ĐH Bách khoa – ĐHQG Tp.HCM Faculty of Applied Sciences Department of Applied Mathematics
PROBABILITY AND STATISTICS Code : 006018 - Credits - Hours - Evaluation
: 2 (2.1.3) - Total: 45 Th: 28 Ex: : Mid-term : 20% Exam : 80%
Assig: (45') (90')
Course outline: PROBABILITY AND STATISTICS The objective of the course is to provide to students the funcdamental knowledge of data analysis by statistical methods, based on the knowledge of the subject: Probabilities and Statistics. The course also introduces to students how to use the software SPSS. The main contents of the subject are descriptive statistics, inferent statistics, regression and correlation etc.
Text Book:  Walter A. Rosenkrantz. Introduction to Probability and Statistics for Scientists and Engineers. McGraw-Hill Companies, Inc. 1997.  Allen L. Webster. Applied Statistics for Business and Economics. McGraw-Hill Companies, Inc. 1995.
Instructor: • Lê Thái Thanh
- Department of Applied Mathematics.
Contents: Week Contents 1,2 Chapter 1: Data Analysis. 1.1 Data Defined on a Population. 1.2 The Frequency Distribution. 1.3 Quantiles of a Distribution. 1.4 Measures of Lacation and Variability. 3 Chapter 2: Probability Theory. 2.1 Sample Space, Events, and Axioms of Probability Theory. 2.2 Mathematical Models of Random Sampling. 2.3 Conditional Probability, Bayes’ Theorem. 4 Chapter 3: Discrete Random Variables. 3.1 Discrete Rnadom Variables. 3.2 Expected Value and Variance. 3.3 The Hypergeometric Distribution. 3.4 The Binomial Distribution. 3.5 The Poisson Distribution. 5 Chapter 4: Continuous Random Variables. 4.1 Definition of Continuous Random Variable. 4.2 Expected Value and Variance. Tr. 1/2
Đề cương MH : Thống kê & Phân tích số liệu
Contents 4.3 The Normal Distribution. 4.4 Function of a Random Variable. Chapter 5: Multivariate Probability Distributions. 5.1 The Joint Probability Function. 5.2 The Multinomial Distribution. 5.3 The Poisson Process. 5.4 The Joint Density Function. Chapter 6: Sampling Distribution Theory. 6.1 Sampling from a Normal Distribution. 6.2 The Gamma Distribution. 6.3 The Distribution of the Sample Variance. Chapter 7: Point and Interval Estimation. 7.1 Point Estimation of the Mean and Variance. 7.2 Confidence Intervals for the Mean and Variance. 7.3 Point and Interval Estimation for the Difference of Two Means. 7.4 Point and Interval Estimation for a Population Proportion. Chapter 8: Inferences About Population Mean. 8.1 Tests of Statistical Hypotheses: Basic Cancepts and Examples. 8.2 Tests of Hypotheses on μ1 – μ2. 8.3 Normal Probability Plots. Chapter 9: Inferences About Population Proportion. 9.1 Test Concerning the Parameter p of a Binomial Distribution. 9.2 Chi-Square Test. 9.3 Contigency Tables. Chapter 10: Linear Regression and Correlation 10.1 Methods of Least Squares 10.2 The Simple Linear Regression Model 10.3 Model Checking Chapter 11: Multiple Linear Regression 11.1 The Matrix Approach to Simple Linear Regression 11.2 The Matrix Approach to Multiple Linear Regression Chapter 12: Single-Factor Experiment: Analysis of Variance (ANOVA) 12.1 The Single-Factor ANOVA Model. 12.2 Confidence Intervals for the Treatment Means. 12.3 Random Effect Model Chapter 13: Design and Analysis of Multifactor Experiment. 13.1 Randomized Complete Block Designs 13.2 Two-Factor Experiment