COURSE OUTLINE: MH3511

Intended Learning Outcomes

Upon successfully completing this course, you should be able to:

1. Evaluate the value of Mathematical functions using R. Writing R program to perform a given algorithm.
2. Distinguish between different types of measurement scales.
3. Explain the meaning of statistical quantities, such as mean, median, variance, etc., and compute the sample values of a given dataset.
4. Use R to construct histogram, boxplot, scatterplot, qq-plot, etc.
5. Construct point and confidence interval estimates for the population parameters using R.
6. Explain the meaning of Type I and Type II errors, and perform statistical hypothesis testing for various types of dataset.
7. Perform statistical inference on categorical dataset.
8. Use parametric methods as an alternative approach to data analysis.
9. Perform linear regression and check for model assumptions.

Course Content

Basic of R programming • Basic R syntax • Write mathematical expression in R language • Variable, vector, matrix and dataframe, and their operations in R • Importing dataset into R, subsetting dataset • Basic loops in R

Describing Data • Basic parameters such as mean, median, standard deviation, variance, inter-quartile range • Boxplot, histogram, stem-leaf plot • Normality checks, qq-plot, outlier, transformation

Statistical Inference • Sample, sampling distribution • Central Limit Theorem • Confidence Interval • Statistical hypothesis testing, Type I and Type II errors, p-value

Categorical Data • Proportion estimate, testing of proportion parameter • Goodness-of-fit test • Two-way contingency table • Paired 2-way contingency table

Multiple Samples • Two independent samples, inference on mean difference • Two dependent samples • Multiple (>2) independent samples, ANOVA test • Multiple (>2) dependent samples

Nonparametric Tests • Quantile test • Wilcoxon rank-sum test • Kruskal-Wallies Test • Sign test, Wilcoxon signed-rank test • Friedman test

Correlation and Regression • Correlation coefficient, its confidence interval and statistical test • Simple linear regression model • Inference on the parameters of linear model • Prediction inference • Model checking

Assessment

Formative Feedback

For midterm Quizzes: Feedback on common mistakes and the level of difficulty of the problems will be given.

For Project Work: You will receive individual written and/or verbal feedback about their project.

For Final Exam: the Examiner's Report will be provided.

Learning and Teaching Approach

Reading and References

1 Hothorn & Everitt: A Handbook of Statistical Analysis Using R, 3rd Edition, CRC Press 2014. ISBN-10: 1482204584, ISBN-13: 978-1482204582

2 Michael J. Crawley: Statistics, An Introduction using R, Wiley 2005. ISBN-10: 0470022981, ISBN-13: 978-0470022986

Course Policies and Student Responsibilities

(1) General

You are expected to complete all lab assignments, take the quizzes, and complete the project. You are expected to take responsibility to follow up with course notes, assignments and course related announcements if you are absent.

(2) Absenteeism

Absence from quizzes and examination without a valid reason will affect your overall course grade. Valid reasons include falling sick supported by a medical certificate and participation in NTU’s approved activities supported by an excuse letter from the relevant bodies.

(3) Absence Due to Medical or Other Reasons

If you are sick and not able to attend the midterm, you have to submit the original Medical Certificate (or another relevant document) to the administration to obtain official leave. In this case, the missed assessment component will not be counted towards the final grade. There are no make-up midterm.

Academic Integrity

Good academic work depends on honesty and ethical behaviour. The quality of your work as a student relies on adhering to the principles of academic integrity and to the NTU Honour Code, a set of values shared by the whole university community. Truth, Trust and Justice are at the core of NTU’s shared values.

As a student, it is important that you recognize your responsibilities in understanding and applying the principles of academic integrity in all the work you do at NTU. Not knowing what is involved in maintaining academic integrity does not excuse academic dishonesty. You need to actively equip yourself with strategies to avoid all forms of academic dishonesty, including plagiarism, academic fraud, collusion and cheating. If you are uncertain of the definitions of any of these terms, you should go to the Academic Integrity website for more information. Consult your instructor(s) if you need any clarification about the requirements of academic integrity in the course.

Course Instructors

Planned Weekly Schedule