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.

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Introductory Biostatistics

Introductory Biostatistics

A course in introductory biostatistics is often required for professional students in public health, dentistry, nursing, and medicine, and for graduate students in nursing and other biomedical sciences, a requirement that is often considered a roadblock, causing anxiety in many quarters. These feelings are expressed in many ways and in many di¤erent settings, but all lead to the same conclusion: that students need help, in the form of a user-friendly and real data-based text, in order to provide enough motivation to learn a subject that is perceived to be di‰cult and dry. This introductory text is written for professionals and beginning graduate students in human health disciplines who need help to pass and benefit from the basic biostatistics requirement of a one-term course or a full-year sequence of two courses. Our main objective is to avoid the perception that statistics is just a series of formulas that students need to ‘‘get over with,’’ but to present it as a way of thinking—thinking about ways to gather and analyze data so as to benefit from taking the required course. There is no better way to do that than to base a book on real data, so many real data sets in various fields are provided in the form of examples and exercises as aids to learning how to use statistical procedures, still the nuts and bolts of elementary applied statistics.

Preface The first five chapters start slowly in a user-friendly style to nurture interest and motivate learning. Sections called ‘‘Brief Notes on the Fundamentals’’ are added here and there to gradually strengthen the background and the concepts. Then the pace is picked up in the remaining seven chapters to make sure that those who take a full-year sequence of two courses learn enough of the nuts and bolts of the subject. Our basic strategy is that most students would need only one course, which would end at about the middle of Chapter 8, after covering simple linear regression; instructors may add a few sections of Chapter 12. For students who take only one course, other chapters would serve as references to supplement class discussions as well as for their future needs. A subgroup of students with a stronger background in mathematics would go on to a second course, and with the help of the brief notes on the fundamentals would be able to handle the remaining chapters. A special feature of the book is the sections ‘‘Notes on Computations’’ at the end of most chapters. These notes cover uses of Microsoft’s Excel, but samples of SAS computer programs are also included at the end of many examples, especially the advanced topics in the last several chapters.

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Preface

The way of thinking called statistics has become important to all professionals: not only those in science or business but also caring people who want to help to make the world a better place. But what is biostatistics, and what can it do? There are popular definitions and perceptions of statistics. We see ‘‘vital statistics’’ in the newspaper: announcements of life events such as births, marriages, and deaths. Motorists are warned to drive carefully, to avoid ‘‘becoming a statistic.’’ Public use of the word is widely varied, most often indicating lists of numbers or data. We have also heard people use the word data to describe a verbal report, a believable anecdote. For this book, especially in the first few chapters, we don’t emphasize statistics as things, but instead, o¤er an active concept of ‘‘doing statistics.’’ The doing of statistics is a way of thinking about numbers (collection, analysis, and presentation), with emphasis on relating their interpretation and meaning to the manner in which they are collected. Formulas are only a part of that thinking, simple tools of the trade; they are needed but not as the only things one needs to know.

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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.

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Experimental Design

Preface

Although there is a moderate amount of data analysis, especially in certain chapters, the emphasis in this book is on the statistical design of experiments.Such emphasis is justified by the widely held view that data from a well-designed experiment are easy to analyze.Certaintypesofdesignsarenotsimple, however,such as those covered in Chapters 7, 8, and 11, and the problem is compounded by the fact that some popular statistical software packages have quite limited capability for those designs.

Modern Experimental Design

Preface The book would be suitable for an undergraduate one-semester course in design of experiments. For a course taught to nonstatistics majors, an instructor may wish to cover Chapters 1–4, part of Chapter 5, and then pick and choose from the other chapters in accordance with the needs of the students. The selection might include eitherorbothofChapters10and12andthencoversectionsofinterestinChapter13. For statistics majors, the book would be suitable for use in an advanced undergraduatecourse,perhapscoveringChapters1–5,7,8,andmuchofChapter13.There is also enough advanced material for the book to be useful as a reference book in a graduatecoursetaughttostatisticsmajors,andmightalsobeusedinagraduatecourse for nonstatistics majors, depending on the needs and backgrounds of the students. There is also enough material for a two-semester course, with the first course perhaps covering Chapters 1–6 and the second course covering Chapters 7–12 and 14, and parts of Chapter 13.

