Introduction - Lecture notes 1

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Môn: Applied statistics (ENEE1006IU)

Trường: Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

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lOMoARcPSD|359747 69
APPLIED STATISTICS
COURSE CODE: ENEE1006IU
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email:
tttu@hcmiu.edu.vn
tttu@hcmiu.edu.vn 1
lOMoARcPSD|359747 69
Week
Lecture
Content
Detail
1
Lecture 1
Introduction to the course
Chapter 1: Data and
Statistics
1.1. Data classification
1.2. Data sources
2
Lecture 2
Chapter 1: Data and
Statistics
1.3. Statistical inference
1.4. Ethical guidelines for statistical practice
3
Lecture 3
Chapter 2: Plotting and
Smoothing data
2.1. Plotting data 2.2.
Smoothing data
4
Lecture 4
Chapter 3: Descriptive
statistics
3.1. Measures of location (mean, mode, median, etc.)
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
Distribution
4.1. Introduction to probability
4.2. Discrete probability distributions
7
Lecture 7
4.3. Continuous probability distributions
8
Lecture 8
4.4. Sampling and sampling distributions
lOMoARcPSD|359747 69
INTRODUCTION TO THE COURSE - THEORY
9,10
Midterm exam. (35%)
2
lOMoARcPSD|359747 69
Week
Lecture
Content
Detail
11
Lecture 9
Chapter 5: Hypothesis
tests
5.1. Hypothesis testing and decision making
5.2. Determining the sample size for a hypothesis test about a
population mean
12
Lecture
10
Chapter 6: t-Test
6.1. Paired t-Test for assessing the average of differences 6.2.
Independent t-Test for assessing the difference of two averages
13
Lecture
11
Chapter 7: Analysis of
Variance (ANOVA)
7.1. Inferences about a population variance
7.2. Inferences about two population variances
14
Lecture
12
Chapter 7: Analysis of
Variance (ANOVA)
7.3. Assumptions for analysis of variance
7.4. A conceptual overview
15
Lecture
13
Chapter 7: Analysis of
Variance (ANOVA)
7.5. ANOVA table
7.6. ANOVA procedure
16
Lecture
14
17
Lecture
15
lOMoARcPSD|359747 69
18, 19
lOMoARcPSD|359747 69
4
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%)
lOMoARcPSD|359747 69
5
tttu@hcmiu.edu.vn
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.
lOMoARcPSD|359747 69
6
[6] Nguyen Van Tuan, Data and Graphic Analysis by R (in Vietnamese: Phân tích số liệu và biểu ồ bằng R)
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
lOMoARcPSD|359747 69
7
WILL BE BANNED FOR LAB-WORK ASSIGNMENT
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
lOMoARcPSD|359747 69
8
• 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
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lOMoARcPSD|359 747 69 APPLIED STATISTICS COURSE CODE: ENEE1006IU
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email: tttu@hcmiu.edu.vn tttu@hcmiu.edu.vn 1 lOMoARcPSD|359 747 69 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 lOMoARcPSD|359 747 69 9,10 Midterm exam. (35%) 2
INTRODUCTION TO THE COURSE - THEORY lOMoARcPSD|359 747 69 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 lOMoARcPSD|359 747 69 18, 19 Final exam. (35%) 3 lOMoARcPSD|359 747 69
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%) 4 lOMoARcPSD|359 747 69 tttu@hcmiu.edu.vn 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. 5 lOMoARcPSD|359 747 69
[6] Nguyen Van Tuan, Data and Graphic Analysis by R (in Vietnamese: Phân tích số liệu và biểu ồ bằng R) 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 6 lOMoARcPSD|359 747 69
WILL BE BANNED FOR LAB-WORK ASSIGNMENT
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 7 lOMoARcPSD|359 747 69
• 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 8
Document Outline

  • APPLIED STATISTICS
    • INTRODUCTION TO THE COURSE
    • INTRODUCTION TO THE COURSE (1)