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