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Preface

It is worth noting that this book does not contain catalogs of designs, as are given in some other books on the subject. Rather, the emphasis is on understanding design concepts and properties, the software that is available for generating specific designs and when to use those designs, and as stated, a moderate amount of analysis of data from experiments in which the designs are used, with extensive analysis provided in some case studies. Although there is some hand computation, the emphasis is on using appropriate software to generate output and interpret the output.

It is also worth noting that whereas there are case studies and a moderate amount of data analyses, there is not a “full” analysis of any dataset as that would include checking for outliers and influential observations, testing assumptions, and so on, which are covered in books on statistical methods. This is important but comes under the heading of data analysis rather than design and analysis of experiments. Although this book has more analysis than most books on the design of experiments, it is not intended to be a handbook on data analysis.

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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.

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Regression Analysis

In statistics, regression analysis consists of techniques for modeling the relationship between a dependent variable (also called response variable) and one or more independent variables (also known as explanatory variables or predictors). In regression, the dependent variable is modeled as a function of independent variables, corresponding regression parameters (coefficients), and a random error term which represents variation in the dependent variable unexplained by the function of the dependent variables and coefficients. In linear regression, the dependent variable is modeled as a linear function of a set of regression parameters and a random error. The parameters need to be estimated so that the model gives the “ best fit ” to the data.

Linear Regression Analysis Theory and Computing

Overview Regression analysis is a process used to estimate a function which predicts the value of the response variable in terms of values of other independent variables. If the regression function is determined only through a set of parameters the type of regression is the parametric regression. Many methods have been developed to determine various parametric relationships between the response variable and independent variables. These methods typically depend on the form of the parametric regression function and the distribution of the error term in a regression model. For example, linear regression, logistic regression, Poisson regression, and probit regression, etc. These particular regression models assume different regression functions and error terms from corresponding underline distributions. A generalization of linear regression models have been formalized in the “ generalized linear model ” and it requires to specify a link function which provides the relationship between the linear predictor and the mean of the distribution function.

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Topics

The topics on regression analysis covered in this book are distributed among 9 chapters. Chapter 1 briefly introduces the basic concept of regression and defines the linear regression model. Chapters 2 and 3 cover the simple linear regression and multiple linear regression. Although the simple linear regression is a special case of the multiple linear regression, we present it without using matrix and give detailed derivations that highlight the fundamental concepts in linear regression. The presentation of multiple regression focus on the concept of vector space, linear projection, and linear hypothesis test. The theory of matrix is used extensively for the proofs of the statistical properties of the linear regression model. Chapters 4 through 6 discuss the diagnosis of a linear regression model. These chapters cover outlier detection, influential observations identification, collinearity, confounding, regression on dummy variables, checking for equal variance assumption, graphical display of residual diagnosis, and variable transformation technique in linear regression analysis. Chapters 7 and 8 provide further discussions on the generalizations of the ordinary least squares estimation in linear regression. In these two chapters, we discuss how to extend the regression model to the situation where the equal variance assumption on the error term fails. To model the regression data with unequal variance the generalized least squares method is introduced. In Chapter 7, two shrinkage estimators, the ridge regression and the LASSO are introduced and discussed. A brief discussion of the least-squares method for nonlinear regression is also included. Chapter 8 briefly introduces generalized linear models. In particular, the Poisson Regression for count data and the logistic regression for binary data are discussed. Chapter 9 briefly discussed the Bayesian linear regression models. The Bayes averaging method is introduced and discussed.

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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.

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SPSS TUTORIAL

BASIC SPSS TUTORIAL

One of the most popular statistical programs of all times, IBM® SPSS® Statistics* (which originally stood for Statistical Package for the Social Sciences), is a result of the project started in 1968 at Stanford University, California. As a result of its immense popularity, it is not surprising to find many SPSS textbooks being published. Most of these books are voluminous because they combine SPSS with statistics. The need to teach introductory courses in statistics, with a basic knowledge of SPSS, at Radboud University Nijmegen, the Netherlands, however, called for an abridged version of a textbook. To achieve this purpose, we refrained from explaining statistical theory almost completely and focused on the commands within the main menus of SPSS instead. The Dutch manuscript was first introduced in 2002 and adapted based on the teaching experiences as we progressed. The end result was this textbook, which helps students understand SPSS, and more important, to work with it. After reading and working with SPSS’s commands, students are able to modify and analyze data with the most common SPSS tools available.

