Applied Statistical Modeling for Data Analysis in R

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Minerva Singh

9:51:37

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1 - Introduction to the Basics of Applied Statistical Modelling

1 - Introduction to the Instructor and Course.mp4

10:46

2 - Data Code Used in the Course.html

2 - Script4Lectures.zip

3 - Statistics in the Real World.mp4

10:08

4 - Designing Studies Collecting Good Quality Data.mp4

08:38

5 - Different Types of Data.mp4

03:37

6 - Conclusion to Section 1.mp4

03:39

2 - Section 2 The Essentials of the R Programming Language

7 - Rationale for this section.html

8 - Introduction to the R Statistical Software R Studio.mp4

11:39

9 - Different Data Structures in R.mp4

14:59

10 - Reading in Data from Different Sources.mp4

15:28

10 - read-inExternalData.txt

11 - Indexing and Subsetting of Data.mp4

11:59

11 - indexingDF.txt

12 - Data Cleaning Removing Missing Values.mp4

17:12

12 - removeNAs.txt

13 - EDA.txt

13 - Exploratory Data Analysis in R.mp4

18:54

14 - Conclusion to Section 2.mp4

02:17

3 - Statistical Tools to Learn More About Your Data

15 - Summarize Quantitative Data.html

16 - Measures of Center.mp4

08:02

16 - meanData.txt

17 - Measures of Variation.mp4

05:48

17 - sd-se1.txt

18 - Charting Graphing Continuous Data.mp4

07:45

18 - boxplt1.txt

19 - Charting Graphing Discrete Data.mp4

14:50

19 - barplt1.txt

20 - Deriving Insights from QualitativeNominal Data.mp4

08:21

20 - chisq1.txt

21 - Conclusions to Section 3.mp4

02:01

4 - Probability Distributions

22 - Background.mp4

03:38

23 - Data Distribution Normal Distribution.mp4

04:07

24 - Checking For Normal Distribution.mp4

06:17

24 - checkNorm.txt

25 - Standard Normal Distribution and Zscores.mp4

04:21

26 - Confidence IntervalTheory.mp4

06:06

27 - CIinR1.txt

27 - Confidence IntervalComputation in R.mp4

04:53

28 - Conclusions to Section 4.mp4

01:24

5 - Statistical Inference

29 - What is Hypothesis Testing.mp4

05:43

30 - Ttests Application in R.mp4

10:59

30 - ttest1.txt

31 - NonParametric Alternatives to TTests.mp4

05:30

31 - mann-wil1.txt

32 - Oneway ANOVA.mp4

07:10

32 - oneAnova1.txt

33 - Nonparametric version of Oneway ANOVA.mp4

02:24

33 - nonP-krusk1.txt

34 - 2wayAn1.txt

34 - Twoway ANOVA.mp4

05:41

35 - Power Test for Detecting Effect.mp4

07:44

36 - Conclusions to Section 5.mp4

02:08

6 - Relationship Between Two Different Quantitative Variables

37 - Explore the Relationship Between Two Quantitative Variables.mp4

04:26

38 - Correlans.txt

38 - Correlation.mp4

19:50

39 - Linear RegressionTheory.mp4

10:44

40 - Linear RegressionImplementation in R.mp4

15:26

40 - regression1.txt

41 - The Conditions of Linear Regression.mp4

12:56

41 - condns-regression1.txt

42 - Dealing with Multicollinearity.mp4

16:42

42 - multicol1.txt

43 - What More Does the Regression Model Tell Us.mp4

13:39

43 - more-lin-reg1.txt

44 - Linear Regression and ANOVA.mp4

03:38

45 - Linear Regression With Categorical Variables and Interaction Terms.mp4

15:05

45 - intercnreg.txt

46 - Analysis of Covariance ANCOVA.mp4

07:37

46 - ancova1.txt

47 - Selecting the Most Suitable Regression Model.mp4

13:19

47 - model-selcn.txt

48 - Conclusions to Section 6.mp4

02:10

7 - Other Types of Regression

49 - Lecture49-transform.txt

49 - Violation of Linear Regression Conditions Transform Variables.mp4

12:17

50 - Lecture50-others-regs1.txt

50 - Other Regression Techniques When Conditions of OLS Are Not Met.mp4

15:38

51 - Model 2 Regression Standardized Major Axis SMA Regression.mp4

12:05

51 - sma1.txt

52 - Polynomial and Nonlinear regression.mp4

09:45

52 - poly1.txt

53 - Linear Mixed Effect Models.mp4

14:07

53 - linMix.txt

54 - Generalized Regression Model GLM.mp4

05:25

55 - Logistic Regression in R.mp4

16:18

55 - logisticreg1.txt

56 - Poisson Regression in R.mp4

06:20

56 - poisson1.txt

57 - Goodness of fit testing.mp4

03:43

58 - Conclusions to Section 7.mp4

03:09

8 - Multivariate Analysis

59 - Why Do Multivariate Analysis.mp4

03:18

60 - Cluster AnalysisUnsupervised Learning.mp4

14:31

60 - cluster1.txt

61 - Principal Component Analysis PCA.mp4

13:10

61 - pca-r.txt

62 - Linear Discriminant Analysis LDA.mp4

12:55

62 - lda.txt

63 - Correspondence Analysis.mp4

09:22

63 - ca-r.txt

64 - Similarity Dissimilarity Across Sites.mp4

07:20

65 - Nonmetric multi dimensional scaling NMDS.mp4

04:07

65 - nmds1.txt

66 - Multivariate Analysis of Variance MANOVA.mp4

04:39

66 - manova1.txt

67 - Conclusions to Section 8.mp4

02:38

9 - Miscellaneous Lectures Information

68 - Exploratory Data Analysis With xda.mp4

04:16

68 - xda.txt

69 - Read in Data from Online HTML TablesPart 1.mp4

04:13

69 - readHTML-xml.txt

70 - Read in Data from Online HTML TablesPart 2.mp4

06:24

70 - readHTML-rcurl.txt

71 - Use R in Colab.mp4

06:07

72 - Summarise By Time.mp4

06:39

73 - POSIT.mp4

03:31

Description

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

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

Who is this 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

What You Need to Know?

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

More details

Description
Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â APPLIED STATISTICAL MODELINGÂ FOR DATA ANALYSIS IN R
COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R
Â Â Â Â Â Â Â Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing?Â
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 youâ€™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, IÂ 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 toÂ complete in order to get a head start in practical statistical modelling for data analysis using R.Â

My course has 9.5 hours of lectures andÂ 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.
Â
GET ACCESS TO A COURSE THAT IS JAM-PACKED WITH TONS OF APPLICABLE INFORMATION! AND GETÂ AÂ FREE VIDEOÂ COURSEÂ INÂ MACHINEÂ LEARNINGÂ ASÂ WELL!
This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous trainingÂ IÂ 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, hereâ€™s what the course will do for you:

Â Â (a) It willÂ 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.

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

Â Â (c) It willÂ 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.
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Â Â (d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis.
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Â Â (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.
Â
The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.
Â
After each video, you will learn a new concept or technique which you may apply to your own projects immediately!
Â
TAKE ACTION NOW :) Youâ€™ll also have my continuous support when you take this course just to make sure youâ€™re successful with it. Â If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case youâ€™re not completely satisfied with the course.
TAKE ACTION TODAY! I will personally support you and ensure your experience with thisÂ 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

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I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

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