# Applied Statistical Modeling for Data Analysis in R

### Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

# 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

- Analyze their own data by applying appropriate statistical techniques
- Interpret the results of their statistical analysis
- Identify which statistical techniques are best suited to their data and questions
- Have a strong foundation in fundamental statistical concepts
- Implement different statistical analysis in R and interpret the results
- Build intuitive data visualizations
- Carry out formalized hypothesis testing
- Implement linear modelling techniques such multiple regressions and GLMs
- Implement advanced regression analysis and multivariate analysis

## Requirements

- Prior Familiarity With the Interface of R and R Studio
- Interest in Learning Statistical Modelling
- Interest in Applying Statistical Analysis to Real Life Data
- Interest in Gleaning Insights About Data (From Any Discipline)
- This Course Will be Demonstrated on a Windows. You Will Have to Adapt the Code Pertaining to the Changing Working Directories For your OS

# About The Course

I created this course to take you by hand and teach you all the concepts, and take your statistical modeling from basic to an advanced level for practical data analysis.

With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only one course you need to complete in order to get a head start in practical statistical modeling for data analysis using R.

My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.

**GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL!**

This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

- It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.
- It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.
- It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
- You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.
- You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results.

To be more specific, here’s what the course will do for you:

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects immediately!

**TAKE ACTION NOW** 🙂 You’ll also have my continuous support when you take this course just to make sure you’re successful with it.

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

## Course Curriculum |

**Section 1: Introduction to the Course**

- SECTION 1: Lecture 1: Introduction to the Course on Statistical Data Analysis in R - 10:16
- SECTION 1: Lecture 2: Basic Statistical Ideas - 10:08
- SECTION 1: Lecture 3: Introduction to Data Quality - 08:38
- SECTION 1: Lecture 4: Different Data Types - 03:37
- SECTION 1: Lecture 5: Conclusion - 03:39

**Section 2: The Essentials of R Programming Language**

- SECTION 2: Lecture 6: Introduction to R & R Studio Software - 11:39
- SECTION 2: Lecture 7: Theory Behind Data Structures - 14:59
- SECTION 2: Lecture 8: Indexing in R - 11:59
- SECTION 2: Lecture 9: Data Cleaning in R - 17:12
- SECTION 2: Lecture 10: Data Visualization in R - 18:54
- SECTION 2: Lecture 11: Conclusions to Section 2 - 02:16

**Section 3: Statistical Tools to Learn More About Your Data**

- SECTION 3: Lecture 12: Compute Measures of Central Tendency - 08:02
- SECTION 3: Lecture 13: Compute Measures of Variation - 05:48
- SECTION 3: Lecture 14: Charting Continuous Data - 07:45
- SECTION 3: Lecture 15: Deriving Insights from Qualitative/Nominal Data - 08:21
- SECTION 3: Lecture 16: Conclusions to Section 3 - 02:01

**Section 4: Probability Distributions**

- SECTION 4: Lecture 17: Introduction to Probability Distributions - 03:38
- SECTION 4: Lecture 18: Normal Distribution - 04:07
- SECTION 4: Lecture 19: Test for Normal Distribution in R - 06:17
- SECTION 4: Lecture 20: Z Score in R - 04:21
- SECTION 4: Lecture 21: Confidence Interval Theory - 06:06
- SECTION 4: Lecture 22: Confidence Interval in R - 04:53
- SECTION 4: Lecture 23: Conclusion to Section 4 - 01:24

**Section 5: Statistical Inference**

- SECTION 5: Lecture 24: What is Hypothesis Testing? - 05:43
- SECTION 5: Lecture 25: T Testing in R - 10:59
- SECTION 5: Lecture 26: Kruskal Wallis Testing in R - 05:30
- SECTION 5: Lecture 27: One Way ANOVA in R - 07:10
- SECTION 5: Lecture 28: Non Parametric Version of One Way ANOVA - 02:24
- SECTION 5: Lecture 29: Two Way ANOVA in R - 05:41
- SECTION 5: Lecture 30: Conclusions to Section 5 - 02:08

**Section 6: Relationship Between Two Different Quantitative Variables**

- SECTION 6: Lecture 31: Explore the Relationship between Quantitative Variables - 04:26
- SECTION 6: Lecture 32: Correlation Analysis in R - 19:50
- SECTION 6: Lecture 33: Theory of Linear Regression - 10:44
- SECTION 6: Lecture 34: Implement Linear Regression in R - 15:26
- SECTION 6: Lecture 35: Check the Conditions of Linear Regression in R - 12:56
- SECTION 6: Lecture 36: Deal with Multicollinearity - 16:42
- SECTION 6: Lecture 37: What More Does the Multiple Regression Model Tell Us? - 13:39
- SECTION 6: Lecture 38: Linear Regression & ANOVA - 03:38
- SECTION 6: Lecture 39: Linear Regression & ANOVA (Cont) - 15:05
- SECTION 6: Lecture 40: ANCOVA in R - 07:37
- SECTION 6: Lecture 41: Selecting the Most Suitable Regression Model in R - 13:39
- SECTION 6: Lecture 42: Conclusions to Section 6 - 02:10

**Section 7: Other Types of Regression**

- SECTION 7: Lecture 43: Variable Transformations for Linear Regression - 12:17
- SECTION 7: Lecture 44: Resistant Regression - 15:38
- SECTION 7: Lecture 45: SMA Regression - 12:05
- SECTION 7: Lecture 46: Polynomial & Non-Linear Regression - 18:19
- SECTION 7: Lecture 47: Linear Mixed Effect Models - 14:07
- SECTION 7: Lecture 48: Theory Behind Generalized Linear Models (GLMs) - 05:25
- SECTION 7: Lecture 49: Generalized Linear Models (GLMs) in R - 16:18
- SECTION 7: Lecture 50: Poisson Regression in R - 06:20
- SECTION 7: Lecture 51: Goodness of Fit in R - 03:43
- SECTION 7: Lecture 52: Conclusion to Section 7 - 03:09

**Section 8: Multivariate Analysis**

- SECTION 8: Lecture 53: Why Do Multivariate Analysis? - 03:18
- SECTION 8: Lecture 54: Cluster Analysis/Unsupervised Classification - 14:31
- SECTION 8: Lecture 55: Principal Component Analysis (PCA) in R - 13:10
- SECTION 8: Lecture 56: Linear Discriminant Analysis (LDA) in R - 12:55
- SECTION 8: Lecture 57: Correspondence Analysis (CA) in R - 09:22
- SECTION 8: Lecture 58: Non-Metric Multidimensional Scaling(NMDS) in R - 04:07
- SECTION 8: Lecture 59: MANOVA in R - 04:39
- SECTION 8: Lecture 60: Conclusions to Section 8 - 02:38

# 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)

Tell us your name and email address and we’ll give you these lectures for free…

## What Our Amazing Clients Have to Say

Great introductory course for R, the basics are explained well with a stress on understanding not only the usage but also the underlying behavior. Examples selected are interesting to keep the learner engaged. If you are beginning R go for this. It does not require knowledge of anything apart from basic math. Every concept has been explained ground up. A very well throughout module.

Maksuda Akhter