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蒙纳什大学马来西亚校区 / 课程

STA2216

Data analysis for science

6 credit pointsLevel 2Second semesterMalaysiaMalaysia School of Science

基本信息

学分6 credit points
开课学期Second semester
校区Malaysia
考核构成
Assignments (2)30%
Mini project report10%
Workshop assessments10%
Examination (2 hours and 10 minutes)50%

开课安排1

教学期授课方式状态
Second semesterTeaching activities are on-campus (ON-CAMPUS)开课

以上为该校区在官方资料中登记的全部开课安排,不是汇总。同一门课可能在多个 教学期开课,也可能不同教学期的授课方式不同。

课程简介

This unit is designed to develop an understanding of some of the most widely used methods of statistical data analysis, from the viewpoint of the user, with an emphasis on planned experiments. You will become familiar with at least one standard statistical package. Topics covered include: parametric and nonparametric procedures to compare two independent and matched samples; review of simple linear regression; multiple linear regression - analysis of residuals, choice of explanatory variables; model selection and validation; nonlinear relationships; introduction to logistic regression; basic principles of experimental design; one-way and two-way analysis of variance models; planned and multiple comparison techniques; power and sample size considerations in design; usage of some available statistical packages including Minitab and/or SPSS, data preparation, interpretation of output.

以上为 Monash Handbook 的官方原文,版权属 Monash University,此处按本站要求转载并标注出处: 官方页面 ↗

学习成果5

官方原文(Learning outcomes),版权属 Monash University。

  1. ULO1 Recognise the requirements for design of an effective experiment and the nature of data arising from these situations;
  2. ULO2 Demonstrate an understanding of some of the important parametric and non-parametric methods of statistical data analysis, including analysis of variance, multiple linear regression and logistic regression;
  3. ULO3 Identify and apply an appropriate statistical technique for analysing a given design/ data set;
  4. ULO4 Formulate a model relating a response variable to a number of given independent variables;
  5. ULO5 Use a statistical package for applying statistical techniques covered in the unit.

教学方式与预期工作量

教学方式

Active learning

Online learning

预期工作量

• Three 1-hour lectures;

• One 1-hour workshop (tutorial) and

• Eight hours of independent study per week

官方原文,版权属 Monash University。

先修 / 同修要求

官方资料未列出该课程的先修要求。

先修链路

官方资料未列出该课程的先修要求,因此没有链路可画。

数据来源

数据来源
官方网页
handbook.monash.edu
抓取时间
2026-09-13
可信度
程序抓取,未人工核实

查看官方完整描述 ↗ — 事实性字段(代码、学分、教学期、授课方式、考核权重、先修/同修/互斥关系)与 课程简介、学习成果、教学方式、预期工作量均取自官方 Handbook; 正文版权属 Monash University,此处转载并逐处标注出处。

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