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.
