Interpreting Data Using Statistical Models in R

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Fredrik Hallgren

1:45:31

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01 - Course Overview#

01 - Course Overview.mp4

01:20

02 - Creating Statistical Models

02 - Creating Statistical Models.mp4

05:16

03 - Statistical Models.mp4

04:07

04 - Examples of Statistical Models.mp4

04:32

05 - Types of Data.mp4

04:47

06 - Statistical Paradigms.mp4

04:04

03 - Fitting Statistical Models

07 - Probability Distributions.mp4

06:41

08 - Probability Theory.mp4

04:01

09 - Finding Model Parameters.mp4

06:46

10 - Choosing the Best Estimate.mp4

03:16

11 - Distribution of the Mean and Variance.mp4

03:19

12 - Further Fitting Methods.mp4

02:51

04 - Implementing a Predictive Model - Single-variable Linear Regression

13 - Regression.mp4

03:15

14 - Linear Regression.mp4

04:09

15 - Categorical Predictors.mp4

05:32

16 - Non-linear Regression.mp4

04:58

05 - Drawing Conclusions from Data with Statistical Testing

17 - Statistical Testing.mp4

05:35

18 - The t-test.mp4

05:54

19 - Evaluating Tests.mp4

01:59

20 - Tables of Categorical Data.mp4

02:22

21 - Differences between Multiple Groups.mp4

03:32

22 - A Word of Caution.mp4

02:54

06 - Using Multi-variable Linear Regression#

23 - Multi-variable Linear Regression.mp4

03:07

24 - A Look at Regression Assumptions.mp4

04:04

07 - Ensuring Predictive Accuracy

25 - Prediction Error on New Data.mp4

03:37

26 - Improving Prediction Accuracy.mp4

03:33

Description

This course introduces the most important methods and concepts from statistics with applications in the R programming language. We cover the fitting of statistical models to data, statistical testing, and prediction.

What You'll Learn?

We need principles, models, and theory to make sense of the vast amounts of data generated in today’s world. In this course, Interpreting Data Using Statistical Models in R, you will gain the ability to apply statistical and data science models to any task. First, you will learn how to fit statistical models to data. Next, you will discover how to test for relationships in data. Finally, you will explore how to create predictions with linear regression. When you are finished with this course, you will have the skills needed to turn data into knowledge.

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Statistics

Data Modeling

R Programming

Fredrik Hallgren

Instructor's Courses

Fred Hallgren is a PhD candidate in machine learning at University College London (UCL). His interest for technical teaching was sparked as a teaching assistant in mathematics while an undergraduate and during his PhD he has taught extensively in statistics and statistical programming to undergraduate and masters students. His research at UCL has focused on kernel methods, neural networks and dimensionality reduction. Prior to his PhD he worked at a quantitative hedge fund, researching trading models and developing software infrastructure in Python and C++. Originally from Sweden, he has lived in the UK for several years, but goes back often for skiing and “fika”.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.