[FreeCourseLab.com] Udemy - Statistics for Data Science and Business Analysis

5. Practical example descriptive statistics/1. Practical example.mp4

17. Practical example regression analysis/1. Practical example regression analysis.mp4

10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.mp4

9. Practical example inferential statistics/1. Practical example inferential statistics.mp4

10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.mp4

7. Estimators and estimates/5. Calculating confidence intervals within a population with a known variance.mp4

3. The fundamentals of descriptive statistics/1. The various types of data we can work with.mp4

8. Confidence intervals advanced topics/1. Calculating confidence intervals for two means with dependent samples.mp4

12. Practical example hypothesis testing/1. Practical example hypothesis testing.mp4

1. Introduction/1. What does the course cover.mp4

6. Distributions/9. Understanding the central limit theorem.mp4

6. Distributions/2. What is a distribution.mp4

7. Estimators and estimates/12. What is a margin of error and why is it important in Statistics.mp4

2. Sample or population data/1. Understanding the difference between a population and a sample.mp4

7. Estimators and estimates/7. Confidence interval clarifications.mp4

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.mp4

3. The fundamentals of descriptive statistics/3. Levels of measurement.mp4

11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.mp4

13. The fundamentals of regression analysis/5. The linear regression model made easy.mp4

4. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.mp4

11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.mp4

7. Estimators and estimates/3. Confidence intervals - an invaluable tool for decision making.mp4

6. Distributions/4. The Normal distribution.mp4

7. Estimators and estimates/1. Working with estimators and estimates.mp4

13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.mp4

4. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.mp4

10. Hypothesis testing Introduction/6. Type I error vs Type II error.mp4

14. Subtleties of regression analysis/12. The adjusted R-squared.mp4

14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.mp4

11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.mp4

15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.mp4

3. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.mp4

3. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.mp4

16. Dealing with categorical data/1. Dummy variables.mp4

4. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.mp4

14. Subtleties of regression analysis/7. Studying regression tables.mp4

14. Subtleties of regression analysis/3. What is R-squared and how does it help us.mp4

11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).mp4

7. Estimators and estimates/8. Student's T distribution.mp4

15. Assumptions for linear regression analysis/5. A2. No endogeneity.mp4

7. Estimators and estimates/10. Calculating confidence intervals within a population with an unknown variance.mp4

11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).mp4

4. Measures of central tendency, asymmetry, and variability/14. The correlation coefficient.mp4

8. Confidence intervals advanced topics/3. Calculating confidence intervals for two means with independent samples (part 1).mp4

4. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.mp4

8. Confidence intervals advanced topics/5. Calculating confidence intervals for two means with independent samples (part 2).mp4

15. Assumptions for linear regression analysis/11. A5. No multicollinearity.mp4

15. Assumptions for linear regression analysis/9. A4. No autocorrelation.mp4

3. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.mp4

13. The fundamentals of regression analysis/3. Correlation and causation.mp4