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Data Analysis and Statistical Modeling in R

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Data Analysis and Statistical Modeling in R, available at $64.99, has an average rating of 4.1, with 37 lectures, 4 quizzes, based on 71 reviews, and has 10081 subscribers.

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You will learn about Statistical modelling in R with real world examples and datasets Develop and execute Hypothesis 1-tailed and 2-tailed tests in R Test differences, durability and data limitations Custom Data visualisations using R with limitations and interpretation Applications of Statistical tests Understand statistical Data Distributions and their functions in R How to interpret different output values and make conclusions To pick suitable statistical technique according to problem To pick suitable visualisation technique according to problem R packages which can improve statistical modelling This course is ideal for individuals who are University and college data science students or Data Science aspirants or Beginners who want to perform statistical modelling and learn about its applications or people who want to shift from SPSS and EXCEL to R to perform statistical analysis It is particularly useful for University and college data science students or Data Science aspirants or Beginners who want to perform statistical modelling and learn about its applications or people who want to shift from SPSS and EXCEL to R to perform statistical analysis.

Enroll now: Data Analysis and Statistical Modeling in R

Summary

Title: Data Analysis and Statistical Modeling in R

Price: $64.99

Average Rating: 4.1

Number of Lectures: 37

Number of Quizzes: 4

Number of Published Lectures: 37

Number of Published Quizzes: 4

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

  • Statistical modelling in R with real world examples and datasets
  • Develop and execute Hypothesis 1-tailed and 2-tailed tests in R
  • Test differences, durability and data limitations
  • Custom Data visualisations using R with limitations and interpretation
  • Applications of Statistical tests
  • Understand statistical Data Distributions and their functions in R
  • How to interpret different output values and make conclusions
  • To pick suitable statistical technique according to problem
  • To pick suitable visualisation technique according to problem
  • R packages which can improve statistical modelling

Who Should Attend

  • University and college data science students
  • Data Science aspirants
  • Beginners who want to perform statistical modelling and learn about its applications
  • people who want to shift from SPSS and EXCEL to R to perform statistical analysis

Target Audiences

  • University and college data science students
  • Data Science aspirants
  • Beginners who want to perform statistical modelling and learn about its applications
  • people who want to shift from SPSS and EXCEL to R to perform statistical analysis

Before applying any data science model its always a good practice to understand the true nature of your data. In this Course we will cover fundamentals and applications of statistical modelling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution and statistical concepts then by using plots and charts we will interpret our data. We will use statistical modelling to prove our claims and use hypothesis testing to confidently make inferences.

This course is divided into 3 Parts

In the 1st section we will cover following concepts

1. Normal Distribution

2. Binomial Distribution

3. Chi-Square Distribution

4. Densities

5. Cumulative Distribution function CDF

6. Quantiles

7. Random Numbers

8. Central Limit Theorem CLT

9. R Statistical Distribution

10. Distribution Functions

11. Mean

12. Median

13. Range

14. Standard deviation

15. Variance

16. Sum of squares

17. Skewness

18. Kurtosis

2nd Section

1. Bar Plots

2. Histogram

3. Pie charts

4. Box plots

5. Scatter plots

6. Dot Charts

7. Mat Plots

8. Plots for groups

9. Plotting datasets

3rd Section of this course will elaborate following concepts

1. Parametric tests

2. Non-Parametric Tests

3. What is statistically significant means?

4. P-Value

5. Hypothesis Testing

6. Two-Tailed Test

7. One Tailed Test

8. True Population mean

9. Hypothesis Testing

10. Proportional Test

11. T-test

12. Default t-test / One sample t-test

13. Two-sample t-test / Independent Samples t-test

14. Paired sample t-test

15. F-Tests

16. Mean Square Error MSE

17. F-Distribution

18. Variance

19. Sum of squares

20. ANOVA Table

21. Post-hoc test

22. Tukey HSD

23. Chi-Square Tests

24. One sample chi-square goodness of fit test

25. chi-square test for independence

26. Correlation

27. Pearson Correlation

28. Spearman Correlation

In all the analysis we will practically see the real world applications using data sets csv files and r built in Datasets and packages.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Promo Video

Lecture 2: Introduction

Lecture 3: All Exercise files downloadable link

Lecture 4: Get maximum of Learning

Lecture 5: Installing and Configuring R

Lecture 6: Navigating R studio

Chapter 2: Statistical Data Distribution

Lecture 1: Math Functions in R

Lecture 2: Basic Statistical Concepts

Lecture 3: Statistical Distributions

Lecture 4: Statistical Distribution Functions

Lecture 5: Data Distribution and Simulation in R

Chapter 3: Plots and Charts

Lecture 1: Bar plot

Lecture 2: Bar plots for groups

Lecture 3: Pie Charts and Graphical Parameters

Lecture 4: Finishing Pie charts

Lecture 5: Histograms

Lecture 6: Understanding Urban Population of US using Histogram6

Lecture 7: Box Plots

Lecture 8: Box plots for groups

Lecture 9: Scatter Plots

Lecture 10: Mat Plots

Chapter 4: Statistical Tests and Applications

Lecture 1: Statistical tests PDF for reference

Lecture 2: statistical tests

Lecture 3: Power of a test; Type 1 and 2 errors

Lecture 4: Data Distribution and Simulation Finished

Lecture 5: Single Proportional Test

Lecture 6: Double Proportion

Lecture 7: T-Test Overview

Lecture 8: One Sample T-Test Default T-Test

Lecture 9: Two sample T-Test Independent sample T-test

Lecture 10: Paired T-Test

Lecture 11: F-Test ANOVA Tukey HSD

Lecture 12: Performing F-Test ANOVA Tukey HSD

Lecture 13: Chi Square One Sample Goodness of fit Test

Lecture 14: Chi-Square test for Independence

Lecture 15: Correlation Test

Chapter 5: Good Bye

Lecture 1: See yOu

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 12 votes
  • 4 stars: 28 votes
  • 5 stars: 29 votes

Frequently Asked Questions


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