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Lecture 2: Descriptive Statistics and Exploratory Data ...

Lecture 2: DescriptiveStatistics and ExploratoryData AnalysisFurther Thoughts on Experimental DesignPop 1 Pop 2 Repeat 2 times processing 16 samples in totalRepeat entire process producing 2 technicalreplicates for all 16 samplesRandomly sample 4 individuals from each popTissue culture and RNA extractionLabeling and array hybridizationSlide scanning and data acquisition 16 Individuals (8 each from two populations) with replicatesOther Business Course web-site: Homework due on Thursday not Tuesday Make sure you look at HW1 soon and seeeither Shameek or myself with questionsToday What is Descriptive Statistics and exploratorydata analysis? Basic numerical summaries of data Basic graphical summaries of data How to use R for calculating Descriptive statisticsand making graphsPopulationSampleInferential StatisticsDescriptiveStatisticsProbabili ty Central Dogma of StatisticsEDAB efore making inferences from data it is essential toexamine all your listen to the data :- to catch mistakes- to see patterns in the data - to find violations of statistical assumptions- to generate because if you don t, you will have trouble laterTypes of DataCategoricalQuantitativecontinuousdis creteordinalnominalbinary2 categories more categories order mattersnumerical uninterruptedDimensionality of data Sets Univariate: Measurement made on one variable persubject Bivariate: Measurement made on two variables persubject Multivariate: Measurement made on ma

•Calculating descriptive statistics in R •Creating graphs for different types of data (histograms, boxplots, scatterplots) •Useful R commands for working with multivariate data (apply and its derivatives) •Basic clustering and PCA analysis

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