Syllabus: Elementary Statistics

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The object (for students) in this course is: To learn how to interpret statistical summaries appearing in journals, newspaper reports, internet, television, etc; to learn about the concepts of probability and probabilistic reasoning; to understand variability and analyze sampling distribution; to learn how to interpret and analyze data arising in your own work (course work or research).

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THAI NGUYEN UNIVERSITY OF AGRICULTURE AND FORESTRY INTERNATIONAL TRAINING AND DEVELOPMENT CENTER ADVANCED EDUCATION PROGRAM STA13 Elementary Statistics Syllabus Semester 2, AY 2 Picture best relevant to the subject 1
Teaching Staff Subject lecturer: PhD. Pham Thanh Hieu Organization: Faculty of Basic Science, Thai Nguyen University of Agriculture and Forestry Office Location: In the campus of university Phone: Mobile phone: +84 917 522 383 Email: phamthanhhieu@tuaf.edu.vn or hieuphamthanh@gmail.com Consultation hours: From 2 pm to 4 pm on weekly Wednesday in the office location. Short description about the lecturer I have been working as a lecturer of Mathematics in Faculty of Basic Science, Thai Nguyen University of Agriculture and Forestry (TUAF) since 2006. I teach two courses in Vietnamese, Short Calculus and Statistics, for the first year students of TUAF and one course in English, Elementary Statistics, for the second year students of advanced education program in s. I have finished my PhD. study of Mathematical Analysis in 2016 and my interesting research is methods for solving variational inequalities and fixed point problems with potential applications in optimization. Subject Overview Statistics is the science of data. This involves collecting, classifying, summarizing, organizing, analysing, and interpreting numerical information. Many problems arising in real-world situation are closely related to statistics which we call statistical problems. For example: know if a new drug is superior (better) to already existing drugs, or possible side effects. opportunities? So we can see that statistics is the science originated from the real-world problems and it plays important role in many disciplines of economy, natural and social problems. Statistics is a meaningful and useful science whose broad scope of applications to business, government, and the physical and social sciences are almost limitless. Learning Outcomes The object (for students) in this course is To learn how to interpret statistical summaries appearing in journals, newspaper reports, internet, television, etc.. To learn about the concepts of probability and probabilistic reasoning. To understand variability and analyze sampling distribution. To learn how to interpret and analyze data arising in your own work (course work or research). 2
Subject Structure List of lectures Week/ Time/ Contents/Topics Instructional Sections methods Lecture(s) Week 1 Chapter 1: Introduction to statistics lecture, …/…/…. Lecture 1 1.1 The science of statistics discussion 1.2. Types of statistics applications 3.0 hours 1.1-1.5 1.3. Fundamental element of statistics 1.4. Types of data 1.5. Methods of data collection Week 2 2.1. Graphical method for describing data lecture, …/…/…. Lecture 2 2.2. Numerical measures of central tendency discussion 2.3. Numerical measures of cariability 3.0 hours 2.1-2.5 2.4. Data position 2.5. Boxplot Week 3 Discussion 1 discussion …/…/…. Lecture 3 3.0 hours Week 4 Chapter 3: Probability lecture, …/…/…. Lecture 4 3.1. The role of probability in statistics discussion 3.2. Basic concepts of probability 3.0 hours 3.1-3.5 3.3. Counting rule 3.4. Event relations 3.5. Conditional probability and the multiplication rule Week 5 Chapter 3 (continued) and Chapter 4: lecture, …/…/…. Lecture 5 Discrete probability distribution discussion 3.6 3.6. Additional rule 3.0 hours 4.1-4.2 4.1. Probability distribution 4.2. Binomial distribution Week 6 Chapter 5: Normal probability distribution lecture, …/…/…. Lecture 6 5.1. Normal distribution and the standard discussion distribution 3.0 hours 5.1-5.4 5.2. Normal distribution: Finding probabilities 5.3. Normal distribution: Finding values 5.4. Sampling distribution and the central limit theorem Week 7 Discussion 2 discussion …/…/…. Lecture 7 Review for midterm exam 3
Midterm exam 3.0 hours Week 8 Chapter 6: Confidence interval lecture, …/…/…. Lecture 8 6.1. Confidence interval for the mean (large discussion 3.0 hours sample n 30) 6.1-6.3 6.2. Confidence interval for the mean (small sample n 30) 6.3. Confidence interval for the population proportion Week 9 Discussion 3 discussion …/…/…. Lecture 9 3.0 hours Week 10 Chapter 7: Hypothesis Testing for One lecture, …/…/…. Lecture 10 Sample discussion 7.1. Introduction to hypothesis testing 3.0 hours 7.1-7.4 7.2. Hypothesis testing for the mean (large sample n>30) 7.3. Hypothesis testing for the mean (small sample n
Attendance/ Participation Requirements Lecture Attendance Requirement: Attendance at all lectures is expected. If, for whatever reason, you cannot attend the lecture, please let the lecturer know in advance. You are required to attend a minimum of 75% of lectures. Assessment The assessment for this course will be in the form of homework, one midterm exam and one final exam. Midterm will take place in class on the 7th week of the course, and the final examination will take place in class following the exam timetable of TUAF. Your overall grade will be based on: Homework/Attendance/Attitude: 20%; Midterm - 30%; Final - 50% The use of calculators, books or notes will not be allowed in the examinations. Assessment for this subject consists of : Assessment type Percentage Due Date Midterm exam 30% The 7th week Final exam 50% After the 12th week. Assessment Criteria: Students should obtain at least 40 points in total 100 points for each exam. Grading system Grade 1-4 1-10 Description in letter scale scale A 4 8.5 – 10  Excellent analysis, comprehensive research, sophisticated theoretical or methodological understanding, impeccable presentation;  Work that meets all the key assessment criteria and excels in most;  Work that meets these criteria and is also in some way original, exciting or challenging could be awarded marks in the high 8 or above.  Marks of 9 and above may be awarded to the best student work in the range. B 3 7 – 8.49  Good work that is solidly researched, shows a good understanding of key ideas, demonstrates some use of 5
critical analysis along with good presentation and documentation;  Work that meets most of the key assessment criteria and performs well in some;  Work that shows some room for improvement. C 2 5.5 –  Completion of key tasks at a satisfactory level, with 6.99 demonstrated understanding of key ideas and some analytical skills, and satisfactory presentation, research and documentation;  Work that meets most of the key assessment criteria;  Work that shows room for improvement in several areas. D 1 4 – 5.49  Completion of key tasks at an adequate level of performance in argumentation, documentation and expression;  Work that meets a limited number of the key assessment criteria;  Work that shows substantial room for improvement in many areas. F 0 1-3.99  Work that fails to meet the basic assessment criteria;  Work that contravenes the policies and regulations set out for the assessment exercise;  Where a student fails a subject, all failed components of assessment are double marked. Extension Policy and Late Submission of Work Late work is not accepted. If, however, you find that it is absolutely impossible for you to make a given deadline due to illness or other unforeseen circumstances, you may negotiate a short- term extension of up to 5 working days. But please note: Extensions are not granted after due dates have passed. Penalty for Submission of Late Assessment Assessment submitted late without an approved extension will be penalised at 2% per working day. In-class tasks missed without approval will not be marked and in-semester tests and exams that are submitted late without an approved extension will not be accepted. Plagiarism Plagiarism is academic misconduct, and is taken very seriously by the Program and University. Any acts of suspected plagiarism detected by assessors will be followed up, and any students involved will be required to respond via the Program and/or University procedures for handling suspected plagiarism. If you have questions about how to appropriately acknowledge your sources, please let the lecturer know. 6