Applied Statistical Modeling for Data Analysis in R
4.2 (607 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,382 students enrolled

Applied Statistical Modeling for Data Analysis in R

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R
Bestseller
4.2 (607 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,382 students enrolled
Created by Minerva Singh
Last updated 10/2018
English
English [Auto-generated]
Current price: $30.00 Original price: $199.99 Discount: 85% off
30-Day Money-Back Guarantee
This course includes
  • 9.5 hours on-demand video
  • 3 articles
  • 40 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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 OS. You Will Have to Adapt the Code Pertaining to the Changing Working Directories For your OS
Description

                                      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 modeling 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 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.

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.

Who this course is for:
  • 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
Course content
Expand all 69 lectures 09:32:27
+ Section 2: The Essentials of the R Programming Language
8 lectures 01:33:19
Different Data Structures in R
14:59
Reading in Data from Different Sources
15:28
Indexing and Subsetting of Data
11:59
Data Cleaning: Removing Missing Values
17:12
Exploratory Data Analysis in R
18:53
Section 2 Quiz
3 questions
+ Statistical Tools to Learn More About Your Data
7 lectures 47:04
Measures of Center
08:02
Measures of Variation
05:48
Charting & Graphing Continuous Data
07:45
Charting & Graphing Discrete Data
14:49
Deriving Insights from Qualitative/Nominal Data
08:20
Section 3 Quiz
3 questions
+ Probability Distributions
7 lectures 30:46
Data Distribution: Normal Distribution
04:07
Checking For Normal Distribution
06:17
Standard Normal Distribution and Z-scores
04:21
Confidence Interval-Theory
06:06
Confidence Interval-Computation in R
04:53
Section 4 Quiz
3 questions
+ Statistical Inference
8 lectures 47:18
T-tests: Application in R
10:59
Non-Parametric Alternatives to T-Tests
05:30
One-way ANOVA
07:10
Non-parametric version of One-way ANOVA
02:24
Two-way ANOVA
05:41
Power Test for Detecting Effect
07:44
Section 5 Quiz
3 questions
+ Relationship Between Two Different Quantitative Variables
12 lectures 02:15:30
Correlation
19:50
Linear Regression-Theory
10:44
Linear Regression-Implementation in R
15:26
The Conditions of Linear Regression
12:56
Dealing with Multi-collinearity
16:42
What More Does the Regression Model Tell Us?
13:39
Linear Regression and ANOVA
03:37
Linear Regression With Categorical Variables and Interaction Terms
15:05
Analysis of Covariance (ANCOVA)
07:37
Selecting the Most Suitable Regression Model
13:19
Section 6 Quiz
4 questions
+ Other Types of Regression
10 lectures 01:38:46
Other Regression Techniques When Conditions of OLS Are Not Met
15:38
Model 2 Regression: Standardized Major Axis (SMA) Regression
12:05
Polynomial and Non-linear regression
09:45
Linear Mixed Effect Models
14:07
Generalized Regression Model (GLM)
05:25
Logistic Regression in R
16:18
Poisson Regression in R
06:19
Goodness of fit testing
03:43
Section 7 Quiz
3 questions
+ Multivariate Analysis
9 lectures 01:12:00
Cluster Analysis/Unsupervised Learning
14:31
Principal Component Analysis (PCA)
13:10
Linear Discriminant Analysis (LDA)
12:55
Correspondence Analysis
09:22
Similarity & Dissimilarity Across Sites
07:20
Non-metric multi dimensional scaling (NMDS)
04:07
Multivariate Analysis of Variance (MANOVA)
04:39
Section 8 Quiz
4 questions
+ Miscellaneous Lectures & Information
2 lectures 10:40
Exploratory Data Analysis With xda
04:16
Read in Data from Online HTML Tables-Part 2
06:24