Data Analysis and Statistical Modeling in R

About Data Analysis and Statistical Modeling in R

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.