Applied Statistical Modeling for Data Analysis in R

Applied Statistical Modeling for Data Analysis in R, available at $109.99, has an average rating of 4.49, with 73 lectures, 7 quizzes, based on 1826 reviews, and has 11978 subscribers.

You will learn about 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 This course is ideal for individuals who are People working in any numerate field which requires data analysis or Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences or People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files or People carrying out observational or experimental studies It is particularly useful for People working in any numerate field which requires data analysis or Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences or People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files or People carrying out observational or experimental studies.

Enroll now: Applied Statistical Modeling for Data Analysis in R

Summary

Title: Applied Statistical Modeling for Data Analysis in R

Price: $109.99

Average Rating: 4.49

Number of Lectures: 73

Number of Quizzes: 7

Number of Published Lectures: 73

Number of Published Quizzes: 7

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

People working in any numerate field which requires data analysis
Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences
People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files
People carrying out observational or experimental studies

Target Audiences

People working in any numerate field which requires data analysis
Students of Environmental Science, Ecology, Biology,Conservation and Other Natural Sciences
People with some prior knowledge of the R interface- (a) installing packages (b) reading in csv files
People carrying out observational or experimental studies

APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R

COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R

Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing?

Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!

I created this course to take you by hand teach you all the concepts, and take your statistical modelling 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 course you need to complete in order to get a head start in practical statistical modelling for data analysis using R.

My course has 9.5 hours of lecturesand 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, the perusal of numerous books and publishing statistically rich papers in renowned international journals like PLOS One.

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

(a) 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.

(b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling.

(c) 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.

(d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.

(e) 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.

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. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.

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

Course Curriculum

Chapter 1: Introduction to the Basics of Applied Statistical Modelling

Lecture 1: Introduction to the Instructor and Course

Lecture 2: Data & Code Used in the Course

Lecture 3: Statistics in the Real World

Lecture 4: Designing Studies & Collecting Good Quality Data

Lecture 5: Different Types of Data

Lecture 6: Conclusion to Section 1

Chapter 2: Section 2: The Essentials of the R Programming Language

Lecture 1: Rationale for this section

Lecture 2: Introduction to the R Statistical Software & R Studio

Lecture 3: Different Data Structures in R

Lecture 4: Reading in Data from Different Sources

Lecture 5: Indexing and Subsetting of Data

Lecture 6: Data Cleaning: Removing Missing Values

Lecture 7: Exploratory Data Analysis in R

Lecture 8: Conclusion to Section 2

Chapter 3: Statistical Tools to Learn More About Your Data

Lecture 1: Summarize Quantitative Data

Lecture 2: Measures of Center

Lecture 3: Measures of Variation

Lecture 4: Charting & Graphing Continuous Data

Lecture 5: Charting & Graphing Discrete Data

Lecture 6: Deriving Insights from Qualitative/Nominal Data

Lecture 7: Conclusions to Section 3

Chapter 4: Probability Distributions

Lecture 1: Background

Lecture 2: Data Distribution: Normal Distribution

Lecture 3: Checking For Normal Distribution

Lecture 4: Standard Normal Distribution and Z-scores

Lecture 5: Confidence Interval-Theory

Lecture 6: Confidence Interval-Computation in R

Lecture 7: Conclusions to Section 4

Chapter 5: Statistical Inference

Lecture 1: What is Hypothesis Testing?

Lecture 2: T-tests: Application in R

Lecture 3: Non-Parametric Alternatives to T-Tests

Lecture 4: One-way ANOVA

Lecture 5: Non-parametric version of One-way ANOVA

Lecture 6: Two-way ANOVA

Lecture 7: Power Test for Detecting Effect

Lecture 8: Conclusions to Section 5

Chapter 6: Relationship Between Two Different Quantitative Variables

Lecture 1: Explore the Relationship Between Two Quantitative Variables?

Lecture 2: Correlation

Lecture 3: Linear Regression-Theory

Lecture 4: Linear Regression-Implementation in R

Lecture 5: The Conditions of Linear Regression

Lecture 6: Dealing with Multi-collinearity

Lecture 7: What More Does the Regression Model Tell Us?

Lecture 8: Linear Regression and ANOVA

Lecture 9: Linear Regression With Categorical Variables and Interaction Terms

Lecture 10: Analysis of Covariance (ANCOVA)

Lecture 11: Selecting the Most Suitable Regression Model

Lecture 12: Conclusions to Section 6

Chapter 7: Other Types of Regression

Lecture 1: Violation of Linear Regression Conditions: Transform Variables

Lecture 2: Other Regression Techniques When Conditions of OLS Are Not Met

Lecture 3: Model 2 Regression: Standardized Major Axis (SMA) Regression

Lecture 4: Polynomial and Non-linear regression

Lecture 5: Linear Mixed Effect Models

Lecture 6: Generalized Regression Model (GLM)

Lecture 7: Logistic Regression in R

Lecture 8: Poisson Regression in R

Lecture 9: Goodness of fit testing

Lecture 10: Conclusions to Section 7

Chapter 8: Multivariate Analysis

Lecture 1: Why Do Multivariate Analysis?

Lecture 2: Cluster Analysis/Unsupervised Learning

Lecture 3: Principal Component Analysis (PCA)

Lecture 4: Linear Discriminant Analysis (LDA)

Lecture 5: Correspondence Analysis

Lecture 6: Similarity & Dissimilarity Across Sites

Lecture 7: Non-metric multi dimensional scaling (NMDS)

Lecture 8: Multivariate Analysis of Variance (MANOVA)

Lecture 9: Conclusions to Section 8

Chapter 9: Miscellaneous Lectures & Information

Lecture 1: Exploratory Data Analysis With xda

Lecture 2: Read in Data from Online HTML Tables-Part 1

Lecture 3: Read in Data from Online HTML Tables-Part 2

Lecture 4: Use R in Colab

Lecture 5: Summarise By Time

Lecture 6: POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 47 votes
2 stars: 64 votes
3 stars: 265 votes
4 stars: 558 votes
5 stars: 892 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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