# REGRESSION ANALYSIS FOR STATISTICS & MACHINE LEARNING IN R

### This Course Will Teach You Regression Analysis for Both Statistical Data Analysis and Machine Learning in R in A Practical Hands-On Manner

# Meet The Instructor

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler

## What Will I Learn

- Implement and infer Ordinary Least Square (OLS) regression using R
- Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
- Carry out variable selection and assess model accuracy using techniques like cross-validation
- Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
- Build machine learning based regression models and test their robustness in R
- Learn when and how machine learning models should be applied
- Compare different different machine learning algorithms for regression modelling

## Requirements

- Should have prior experience of working with R and RStudio
- Should have basic knowledge of statistics
- Should have prior experience of using simple linear regression modelling
- Should have interest in building on the previous concepts to learn which regression models are applicable under different circumstances
- Should have an interest in learning the machine learning based regression models in R

# About The Course

With so many R Statistics & Machine Learning courses around, why enroll for this

Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.

This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models.

Become a Regression Analysis Expert and Harness the Power of R for Your Analysis

- Get started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in R
- Carry out data cleaning and data visualization using R
- Implement ordinary least square (OLS) regression in R and learn how to interpret the results
- Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regression
- Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods
- Evaluate regression model accuracy
- Implement generalized linear models (GLMs) such as logistic regression and Poisson regression
- Use logistic regression as a binary classifier to distinguish between male and female voices
- Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data
- Work with tree-based machine learning models
- Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy
- Carry out model selection
- Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data
- Take the students with a basic level statistical knowledge to performing some of the most common advanced regression analysis based techniques
- Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks
- Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation
- Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.
- Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results

This course is your one-shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

**Specifically, the course will:** It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

**TAKE ACTION TODAY!** I will personally support you and ensure your experience with this course is a success.

## Course Curriculum |

**Section 1: Get Started with Practical Regression Analysis in R**

- SECTION 1: Lecture 1: Introduction to Regression Modelling in R - 06:58
- SECTION 1: Lecture 2: Read in Data in R - 06:36
- SECTION 1: Lecture 3: Data Cleaning in R - 17:12
- SECTION 1: Lecture 4: More Data Cleaning in R - 08:05
- SECTION 1: Lecture 5: Exploratory Data Analysis in R - 18:54
- SECTION 1: Lecture 6: More EDA in R - 04:16
- SECTION 1: Lecture 7: Conclusions to Section 1 - 01:58

**Section 2: Ordinary Least Square Regression Modelling**

- SECTION 2: Lecture 8: Theory of OLS Regression - 10:44
- SECTION 2: Lecture 9: Implement OLS Regression in R - 08:40
- SECTION 2: Lecture 10: More on OLS Regression Interpretation - 07:46
- SECTION 2: Lecture 11: Confidence Interval Theory - 06:06
- SECTION 2: Lecture 12: CI in R - 04:54
- SECTION 2: Lecture 13: Confidence Intervals and OLS Regressions in R - 07:19
- SECTION 2: Lecture 14: No Intercept OLS Regressions in R - 03:40
- SECTION 2: Lecture 15: Implement ANOVA in OLS - 03:38
- SECTION 2: Lecture 16: Multiple Linear Regression in R - 06:27
- SECTION 2: Lecture 17: Multiple Linear Regression with Dummy Variables in R - 15:05
- SECTION 2: Lecture 18: Check the Conditions of OLS - 12:56
- SECTION 2: Lecture 19: Conclusion to Section 2 - 02:55

**Section 3: Deal with Multicollinearity in OLS Regression Models**

- SECTION 3: Lecture 20: Identify multicollinearity - 16:42
- SECTION 3: Lecture 21: Deal with multicollinearity - 05:36
- SECTION 3: Lecture 22: Principal Component Regression in R - 10:39
- SECTION 3: Lecture 23: Partial Least Square Regression in R - 07:33
- SECTION 3: Lecture 24: Ridge Regression in R - 07:22
- SECTION 3: Lecture 25: LASSO Regression in R - 04:24
- SECTION 3: Lecture 26: Conclusions to Section 3 - 02:00

**Section 4: Variable & Model Selection**

- SECTION 4: Lecture 27: Most Suitable OLS Models - 13:19
- SECTION 4: Lecture 28: Select Model Subset - 08:22
- SECTION 4: Lecture 29: Machine Learning Centric Accuracy Estimation - 07:10
- SECTION 4: Lecture 30: Test Model Accuracy R - 14:26
- SECTION 4: Lecture 31: LASSO Regression for Variable Selection in R - 03:42
- SECTION 4: Lecture 32: Identify Variable Selection - 08:38
- SECTION 4: Lecture 33: Conclusions to section 4 - 01:35

**Section 5: Dealing With Other Violations of the OLS Regression Models**

- SECTION 5: Lecture 34: Violate the conditions of regressions - 12:17
- SECTION 5: Lecture 35: Robust Regressions - 06:58
- SECTION 5: Lecture 36: Dealing with Heteroscedasticity - 07:13
- SECTION 5: Lecture 37: Conclusion to Section 5 - 01:12

**Section 6: Generalized Linear Models(GLMs)**

- SECTION 6: Lecture 38: What are GLMs? - 05:25
- SECTION 6: Lecture 39: Logistic Regression in R - 16:18
- SECTION 6: Lecture 40: Logistic Regression with Binary Y in R - 09:10
- SECTION 6: Lecture 41: Multinomial Logistic Regression in R - 06:12
- SECTION 6: Lecture 42: Missing Lecture- 00:00
- SECTION 6: Lecture 43: Goodness of Fit in R - 03:43
- SECTION 6: Lecture 44: Conclusion to Section 6 - 02:12

**Section 7: Working with Non-Parametric and Non-Linear Data**

- SECTION 7: Lecture 45: Polynomial & Non-Linear Regression - 18:19
- SECTION 7: Lecture 46: Generalized Additive Modelling (GAM) in R - 14:09
- SECTION 7: Lecture 47: Boosted GAM in R - 06:15
- SECTION 7: Lecture 48: MARS in R - 08:06
- SECTION 7: Lecture 49: CART in R - 10:54
- SECTION 7: Lecture 50: CIR in R - 05:46
- SECTION 7: Lecture 51: Random Forests in R - 11:52
- SECTION 7: Lecture 52: GBM in R - 04:10
- SECTION 7: Lecture 53: Model Selection in R - 05:31

# Want Two Course For Free?

Free Resource 1: Basic Data Analysis – (42:01 Minutes Lecture)

**Free Resource 2:** Basic Machine Learning in R – (53.56 Minutes Lecture)

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