Statistical Concepts Explained and Applied with R

This Regression Statistical Concepts Explained and Applied with R course enables you to delve deeply into the statistical concepts with R, in order to better explore, understand and apply statistical concepts with R skills.

Through this Regression Statistical Concepts Explained and Applied with R course, you will gain a world-class knowledge and understanding with a focus on the statistical concepts with R, based on solving real life examples.

Frontier Education provides working professionals, or those with no previous experience, with trailblazing, practice-based teaching, aimed to inspire and prepare you for a career related to statistics with R.

What you will learn in this course

• Thorough understanding of basic and advanced statistical theory
• How to perform simple and advanced statistical analyses in R
• How to fully and correctly interpret the results
• How to correctly present the results in papers or reports
• How to get reproducible results with every type of analysis carried out in the course
• How to make accurate predictions based on your regression results
• How to deal with real issues in statistical modelling
• The concepts are made simple and the understanding about them is at an advanced level once you finish the course

Reed courses certificate of completion

Digital certificate - Included

• Regression 3:33:05

  • 1: 1 Statistical concepts explained and applied with R teaser 02:54
  • 2: 2 Installing R, RStudio and Basic Functionality 07:50
  • 3: 3 Simple Linear Regression Basics 03:15
  • 4: 4 Simple Linear Regression Basics Ctnd 03:05
  • 5: 5 Simple Linear Regression Analysis 09:27
  • 6: 6 Simple Linear Regression Linear Relationships 08:51
  • 7: 7 Simple Linear Regression Line of Best Fit, SSE and MSE 04:45
  • 8: 8 Simple Linear Regression Analysis Ctnd 01:46
  • 9: 9 Simple Linear Regression Results and Interpretation 10:30
  • 10: 10 Simple Linear Regression Predicting Future Profits 13:27
  • 11: 11 Simple Linear Regression Statistical Validity Tests 09:05
  • 12: 12 Simple Linear Regression Statistical Validity Discussion 07:01
  • 13: 13 Multiple Linear Regression Introduction 05:52
  • 14: 14 Multiple Linear Regression Importing the Data 04:01
  • 15: 15 Multiple Linear Regression Correlation Matrix and MLR 07:36
  • 16: 16 Multiple Linear Regression Results and ANOVA 06:38
  • 17: 17 Multiple Linear Regression The best model_ 05:15
  • 18: 18 Multiple Linear Regression Interaction Terms and Validity Testing 18:22
  • 19: 19 Multiple Linear Regression ANOVA and Predictions 18:05
  • 20: 20 Non-linear Regression and Recap 06:58
  • 21: 21 Non-linear Regression Logistic Regression Overview 21:44
  • 22: 22 Non-linear Regression Logistic Regression_ Odds, Logs and Poisson 12:47
  • 23: 23 Non-linear Regression Logistic Regression_ Fitting the Models in R 23:51

In this Regression Statistical Concepts Explained and Applied with R course, you will learn and understand the core theories and practises in statistical concepts, developing a solid base of knowledge. You will understand core statistical concepts, theories and practises and be able to think critically, actively contributing towards the body of knowledge in the industry and pushing the boundaries with statistical concepts skills.

Bridging the gap between regression statistical concepts and application of R, the statistical concepts with R equips you with the necessary technical skills and expertise to take your career to the next level. You’ll gain the knowledge, problem-solving and decision-making skills that are vital in today’s business world, opening your career to a host of exciting career roles.

The Regression Statistical Concepts Explained and Applied with R course not only sets you up with a deeper understanding of statistical Concepts, it equips you with the personal acumen so you can put knowledge into practice and take your career to the next level.

Curriculum curriculum:

❖ Statistical concepts explained and applied with R teaser

❖ Installing R, R Studio and Basic Functionality

❖ Simple Linear Regression Basics

❖ Simple Linear Regression Basics Ctnd

❖ Simple Linear Regression Analysis

❖ Simple Linear Regression Linear Relationships

❖ Simple Linear Regression Line of Best Fit, SSE and MSE

❖ Simple Linear Regression Analysis Ctnd

❖ Simple Linear Regression Results and Interpretation

❖ Simple Linear Regression Predicting Future Profits

❖ Simple Linear Regression Statistical Validity Tests

❖ Simple Linear Regression Statistical Validity Discussion

❖ Multiple Linear Regression Introduction

❖ Multiple Linear Regression Importing the Data

❖ Multiple Linear Regression Correlation Matrix and MLR

❖ Multiple Linear Regression Results and ANOVA

❖ Multiple Linear Regression The best model?

❖ Multiple Linear Regression Interaction Terms and Validity Testing

❖ Multiple Linear Regression ANOVA and Predictions

❖ Non-linear Regression and Recap

❖ Non-linear Regression Logistic Regression Overview

❖ Non-linear Regression Logistic Regression: Odds, Logs and Poisson

❖ Non-linear Regression Logistic Regression: Fitting the Models in R

Frontier Education’s vision is to enhance the learning and employability of every learner and ensure that employers and students can access the skills they need to compete in the global digital economy. This initiative means that the Frontier Education is ideally placed to help you develop the knowledge and skills that employers need the most, in order to advance your career goals.

Top reasons to Study Online at Frontier Education

• Tailor made: Course adapted to market needs and interests

• Flexible programs: study and work at your own pace on easy-to-use web platforms

• Online education: progressive teaching methods with video or easy to understand medium

• Multicultural: connect with classmates from all corners of the globe

• Undergraduate/graduate/academic scholars/managers who wish to perform a statistical analysis from beginning to end
• Anyone who is serious about a job involving statistical analyses
• Beginner-level students interested in the correct appliances of statistical analyses from theory to completed analysis

• Interest in thoroughly learning statistical concepts and applying them hands-on in analyses

• Access to a computer if you want to code along

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