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

Slides:

Advertisements

Similar presentations

1 1 Chapter 5: Multiple Regression 5.1 Fitting a Multiple Regression Model 5.2 Fitting a Multiple Regression Model with Interactions 5.3 Generating and.

Advertisements

Correlation and regression

Forecasting Using the Simple Linear Regression Model and Correlation

Hypothesis Testing Steps in Hypothesis Testing:

CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.

6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.

Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.

Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Bivariate Correlation and Regression CHAPTER Thirteen.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Nonparametric Methods Chapter 15.

Correlation Correlation is the relationship between two quantitative variables. Correlation coefficient (r) measures the strength of the linear relationship.

Simple Linear Regression

© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.

The Multiple Regression Model Prepared by Vera Tabakova, East Carolina University.

Statistics II: An Overview of Statistics. Outline for Statistics II Lecture: SPSS Syntax – Some examples. Normal Distribution Curve. Sampling Distribution.

Generalised linear models

Topic 2: Statistical Concepts and Market Returns

Final Review Session.

Log-linear and logistic models Generalised linear model ANOVA revisited Log-linear model: Poisson distribution logistic model: Binomial distribution Deviances.

Log-linear and logistic models

Topics: Regression Simple Linear Regression: one dependent variable and one independent variable Multiple Regression: one dependent variable and two or.

Generalised linear models Generalised linear model Exponential family Example: Log-linear model - Poisson distribution Example: logistic model- Binomial.

T-Tests Lecture: Nov. 6, 2002.

Linear and generalised linear models

Linear and generalised linear models

Simple Linear Regression Analysis

Generalized Linear Models

Correlation and Linear Regression

Regression Analysis (2)

A Statistical Analysis Example of A Full Functional Utilization of An Engineering Calculator Li-Fei Huang Dept. of App. Statistics.

Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Inference on the Least-Squares Regression Model and Multiple Regression 14.

OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.

Mid-Term Review Final Review Statistical for Business (1)(2)

Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.

ALISON BOWLING THE GENERAL LINEAR MODEL. ALTERNATIVE EXPRESSION OF THE MODEL.

Today: Lab 9ab due after lecture: CEQ Monday: Quizz 11: review Wednesday: Guest lecture – Multivariate Analysis Friday: last lecture: review – Bring questions.

Go to Table of Content Single Variable Regression Farrokh Alemi, Ph.D. Kashif Haqqi M.D.

Linear Model. Formal Definition General Linear Model.

Forecasting Choices. Types of Variable Variable Quantitative Qualitative Continuous Discrete (counting) Ordinal Nominal.

一般化線形モデル（ GLM ） generalized linear Models データ解析のための統計モデリング入門久保拓也（２０１２）岩波書店.

1 GLM I: Introduction to Generalized Linear Models By Curtis Gary Dean Distinguished Professor of Actuarial Science Ball State University By Curtis Gary.

1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Chapter 12: Hypothesis Testing.

ITEC6310 Research Methods in Information Technology Instructor: Prof. Z. Yang Course Website: c6310.htm Office:

Chapter Thirteen Copyright © 2006 John Wiley & Sons, Inc. Bivariate Correlation and Regression.

CHAPTERS HYPOTHESIS TESTING, AND DETERMINING AND INTERPRETING BETWEEN TWO VARIABLES.

Correlation & Regression Analysis

Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.

Statistics 2: generalized linear models. General linear model: Y ~ a + b 1 * x 1 + … + b n * x n + ε There are many cases when general linear models are.

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.

Remembering way back: Generalized Linear Models Ordinary linear regression What if we want to model a response that is not Gaussian?? We may have experiments.

1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 

ENGR 610 Applied Statistics Fall Week 7 Marshall University CITE Jack Smith.

Week 7: General linear models Overview Questions from last week What are general linear models? Discussion of the 3 articles.

Lecturer: Ing. Martina Hanová, PhD.. Regression analysis Regression analysis is a tool for analyzing relationships between financial variables:  Identify.

R Programming/ Binomial Models Shinichiro Suna. Binomial Models In binomial model, we have one outcome which is binary and a set of explanatory variables.

Lesson 5 DATA ANALYSIS. Am I using and independent groups design or repeated measures? Independent groups Mann- Whitney U test Repeated measures Wilcoxon.

 Naïve Bayes  Data import – Delimited, Fixed, SAS, SPSS, OBDC  Variable creation & transformation  Recode variables  Factor variables  Missing.

Advanced Data Analytics

Correlation and Linear Regression

Generalized Linear Models

Generalized Linear Models

Correct statistics in ecological research

SA3202 Statistical Methods for Social Sciences

Quantitative Methods What lies beyond?.

Quantitative Methods What lies beyond?.

Fixed, Random and Mixed effects

Statistics II: An Overview of Statistics

Nonparametric Statistics

Introductory Statistics

Correlation and Simple Linear Regression

Correlation and Simple Linear Regression

Presentation transcript:

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

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

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

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.

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

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

One-Tailed 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 5%) Hypothesis Testing Null hypothesis:    0 Alternative hypothesis:  >  SE 0 SE 00 Null hypothesis:  =  0 Alternative hypothesis:    0 where  0 is the hypothesised mean Rejection area Script4

Questions ?