1、Techniques of Data AnalysisAssoc. Prof. Dr. Abdul Hamid b. Hj. Mar ImanDirectorCentre for Real Estate StudiesFaculty of Engineering and Geoinformation ScienceUniversiti Tekbnologi MalaysiaSkudai, JohorObjectivesl Overall: Reinforce your understanding from the main lecturel Specific:* Concepts of dat
2、a analysis* Some data analysis techniques* Some tips for data analysisl What I will not do:* To teach every bit and pieces of statistical analysis techniquesData analysis “The Concept”l Approach to de-synthesizing data, informational, and/or factual elements to answer research questionsl Method of p
3、utting together facts and figuresto solve research probleml Systematic process of utilizing data to address research questionsl Breaking down research issues through utilizing controlled data and factual informationCategories of data analysisl Narrative (e.g. laws, arts)l Descriptive (e.g. social sc
4、iences)l Statistical/mathematical (pure/applied sciences)l Audio-Optical (e.g. telecommunication)l OthersMost research analyses, arguably, adopt the firstthree.The second and third are, arguably, most popular in pure, applied, and social sciencesStatistical Methodsl Something to do with “statistics”
5、l Statistics: “meaningful” quantities about a sample of objects, things, persons, events, phenomena, etc. l Widely used in social sciences.l Simple to complex issues. E.g.* correlation* anova* manova* regression* econometric modellingl Two main categories:* Descriptive statistics* Inferential statis
6、ticsDescriptive statisticslUse sample information to explain/make abstraction of population “phenomena”. lCommon “phenomena”:l* Association (e.g. 1,2.3 = 0.75) l* Tendency (left-skew, right-skew)l* Causal relationship (e.g. if X, then, Y)l* Trend, pattern, dispersion, rangelUsed in non-parametric an
7、alysis (e.g. chi-square, t-test, 2-way anova)Examples of “abstraction” of phenomenaExamples of “abstraction” of phenomena% prediction errorInferential statisticslUsing sample statistics to infer some “phenomena” of population parameterslCommon “phenomena”: cause-and-effect * One-way r/ship* Multi-directional r/ship* RecursivelUse parametric analysisY1 = f(Y2, X, e1)Y2 = f(Y1, Z, e2)Y1 = f(X, e1)Y2 = f(Y1, Z, e2)Y = f(X)Examples of relationshipDep=9t 215.8Dep=7t 192.6
