Regression Modeling

Applied Regression Modeling

The first edition of this book was developed from class notes written for an applied regression course taken primarily by undergraduate business majors in their junior year at the University of Oregon. Since the regression methods and techniques covered in the book have broad application in many fields, not just business, this second edition widens its scope to reflect this. Details of the major changes for the second edition are included below.

Preface

The book is suitable for any undergraduate statistics course in which regression analysis is the main focus. A recommended prerequisite is an introductory probability and statistics course. It would also be suitable for use in an applied regression course for non-statistics major graduate students, including MBAs, and for vocational, professional, or other nondegree courses. Mathematical details have deliberately been kept to a minimum, and the book does not contain any calculus. Instead, emphasis is placed on applying regression analysis to data using statistical software, and understanding and interpreting results

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Preface

Chapter 1 reviews essential introductory statistics material, while Chapter 2 covers simple linear regression. Chapter 3 introduces multiple linear regression, while Chapters 4 and 5 provide guidance on building regression models, including transforming variables, using interactions, incorporating qualitative information, and using regression diagnostics. Each of these chapters includes homework problems, mostly based on analyzing real datasets provided with the book. Chapter 6 contains three in-depth case studies, while Chapter 7 introduces extensions to linear regression and outlines some related topics. The appendices contain a list of statistical software packages that can be used to carry out all the analyses covered in the book (each with detailed instructions available from the book website), a table of critical values for the t-distribution, notation and formulas used throughout the book, a glossary of important terms, a short mathematics refresher, and brief answers to selected homework problems.

The first five chapters of the book have been used successfully in quarter-length courses at a number of institutions. An alternative approach for a quarter-length course would be to skip some of the material in Chapters 4 and 5 and substitute one or more of the case studies in Chapter 6, or briefly introduce some of the topics in Chapter 7. A semester-length course could comfortably cover all the material in the book.

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About the Author:

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:ednielmaratas@gmail.com or you can visit the facebook pageStatisticss For Funfor more details about statistics.

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Statistical Modeling

Statistical Modeling in R (Part 1)

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Course Description...

Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 1, we'll take a look at what modeling is and what it's used for, R tools for constructing models, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables. This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.

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TOPICS

.What is statistical modeling?
.Designing, training, and evaluating models
.Assessing prediction performance
.Exploring data with models
.Covariates and effect size

And lot MORE...

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Instructors Profile:

Daniel Kaplan DeWitt Wallace Professor at Macalester College

Danny is the DeWitt Wallace Professor of Mathematics, Statistics, and Computer Science at Macalester College in Saint Paul, Minnesota. At Macalester, he has developed the introductory sequence in calculus and statistics as well as an introduction to computing for scientists. He’s co-authored the mosaic R package and written several textbooks: Understanding Nonlinear Dynamics, Introduction to Scientific Computation and Programming, and Statistical Modeling: A Fresh Approach.

PREREQUISITES OF THE COURSE:

>>>INTRODUCTION TO R<<<

DESCRIPTION OF THE COURSE:

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

>>>Intermediate R<<<

DESCRIPTION OF THE COURSE:

The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.

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About the Author...

Ed Neil O. Maratas an instructor of Jose Rizal Memorial State University, Dapitan Campus, Philippines as regular status. He earned his Bachelor of Science in Statistics at Mindanao State University-Tawi-Tawi College of Technology and Oceanography in the year 2003 and finished Master of Arts in Mathematics at Jose Rizal Memorial State University year 2009. He Became a researcher, a data analyst, and engaged to several projects linked to the university as data processor.

Prepared by:Ed Neil or you can visit the facebook pageStatisticss For Funfor

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