Enquiry for Science Major/Minor/Programme Requirements
   <<< This course is not offered in 2025-2026. Course details are subject to change. >>>
STAT1601 Elementary statistical methods (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator TBC, SCDS (Department of Statistics and Actuarial Science) < ugenq@hku.hk >
Teachers Involved
Course Objectives Research findings are usually supported by data.  Data collected in an experiment/survey are often concerned with situations involving variability and uncertainty. They are used to estimate the true value of a certain quantity or to test the acceptability of a certain new hypothesis.  Valid methods of analysing the data are thus essential to any successful investigation.  The course aims to present the fundamentals of statistical methods widely used by researchers. Microsoft Excel might be used to carry out some statistical analysis. There is no demand of sophisticated technical mathematics.
Course Contents & Topics The course will introduce and study the following topics:
Presentation of data, Measures of Central Tendency, Measures of Variability and Uncertainty, Basic Probability Laws, Common Probability Distributions such as Uniform, Binomial, Poisson, Hyper-geometric, Geometric and Normal distributions, Random Sampling, Distribution of the Mean, Normal Sampling Theorem, Point Estimation, Confidence Intervals, Sample Size Determination, Hypothesis Testing, Inferences for Mean and Proportion, Chi-squared tests, Simple Regression and Correlation
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 select and use appropriate statistical methods to analyze data
CLO 2 perform statistical analysis with calculator and Microsoft Excel
CLO 3 understand and apply basic concepts of probability
CLO 4 gain familiarity with the fundamental concepts of random variables
CLO 5 make inferences on a population based on sample data
CLO 6 determine the most appropriate statistical method to use for a given statistical problem
CLO 7 write appropriate conclusions based on the statistical results
CLO 8 understand the basic principles of simple linear regression and correlation and their applications to practical problems
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Level 2 or above in HKDSE Mathematics or equivalent; and
Not for students with Level 2 or above in HKDSE Mathematics Extended Module 1 or 2; and
Not for students who have passed or already enrolled in any of the following courses: STAT2901, STAT1602, STAT2601, STAT1603, ECON1280
Only for students admitted in 2024-25 or before.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2022 Major in Chemistry (Intensive) ( Disciplinary Elective )
2021 Major in Chemistry (Intensive) ( Disciplinary Elective )
2021 Minor in Risk Management ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2022 Major in Chemistry (Intensive) < PLO 4,5 >
2021 Major in Chemistry (Intensive) < PLO 4,5 >
Offer in 2025 - 2026 N        Examination
Offer in 2026 - 2027 N
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 25.0 1,2,3,4,5,6
Examination One 2-hour written examination 75.0 1,2,3,4,5,6,7,8
Required/recommended reading
and online materials		 Chiu W. K.: Basic Statistics (Pearson (Asia), 2007)
Larson, R. & Farber, B.: Elementary Statistics, Picturing the World (Prentice Hall, 2008, 4th ed.)
Berk, K.N. & Carey, P.: Data Analysis with Microsoft EXCEL (Duxbury press, Update Office 2007)
Freund, J. E. & Perles, B. M.: Statistics - A First Course (Prentice Hall, 2004, 8th ed.)
Course Website http://moodle.hku.hk
Additional Course Information Calculator: CASIO fx-50FH (This model has SD-MODE, REG-MODE, nCr and Normal Probability Function which is very suitable for this course.)


Back  /  Home