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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.

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Data Science: Inference and Modeling

Data Science: Inference and Modeling

Overview

Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting. This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast. Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling.

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What you'll learn... The concepts are necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data. How to use models to aggregate data from different sources. And the very basics of Bayesian statistics and predictive modeling.

Chi-Square Test

Chi-Square Analysis

Learning Objectives Given the learning materials and activities of this chapter, the students will be able to: Perform Chi-square test for Goodness of Fit and test of independence to test the significance preference and significance of associations between categorical variables. Interpret the results..

A chi-square tests involve comparing the observed frequencies in a one-way or two-way frequency distribution table with the expected frequencies if the null hypothesis were true. These tests play an important role in many other problems where information is obtained by counting rather than measuring. The method we shall describe here applies to two kinds of problems. The first is the Chi-square goodness-of-fit test, and the second is the chi-square test for independence.

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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.

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Foundations of Data Analysis - Part 1: Statistics Using R

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Foundations of Data Analysis - Part 1: Statistics Using R

Overview

In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion. This course will consist of: Instructional videos for statistical concepts broken down into manageable topics Guided questions to help your understanding of the topic Weekly tutorial videos for using R Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets Weekly wrap-up questions challenging both topic and application knowledge We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap. Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Course Content

Week One: Introduction to Data

Week Two: Univariate Descriptive Statistics

Week Three: Bivariate Distributions

Week Four: Bivariate Distributions (Categorical Data)

Week Five: Linear Functions

Week Six: Exponential and Logistic Function Models

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About the Author:

Ed Neil O. Maratas is 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.

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Statistical Methods

Statistical Methods for the Social Sciences

The purpose of this book is to acquaint the reader with the increasing number of applications of statistics in engineering and the social sciences. It can be used as a textbook for a first course in statistical methods in Universities and Polytechnics. The book can also be used by decision-makers and researchers to either gain a basic understanding or to extend their knowledge of some of the most commonly used statistical methods. The book contains ten Chapters. Chapter 1 deals with the overview of statistics. In Chapter 2, we discuss how to describe data, using graphical and summary statistics. Chapter 3 covers probability while Chapters 4 and 5 cover probability distributions. Chapters 6, 7, 8 and 9 present basic tools of statistical inference; point estimation, interval estimation, hypothesis testing and analysis of variance. Chapter 10 presents linear regression and correlation. Our presentation is distinctly applications-oriented.

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A prominent feature of the book is the inclusion of many examples. Each example is carefully selected to illustrate the application of a particular statistical technique and or interpretation of results. Another feature is that each chapter has an extensive collection of exercises. Many of these exercises are from published sources, including past examination questions from King Saud University (Saudi Arabia) and Methodist University College Ghana. Answers to all the exercises are given at the end of the book.

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.

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Statistical Analysis using R

Statistical Analysis with R For Dummies

Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. • Gets you up to speed on the #1 analytics/data science software tool • Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling • Shows you how R offers intel from leading researchers in data science, free of charge • Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way!

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Content

Chapter 1. >>>Getting Started with Statistical Analysis with R<<<,,,2. >>>Describing Data<<<,,,3. >>>Drawing Conclusion form Data<<<,,,4. >>>Working with Probabiilty<<<,,,5. >>>Spectral Plots<<<,,,6. >>>The Parts of Tens<<<,,,

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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How to Use SPSS in Forecasting?

