1. Baburao Kamble (Ph.D) University of Nebraska-Lincoln Data Analysis Using R Week 8: Advanced Regression and Probability Distribution

2. Week 9: 3 rd Nov- TIME SERIES DATA ANALYSIS TIME SERIES DATA ANALYSIS Week 10: 10 th Nov-GEOSPATIAL DATA ANALYSIS USING RGEOSPATIAL DATA ANALYSIS USING R Week 11: 17 th Nov DATA MINING -1: SOCIAL NETWORK DATA Week 12: 24 th Nov-DATA MINING -2: CLUSTERING, CLASSIFICATION AND PREDICTION USING RDATA MINING -1: SOCIAL NETWORK DATADATA MINING -2: CLUSTERING, CLASSIFICATION AND PREDICTION USING R Week 13: 1 st Dec: Project Presentations (6 Projects) Week 14: 8 th Dec: Project Presentations (6 projects) Week 15: 15 th Dec: Project Presentations (6 Projects) Presentation Format : Presentation 5+2 Minute Script Demo+1 Minute Q& A

3. Multiple Regression Model Dependent Variable Independent Variables Intercept Coefficients Error Observation or data point, i, goes from 1…n Script0

4. GENERALISED LINEAR MODELS Mathematical extensions of linear models that do not force data into unnatural scales. Thereby allow for non-linearity and non- constant variance structures in the data. More flexible and better suited for analysing real-life data than 'conventional' regression techniques.

5. Generalized Linear Models The basic tool for fitting generalized linear models is the glm function glm(formula, family, data, weights, subset,...) FamilyVarianceLink gaussian identity binomial logit, probit or cloglog poisson log, identity or sqrt Gamma inverse, identity or log inverse.gaussian 1/mu^2 quasiuser-defined FamilyDefault Link Function binomial(link = "logit") gaussian(link = "identity") Gamma(link = "inverse") inverse.gaussi an (link = "1/mu^2") poisson(link = "log") quasi(link = "identity“) quasibinomial(link = "logit") quasipoisson(link = "log") Script1

6. R Functions for Probability Distributions DistributionR Functions Betapbetaqbetadbetarbeta Binomialpbinomqbinomdbinomrbinom Cauchypcauchyqcauchydcauchyrcauchy Chi-Squarepchisqqchisqdchisqrchisq Exponentialpexpqexpdexprexp Fpfqfdfrf Gammapgammaqgammadgammargamma Geometricpgeomqgeomdgeomrgeom Hypergeometricphyperqhyperdhyperrhyper Logisticplogisqlogisdlogisrlogis Log Normalplnormqlnormdlnormrlnorm Negative Binomialpnbinomqnbinomdnbinomrnbinom Normalpnormqnormdnormrnorm Poissonppoisqpoisdpoisrpois Student tptqtdtrt Studentized Rangeptukeyqtukeydtukeyrtukey Uniformpunifqunifdunifrunif Weibullpweibullqweibulldweibullrweibull Wilcoxon Rank Sum Statistic pwilcoxqwilcoxdwilcoxrwilcox Wilcoxon Signed Rank Statistic psignrankqsignrankdsignrankrsignrank p for "probability", the cumulative distribution function (c. d. f.) q for "quantile", the inverse c. d. f. d for "density", the density function (p. f. or p. d. f.) r for "random", a random variable having the specified distribution Script2Script3

7. One-Tailed Test (Z-test @ 5%) Steps in Hypothesis Testing: 1.State the hypotheses 2.Identify the test statistic and its probability distribution 3.Specify the significance level 4.State the decision rule 5.Collect the data and perform the calculations 6.Make the statistical decision 7.Make the research model decision Two-Tailed Test (Z-test @ 5%) Hypothesis Testing Null hypothesis:    0 Alternative hypothesis:  >  0 1.645 SE 00 1.96 SE 00 Null hypothesis:  =  0 Alternative hypothesis:    0 where  0 is the hypothesised mean Rejection area Script4

8. Questions ?