Syllabus of Elementary Statistics - Phạm Thanh Hiếu

1 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 2 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). 3 Subject Structure List of lectures Week/ Lecture(s) Time/ Sections Contents/Topics Instructional methods Week 1 Lecture 1 3.0 hours //. 1.1-1.5 Chapter 1: Introduction to statistics 1.1 The science of statistics 1.2. Types of statistics applications 1.3. Fundamental element of statistics 1.4. Types of data 1.5. Methods of data collection lecture, discussion Week 2 Lecture 2 3.0 hours //. 2.1-2.5 2.1. Graphical method for describing data 2.2. Numerical measures of central tendency 2.3. Numerical measures of cariability 2.4. Data position 2.5. Boxplot lecture, discussion Week 3 Lecture 3 3.0 hours //. Discussion 1 discussion Week 4 Lecture 4 3.0 hours //. 3.1-3.5 Chapter 3: Probability 3.1. The role of probability in statistics 3.2. Basic concepts of probability 3.3. Counting rule 3.4. Event relations 3.5. Conditional probability and the multiplication rule lecture, discussion Week 5 Lecture 5 3.0 hours //. 3.6 4.1-4.2 Chapter 3 (continued) and Chapter 4: Discrete probability distribution 3.6. Additional rule 4.1. Probability distribution 4.2. Binomial distribution lecture, discussion Week 6 Lecture 6 3.0 hours //. 5.1-5.4 Chapter 5: Normal probability distribution 5.1. Normal distribution and the standard distribution 5.2. Normal distribution: Finding probabilities 5.3. Normal distribution: Finding values 5.4. Sampling distribution and the central limit theorem lecture, discussion Week 7 Lecture 7 //. Discussion 2 Review for midterm exam discussion 4 3.0 hours Midterm exam Week 8 Lecture 8 3.0 hours //. 6.1-6.3 Chapter 6: Confidence interval 6.1. Confidence interval for the mean (large sample n 30) 6.2. Confidence interval for the mean (small sample n 30) 6.3. Confidence interval for the population proportion lecture, discussion Week 9 Lecture 9 3.0 hours //. Discussion 3 discussion Week 10 Lecture 10 3.0 hours //. 7.1-7.4 Chapter 7: Hypothesis Testing for One Sample 7.1. Introduction to hypothesis testing 7.2. Hypothesis testing for the mean (large sample n>30) 7.3. Hypothesis testing for the mean (small sample n<30) 7.4. Hypothesis testing for proportions lecture, discussion Week 11 Lecture 11 3.0 hours //. Discussion 4 Review for the midterm exam 2 Midterm exam 2 discussion Week 12 Lecture 12 3.0 hours //. Review all for the final exam The sample final exam discussion 21 hours teaching and giving the sample midterm exams 12 hours discussing and taking the midterm exams 3 hours for reviewing and taking the sample final example =36 hours Time for Final Exam is followed the TUAF exam timetable Reading materials: 1. Elementary statistics, Robert Johnson, Patricia Kuby, THOMSON, 2004 2. Introduction to Probability and Statistics , 13rd edition, W. Mendenhall, R.J. Beaver, and B.M. Beaver, Books/Cole, Cengage Learning, 2009. 3. Applied Statistics, M. A. Shayib, bookboon.com, 2013. 5 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 7 th 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 7 th week Final exam 50% After the 12 th week. Assessment Criteria: Students should obtain at least 40 points in total 100 points for each exam. Grading system Grade in letter 1-4 scale 1-10 scale Description 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 6 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 – 6.99  Completion of key tasks at a satisfactory level, with 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.