting Data in Tables and Charts
Frequency distributions table, histograms, polygons. Cumulative distributions tables, ogive. Scatter diagram. Tabulating and graphing univariate categorical data: summary tables, bar and pie charts, the Pareto diagram. Tabulating and graphing bivariate categorical data: contingency tables, side-by-side bar charts. Graphical excellence and common errors in presenting data.
3. Numerical Descriptive Measures
Measures of central tendency: mean, median, mode, geometric mean, and quartile. Measure of variation: range, midrange, interquartile range, variance & standard deviation, and coefficient of variation. Shape of distributions: symmetric, skewed, using box-and-whisker plots. Exploratory Data Analysis. Coefficient of correlation. Pitfalls in numerical descriptive measures and ethical considerations.
4. Basic Probability and Discrete Probability Distributions
Basic probability concepts: sample spaces and events. Operations with Events: union, intersection, complement, and mutually exclusive. Relative frequency & definition of probability, simple probability, joint probability. Conditional probability. Statistical independence, marginal probability. Bayes’s Theorem. Concept of a random variable. Discrete random variable. Discrete probability distribution. Expected value. Variance. Binomial distribution. Poisson distribution. Hypergeometric distribution.
5. The Normal Distribution and Sampling Distributions
Continuous probability distribution. Normal distribution. Assessing normality. Exponential distribution. Sampling distributions of the mean and proportion. Central Limit Theorem.
6. Confidence Interval Estimation
Point and interval estimations. Confidence interval estimations for the mean (s known and unknown). t-distribution. Confidence interval estimation for the proportion. Determining sample size.
7. Fundamentals of Hypothesis Testing: One-Sample Tests
Hypothesis testing. Null and alternative hypotheses. Type I and II errors. Level of significance. Z-test for the mean (s known). P-value approach to hypothesis testing. Connection to confidence interval estimation. One- and two-tail tests. t-test for the mean (s unknown). Z-test for the proportion.
8. Two Sample Tests with Numerical Data
Comparing two independent samples. Independent samples Z-test for the difference in two means. Pooled variance t-test for the difference in two means. F-test for the comparisons of two variances. Paired sample for the mean difference. Z-test for differences in two proportions.
9. Simple Linear Regression
Types of regression models. Simple linear regression equation. Interpretation of the slope and the intercept. Measures of variation. Assumptions of regression and correlation. ANOVA table. The coefficient of determination (R2) and correlation (r). Residual analysis. Inferences about the slope. Relationship between a t-test and an F-test. Estimation of mean values and prediction of individual values. Strategies for avoiding the pitfalls of regression.
Other topics, if time permits.
Almost all the material is included in the textbook; Chapters 1 to 8, 10 (Section 1) and 11.
The students will extend their knowledge in Microsoft Excel, learning about the “Statistical Functions” and “Data Analysis Tools” in the “Tools” menu. If the later are not available, the “Analysis ToolPak” and the “Analysis ToolPak-VBA” should be checked in the “Tools –– add-ins”, or be installed form the original installation CD.
Continuous Assessment 40%:
Tutorial Participation and Project Presentation: 10%
Midterm Exam 20%
(The midterm exam will be held in the week 29 September 2003.)
Final Exam 60%
To send the lecturer an email, click <firstname.lastname@example.org>
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Last modified: 21 July 2003.