STEPS ON HOW USE SPSS IN FORECASTING

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily -- without being an expert statistician. Reliable forecasts can have a major impact on your organization’s ability to develop and implement successful strategies. Unlike spreadsheet programs, IBM SPSS Forecasting has the advanced statistical techniques needed to work with time-series data regardless of your level of expertise. Analyze historical data and predict trends faster, and deliver information in ways that your organization’s decision-makers can understand and use Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time Save models to a central file so that forecasts can be updated when data changes, without having to re-set parameters or re-estimate models Write scripts so that models can be updated with new data automatically

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Content

Chapter 1. >>>Introduction To Time Series<<<,,,2. >>>Introduction To Time SeriesTime Series Modeler<<<,,,3. >>>Apply Time Series Models<<<,,,4. >>>Seasonal Decomposition<<<,,,5. >>>Spectral Plots<<<,,,6. >>>The goodness of Fit Measures<<<,,,7. >>>Outlier Types<<<,,,8. >>>Guide to ACF/PACF plots<<<

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About the Researcher...

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.

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HOW TO BECOME A DATA SCIENTIST?

A DATA SCIENTIST

Data scientists are a replacement breed of the information who have the technical skills to resolve advanced issues – and therefore the curiosity to explore what problems ought to be resolved. They're the half-man of science, half scientist and part trend-spotter. (Source:https://www.sas.com/en_us/insights/analytics/what-is-a-data-scientist.html). The data scientist role is a consequence of the statistician role that has the use of advanced analytics technologies, as well as machine learning and predictive modeling, to supply insights on the far side statistical analysis. The demand for information science skills has grown considerably in recent years as corporations look to glean helpful information from the voluminous amounts of structured, unstructured and semistructured data that an oversized enterprise produces and collects -- collectively brought up as huge data.

Source:>>>https://medium.com/@YvesMulkers/how-to-become-data-scientist-f2b5b3d2a73a<<<

Skills Needed to Become a Data Scientist...

Data person appearance cool once someone says that he's a scientist in knowledge. Data scientists are a rare imaginary creature and that they masters whole vary of talent set. The unicorns to handle data and build them respectable to knowledge Analysts exploitation completely different techniques like Python, R and then on...Data scientists Role is Cleans the information. knowledge could Unstructured. Unstructured suggests that knowledge are often in any format. Skills and abilities for knowledge person are Machine learning, Statistics, Modelling, and techniques to visualizing knowledge. So, we must explore the various skills important to becoming a Data Scientist in this modern world.

1. Earn a Degree

Earn a bachelor's degree in IT, computer science, math, physics, or another related field. further, earn a master's degree in data or related field is the topmost to become a data scientist. Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data. (According to https://www.kdnuggets.com/2018/05/simplilearn-9-must-have-skills-data-scientist.html). Apart from the classroom experience, you can practice by exploring data analysis to enable you to learn and master more.

2.Soft Skill

This skill is required for information scientists embrace intellectual curiosity combined with skepticism and intuition, in conjunction with power. interpersonal skills are an essential a part of the role, and lots of employers need their information scientists to be data storytellers who know how to present data insights to individuals in any respect levels of a company. They additionally would like leadership skills to steer data-driven decision-making processes in a company.

3.Hard Skill

Hard skills required for the job include data mining, machine learning and the ability to integrate structured and unstructured data. Experience with statistical research techniques, such as modeling, clustering, and segmentation, is also often necessary. Data science requires knowledge of a number of big data platforms and tools, including Hadoop, Pig, Hive, Spark and MapReduce, and programming languages that include structured query language (SQL), Python, Scala, and Perl, as well as statistical computing languages such as R.

3.Teamwork

A data scientist cannot work alone. you may work with company executives to develop strategies, work product managers and designers to make a better product, work with marketers to launch better-converting campaigns, work with consumer and server software developers to make data pipelines and improve progress. you may virtually work with everybody within the organization, as well as your customers. basically, you may be collaborating together with your team members to develop use cases so as to grasp the business goals and data that may be needed to resolve issues. you may have to be compelled to grasp the proper approach to deal with the employment cases, the data that's required to resolve the matter and the way to translate and present the result into what will simply be understood by everybody concerned.

Here is a video on Can You Be a Data Scientist I know there are lots of ideas that are not presented here about how to become a Data Scientist, thus, suggestions and comments are welcome to this section. Thank you.
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About the Researcher...

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.

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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.

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