STAT 3210 生命科學之統計方法 Stat Tech in Life Sciences

 

Course Code	STAT 3210 STAT3210			科目名稱		Stat Tech in Life Sciences 生命科學之統計方法
教員	Dr. LEUNG Sze Him Isaac [官方介紹及學術著作]			學　　分		3學分
課程性質				同科其他選擇
Workload	l   MIDTERM l   FINAL EXAM								好重
									重
									平均
									輕	1
									極輕
評價教學內容	#1 stat最簡單嘅一個3字頭course，基本上有一半嘢係stat1011/2 學過嘅野，剩低嘅non parametric test, linear regression 都係講最basic嘅concept，plug in formula就得。由於呢科同時係chem bio嗰邊嘅electives，一般stat major讀嘅話都會好著數。唔知係咪stat major嘅hon年年都太低想等大家有機會拉返上去，呢個course係21年入學打後先開始計落major electives入面
評價教員教學	#1 講書有啲1999 但係notes超級簡單易明（基本上當你中學生咁教 咩都講得好清楚
CUSIS科目資料	Description： This course introduces statistical techniques commonly used in life sciences. Topics include descriptive statistics, parameter estimation, hypothesis testing for population proportions and population means, contingency table analysis, correlation and linear regression, logistic regression, survival analysis, and study designs.   Learning Outcome： Upon completion of the course, students should be able to  (1) understand the typical designs of life science studies;  (2) use descriptive statistics to explore data;  (3) choose appropriate statistical tests (e.g., Z-test, t-test, F-test, chi-square test and selected nonparametric tests), implement them and interpret their results;  (4) apply and interpret methods for comparison, correlation and regression analyses; and  (5) calculate statistical power and determine sample size for commonly used statistical tests.
其他資料	2023Sem2：學位 150|註冊 122|剩餘 28
同學推薦	高度推薦	1	推薦			有保留		極有保留

STAT 1011 統計導論 Introduction to Statistics

 

Course Code	STAT 1011 STAT1011			科目名稱		Introduction to Statistics 統計導論
教員	Dr. CHAN Chun Man [官方介紹] [學術著作]			學　　分		3學分
課程性質	理學院 faculty package			同科其他選擇
Workload	l   非PAPER類HOMEWORK l   MIDTERM l   FINAL EXAM								好重
									重
									平均
									輕	2
									極輕
評價教學內容	#1輕鬆易明 #2極淺
評價教員教學	#1根本零備課，對住ppt照讀都夠膽死堂堂教錯書，又成日講啲懶好笑嘅gag，上足三粒鐘簡直痛不欲生 #2非常幽默 令我從此愛上statistics
CUSIS科目資料	Description： Students will learn the basic concepts of statistics, so that they will be equipped to understand statistical reports and recognize when the quantitative information presented is reliable. They will also learn about organizing and displaying graphical and numerical summaries of data, and drawing conclusions from them. The course uses computer interactive techniques, instead of tedious arithmetic calculations, to introduce simple but powerful statistical concepts. Topics include exploratory data analysis, statistical graphics, sampling variability, point and confidence interval estimation, hypothesis testing, as well as other selected topics.   Learning Outcome： Upon completion of the course, students should be able to  (1) understand basic concepts in statistics;  (2) use various statistical methods and techniques to summarise, present and analyse data;  (3) read statistical reports and recognise when the quantitative information being presented is accurate or misleading; and  (4) use computer software to analyse data and draw conclusions.
其他資料	2223Sem1：學位 151|註冊 151|剩餘 0 2223Sem1：學位 150|註冊 130|剩餘 20
同學推薦	高度推薦	1	推薦		1	有保留		極有保留

STAT 3008 應用迴歸分析 Applied Regression Analysis

 

Course Code	STAT 3008 STAT3008			科目名稱		Applied Regression Analysis 應用迴歸分析
教員	Professor WANG Junhui [官方介紹] [學術著作]			學　　分		3學分
課程性質	STAT選修			同科其他選擇
Workload	l   非PAPER類HOMEWORK l   MIDTERM l   FINAL EXAM								好重	1
									重	1
									平均	1
									輕
									極輕
評價教學內容	#1 1999 冇用 考試出d奇怪野。唔會跟past papers 出 #2正常regression course #3 出嘢出太難，但比考前pastpaper係完全兩回事
評價教員教學	#1 prof同TA都教得好差，啲功課（出自教科書）TA要email問professor點做，然後professor都係上網google答案，再教TA。問TA嘅時候，TA俾嘅solution都唔一定啱，某幾份功課只計completion分又唔講明。Professor喺lecture time 唔夠時間講嘅topic唔係cut syllabus而係俾講到1999嘅TA教。作為新嚟嘅professor midterm同final照抄同一年pp，前面提到教唔切俾TA教懷疑係要夾硬教曬pp cover嘅topic。喺考試前有俾pp我哋睇，但唔係佢抄嗰份，同埋佢話唔建議我哋溫pp。 #2份lecture notes 可以係70幾版，每一版可以show 好幾次，Tutorial notes 可以有大量typo 完全唔check清楚先黎up上blackboard ，手寫D字可以係鬼劃符，寫完等如冇寫，亳無參考價值
CUSIS科目資料	Description： This course introduces the general methodology in regression analysis. Topics include least squares method in simple and multiple regression, weighted least squares, diagnostic checkings, variables selection, dummy variables and multicollinearity. A laboratory section will be held to demonstrate the use of related statistical packages and provide student opportunities for hands-on practices.   Learning Outcome： Upon completion of the course, students should be able to   (1) understand the matrix presentation of data;   (2) understand the parameter estimation and hypothesis testing for a regression model;   (3) build an ANOVA table for a regression model;   (4) perform model selection;   (5) perform model diagnostics; and   (6) use statistical software to analyse regression data.
其他資料	2223Sem1：學位 254|註冊 139|剩餘 115
同學推薦	高度推薦		推薦			有保留		極有保留			3