1. Introduction to Econometrics
The Statistical Analysis of Economic (and related) Data

2. Brief Overview of the Course

3. This course is about using data to measure causal effects.

4. In this course you will:

5. Review of Probability and Statistics (SW Chapters 2, 3)

6. The California Test Score Data Set

7. Initial look at the data: (You should already know how to interpret this table)
This table doesn’t tell us anything about the relationship between test scores and the STR.

8. Do districts with smaller classes have higher test scores
Do districts with smaller classes have higher test scores? Scatterplot of test score v. student-teacher ratio
What does this figure show?

9. We need to get some numerical evidence on whether districts with low STRs have higher test scores – but how?

10. Standard deviation (sBYB)
Initial data analysis: Compare districts with “small” (STR < 20) and “large” (STR ≥ 20) class sizes:
Class Size
Average score
( )
Standard deviation (sBYB) n
Small
657.4
19.4
238
Large
650.0
17.9
182
1. Estimation of  = difference between group means
2. Test the hypothesis that  = 0
3. Construct a confidence interval for 

11. 1. Estimation

12. 2. Hypothesis testing

13. Compute the difference-of-means t-statistic:

14. 3. Confidence interval

15. What comes next…

16. Review of Statistical Theory

17. (a) Population, random variable, and distribution

18. Population distribution of Y

19. (b) Moments of a population distribution: mean, variance, standard deviation, covariance, correlation

20. Moments, ctd.

22. 2 random variables: joint distributions and covariance

23. The covariance between Test Score and STR is negative:
so is the correlation…

24. The correlation coefficient is defined in terms of the covariance:

25. The correlation coefficient measures linear association

26. (c) Conditional distributions and conditional means

27. Conditional mean, ctd.

28. (d) Distribution of a sample of data drawn randomly from a population: Y1,…, Yn

29. Distribution of Y1,…, Yn under simple random sampling

31. (a) The sampling distribution of

32. The sampling distribution of , ctd.

33. The sampling distribution of when Y is Bernoulli (p = .78):

34. Things we want to know about the sampling distribution:

35. The mean and variance of the sampling distribution of

37. Mean and variance of sampling distribution of , ctd.

38. The sampling distribution of when n is large

39. The Law of Large Numbers:

40. The Central Limit Theorem (CLT):

41. Sampling distribution of when Y is Bernoulli, p = 0.78:

42. Same example: sampling distribution of :

43. Summary: The Sampling Distribution of

44. (b) Why Use To Estimate Y?

45. Why Use To Estimate Y?, ctd.

48. Calculating the p-value, ctd.

49. Calculating the p-value with Y known:

50. Estimator of the variance of Y:

51. Computing the p-value with estimated:

52. What is the link between the p-value and the significance level?

53. At this point, you might be wondering,...

54. Comments on this recipe and the Student t-distribution

55. Comments on Student t distribution, ctd.

56. Comments on Student t distribution, ctd.

58. Comments on Student t distribution, ctd.

59. The Student-t distribution – summary

61. Confidence intervals, ctd.

62. Summary:

63. Let’s go back to the original policy question: