UdemyCourseDownloader Statistics for Data Science and Business Analysis

1. 17. Practical example regression analysis/1. Practical example regression analysis.mp4129.31MB
2. 01. Introduction/1. What does the course cover.mp468.63MB
3. 02. Sample or population data/1. Understanding the difference between a population and a sample.mp458.05MB
4. 03. The fundamentals of descriptive statistics/1. The various types of data we can work with.mp472.6MB
5. 03. The fundamentals of descriptive statistics/3. Levels of measurement.mp454.38MB
6. 03. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.mp438.48MB
7. 03. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.mp425.84MB
8. 03. The fundamentals of descriptive statistics/11. Histogram charts.mp413.79MB
9. 03. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.mp439.8MB
10. 04. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.mp437.12MB
11. 04. Measures of central tendency, asymmetry, and variability/3. Measuring skewness.mp419.41MB
12. 04. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.mp450.94MB
13. 04. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.mp445.21MB
14. 04. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.mp427.48MB
15. 04. Measures of central tendency, asymmetry, and variability/14. The correlation coefficient.mp429.41MB
16. 09. Practical example inferential statistics/1. Practical example inferential statistics.mp4102.59MB
17. 10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.mp492.16MB
18. 10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.mp4112.69MB
19. 10. Hypothesis testing Introduction/6. Type I error vs Type II error.mp443.93MB
20. 11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.mp454.3MB
21. 11. Hypothesis testing Let's start testing!/3. What is the p-value and why is it one of the most useful tools for statisticians.mp455.88MB
22. 11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.mp440.26MB
23. 11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.mp450.45MB
24. 11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).mp429.97MB
25. 11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).mp436.39MB
26. 12. Practical example hypothesis testing/1. Practical example hypothesis testing.mp469.39MB
27. 13. The fundamentals of regression analysis/1. Introduction to regression analysis.mp419.41MB
28. 13. The fundamentals of regression analysis/3. Correlation and causation.mp425.58MB
29. 13. The fundamentals of regression analysis/5. The linear regression model made easy.mp450.99MB
30. 13. The fundamentals of regression analysis/7. What is the difference between correlation and regression.mp412.71MB
31. 13. The fundamentals of regression analysis/9. A geometrical representation of the linear regression model.mp44.91MB
32. 13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.mp445.88MB
33. 14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.mp442.22MB
34. 14. Subtleties of regression analysis/5. The ordinary least squares setting and its practical applications.mp420.05MB
35. 14. Subtleties of regression analysis/7. Studying regression tables.mp436.78MB
36. 14. Subtleties of regression analysis/10. The multiple linear regression model.mp419.1MB
37. 14. Subtleties of regression analysis/12. The adjusted R-squared.mp443.71MB
38. 14. Subtleties of regression analysis/14. What does the F-statistic show us and why do we need to understand it.mp413.9MB
39. 15. Assumptions for linear regression analysis/1. OLS assumptions.mp419.39MB
40. 15. Assumptions for linear regression analysis/3. A1. Linearity.mp412.06MB
41. 15. Assumptions for linear regression analysis/5. A2. No endogeneity.mp432.44MB
42. 15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.mp439.97MB
43. 15. Assumptions for linear regression analysis/9. A4. No autocorrelation.mp425.89MB
44. 15. Assumptions for linear regression analysis/11. A5. No multicollinearity.mp426.59MB
45. 16. Dealing with categorical data/1. Dummy variables.mp438.18MB