Introduction - Lecture notes 1 | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

lOMoAR cPSD| 45903860 APPLIED STATISTICS COURSE CODE: ENEE1006IU
(3 credits: 2 is for lecture, 1 is for lab-work) lOMoAR cPSD| 45903860 Week Lecture Content Detail 1
Lecture 1 Introduction to the course 1.1. Data classification Chapter 1: Data and 1.2. Data sources Statistics 2 Lecture 2 Chapter 1: Data and 1.3. Statistical inference Statistics
1.4. Ethical guidelines for statistical practice 3
Lecture 3 Chapter 2: Plotting and 2.1. Plotting data 2.2. Smoothing data Smoothing data 4
Lecture 4 Chapter 3: Descriptive
3.1. Measures of location (mean, mode, median, etc.) statistics
3.2. Measures of variability (range, variance, deviation, etc.) 5 Lecture 5
3.3. Measures of distribution shape, relative location, and detecting outliers
3.4. Five-number summaries and box plots
3.5. Measures of association between two variables 6
Lecture 6 Chapter 4: Probability and
4.1. Introduction to probability Distribution
4.2. Discrete probability distributions 7 Lecture 7
4.3. Continuous probability distributions 8 Lecture 8
4.4. Sampling and sampling distributions lOMoAR cPSD| 45903860 9,10 Midterm exam. (35%) 2
INTRODUCTION TO THE COURSE - THEORY lOMoAR cPSD| 45903860 Week Lecture Content Detail 11 Lecture 9 Chapter 5: Hypothesis
5.1. Hypothesis testing and decision making tests
5.2. Determining the sample size for a hypothesis test about a population mean 12 Lecture Chapter 6: t-Test
6.1. Paired t-Test for assessing the average of differences 6.2. 10
Independent t-Test for assessing the difference of two averages 13 Lecture Chapter 7: Analysis of
7.1. Inferences about a population variance 11 Variance (ANOVA)
7.2. Inferences about two population variances 14 Lecture Chapter 7: Analysis of
7.3. Assumptions for analysis of variance 12 Variance (ANOVA) 7.4. A conceptual overview 15 Lecture Chapter 7: Analysis of 7.5. ANOVA table 13 Variance (ANOVA) 7.6. ANOVA procedure 16 Lecture
Chapter 8: Time series 8.1. Time series patterns analysis and 14
forecasting 8.2. Forecast accuracy 17 Lecture
Chapter 8: Time series 8.3. Trend projection analysis and 15
forecasting 8.4. Time series decomposition lOMoAR cPSD| 45903860 18, 19 Final exam. (35%) 3 lOMoAR cPSD| 45903860
INTRODUCTION TO THE COURSE - PRACTICE Week Lecture Content 12 Lab-work 1 Lab-work 1:
- Data with R (install R, input data into R) 13 Lab-work 2 Lab-work 2:
- Graphics with R (draw graphic by R) 14 Lab-work 3 Lab-work 3: - Statistical analyses with R 15 Lab-work 4 Lab-work 4:
- Programming with R in practice (part 1) 16 Lab-work 5 Lab-work 5:
- Programming with R in practice (part 2) 17 Lab-work 6 Lab-work 6: Assignment (30%) lOMoAR cPSD| 45903860 INTRODUCTION TO THE COURSE Textbook:
[1] David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran (2017), Statistics
for Business & Economics, 13th Edition, Cengage Learning, USA. Reference:
[2] Paul Mac Berthouex. Linfield C. Brown (2002), Statistics for Environmental Engineers, 2nd Edition, Lewis Publishers.
[3] Nathabandu T. Kottegoda and Renzo Rosso (2008), Applied Statistics for Civil and Environmental Engineers, 2nd
Edition, Blackwell publishing.
[4] C. Reimann, P. Filzmoser, R. G. Garrett, R. Dutter (2008), Statistical Data Analysis Explained: Applied Environmental
Statistics with R, John Wiley & Sons.
[5] Yosef Cohen and Jeremiah Y. Cohen (2008), Statistics and data with R - An applied approach through examples, John Wiley & Sons.
[6] Nguyen Van Tuan, Data and Graphic Analysis by R (in Vietnamese: Phân tích số liệu và biểu ồ bằng R) 5 lOMoAR cPSD| 45903860 INTRODUCTION TO THE COURSE Evaluation:
•Class participation and lab-work assignment: 30% • Mid-term Exam: 35% • Final Exam: 35%
Students must attend at least 80% of the classes.
More than 3 times absence of theory
WILL BE BANNED FOR THE FINAL EXAM
More than 2 times absence of lab-work
WILL BE BANNED FOR LAB-WORK ASSIGNMENT lOMoAR cPSD| 45903860
Read textbook for the next class!!!
INTRODUCTION TO THE COURSE Learning outcomes:
Successful completion of this course will be able to:
• Memorize the principles of data and statistics, plotting and smoothing data, descriptive statistics
• Outline the discrete probability distributions, continuous probability distributions, sampling and sampling distributions
• Describe hypothesis testing and decision making, paired t-Test and independent tTest
• Demonstrate the Analysis of Variance (ANOVA) as well as time series analysis and forecasting
• Describe the data with R and graphics with R
• Practice using R software in statistical analyses and programming 7