• Урок 1. 00:03:55
    What does the course cover?
  • Урок 2. 00:04:03
    Understanding the difference between a population and a sample
  • Урок 3. 00:04:34
    The various types of data we can work with
  • Урок 4. 00:03:44
    Levels of measurement
  • Урок 5. 00:04:53
    Categorical variables. Visualization techniques for categorical variables
  • Урок 6. 00:03:10
    Numerical variables. Using a frequency distribution table
  • Урок 7. 00:02:15
    Histogram charts
  • Урок 8. 00:04:45
    Cross tables and scatter plots
  • Урок 9. 00:04:21
    The main measures of central tendency: mean, median and mode
  • Урок 10. 00:02:38
    Measuring skewness
  • Урок 11. 00:05:56
    Measuring how data is spread out: calculating variance
  • Урок 12. 00:04:41
    Standard deviation and coefficient of variation
  • Урок 13. 00:03:24
    Calculating and understanding covariance
  • Урок 14. 00:03:18
    The correlation coefficient
  • Урок 15. 00:16:16
    Practical example
  • Урок 16. 00:01:01
    Introduction to inferential statistics
  • Урок 17. 00:04:34
    What is a distribution?
  • Урок 18. 00:03:55
    The Normal distribution
  • Урок 19. 00:03:31
    The standard normal distribution
  • Урок 20. 00:04:21
    Understanding the central limit theorem
  • Урок 21. 00:01:28
    Standard error
  • Урок 22. 00:03:08
    Working with estimators and estimates
  • Урок 23. 00:02:42
    Confidence intervals - an invaluable tool for decision making
  • Урок 24. 00:08:02
    Calculating confidence intervals within a population with a known variance
  • Урок 25. 00:04:39
    Confidence interval clarifications
  • Урок 26. 00:03:23
    Student's T distribution
  • Урок 27. 00:04:37
    Calculating confidence intervals within a population with an unknown variance
  • Урок 28. 00:04:53
    What is a margin of error and why is it important in Statistics?
  • Урок 29. 00:06:05
    Calculating confidence intervals for two means with dependent samples
  • Урок 30. 00:04:32
    Calculating confidence intervals for two means with independent samples (part 1)
  • Урок 31. 00:03:58
    Calculating confidence intervals for two means with independent samples (part 2)
  • Урок 32. 00:01:28
    Calculating confidence intervals for two means with independent samples (part 3)
  • Урок 33. 00:10:07
    Practical example: inferential statistics
  • Урок 34. 00:05:53
    The null and the alternative hypothesis
  • Урок 35. 00:07:06
    Establishing a rejection region and a significance level
  • Урок 36. 00:04:15
    Type I error vs Type II error
  • Урок 37. 00:06:35
    Test for the mean. Population variance known
  • Урок 38. 00:04:14
    What is the p-value and why is it one of the most useful tools for statisticians
  • Урок 39. 00:04:49
    Test for the mean. Population variance unknown
  • Урок 40. 00:05:19
    Test for the mean. Dependent samples
  • Урок 41. 00:04:23
    Test for the mean. Independent samples (Part 1)
  • Урок 42. 00:04:27
    Test for the mean. Independent samples (Part 2)
  • Урок 43. 00:07:17
    Practical example: hypothesis testing
  • Урок 44. 00:01:03
    Introduction to regression analysis
  • Урок 45. 00:04:13
    Correlation and causation
  • Урок 46. 00:05:51
    The linear regression model made easy
  • Урок 47. 00:01:44
    What is the difference between correlation and regression?
  • Урок 48. 00:01:26
    A geometrical representation of the linear regression model
  • Урок 49. 00:05:46
    A practical example - Reinforced learning
  • Урок 50. 00:03:38
    Decomposing the linear regression model - understanding its nuts and bolts
  • Урок 51. 00:05:25
    What is R-squared and how does it help us?
  • Урок 52. 00:02:24
    The ordinary least squares setting and its practical applications
  • Урок 53. 00:04:55
    Studying regression tables
  • Урок 54. 00:02:56
    The multiple linear regression model
  • Урок 55. 00:05:25
    The adjusted R-squared
  • Урок 56. 00:02:02
    What does the F-statistic show us and why do we need to understand it?
  • Урок 57. 00:02:22
    OLS assumptions
  • Урок 58. 00:01:51
    A1. Linearity
  • Урок 59. 00:04:10
    A2. No endogeneity
  • Урок 60. 00:05:48
    A3. Normality and homoscedasticity
  • Урок 61. 00:03:15
    A4. No autocorrelation
  • Урок 62. 00:03:27
    A5. No multicollinearity
  • Урок 63. 00:05:04
    Dummy variables
  • Урок 64. 00:14:10
    Practical example: regression analysis
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