1. Vegas Baby A trip to Vegas is just a sample of a random variable (i.e. 100 card games, 100 slot plays or 100 video poker games) Which is more likely? Win on a short trip Win on a long trip

2. Vegas Baby House likely to win in the long run, but loses in the short run all the time. Player likely to lose in the long run, but wins in the short run all the time

3. Example: Blackjack What if you went to Vegas 1000 times Each weekend played exactly 8 hands? How often would you win all 8 hands? How often would you win 6 out of 8 hands? How often would you lose all 8 hands?

4. Example: Blackjack The outcomes of each trip would be on a distribution. This distribution would be affected by: The distribution of each blackjack event Sampling (i.e. luck)

5. Distribution – 8 Hands ___ Win.49 Lose.51 Winning or losing in the short run is greatly affected by LUCK

6. Distribution – 256 Hands

7. Flipping out Very important concept. The mean of a larger sample is more likely to be close to expected value. Flip 1,000,000 coins then proportion of heads is very close to.5 (e.g..4993). Flip 100 coins then number of head is not so close to.5 (e.g..7). Almost impossible to get.7 heads with a 1,000,000 flips

8. In class demo: flip 10 coins

9. Go to online text sampling demo Sampling Mean Distribution

10. 1)MEAN OF A DISTRIBUTION OF MEANS = MEAN OF POPULATION OF INDIVIDUAL CASES CENTRAL LIMIT THEOREM 2) THE SPREAD OF A DISTRIBUTION OF MEANS IS LESS THAN THE SPREAD OF THE POPULATION OF INDIVIDUAL CASES = STANDARD ERROR

11. 3) THE SHAPE OF THE DISTRIBUTION OF MEANS TENDS TO BE NORMAL (REGARDLESS OF SHAPE OF DISTRIBUTION OF X, IF n > 30) CENTRAL LIMIT THEOREM

12. Sampling with and without Replacement Let's look at how sampling with and without replacement works. In this example, we have a population of 1,3,5 and 7. The sample size is 2.

13. Sampling with and without Replacement With four scores in the population, there are 4 times 4 equals 16 samples with two scores in the sample.

14. Sampling with and without Replacement These 12 combinations of scores involve a sample where the first score and second score are not the same. These are all the combinations when sampling without replacement is used.

15. Sampling with and without Replacement When sampling with replacement is used, then there are some additional samples that are possible. These four combinations of scores involve a sample where the same score was selected twice. Therefore, sampling with replacement always involves a larger number of possible samples than sampling without replacement.

16. With Replacement Central Limit Theorem Holds Without Replacement Central Limit Theorem DOES NOT Hold But with large samples, this is not a big problem Sampling Mean Distribution

17. Good samples and Bad samples When making an estimate of a population, you want your sample to be a random subset of the population. This is often not the case in some samples that are commonly reported. Example: Political opinion polls that sample by telephone – why?

18. Consider a population distribution of means that come from a distribution of the weights of men. The mean of the score distribution (μ)=150 and the standard deviation = 10 and the means are from sample size = 25. The maximum score value is 160. What is the proportion of means above a mean of 152? Proportion Problem for Means

19. Is a Mean Distribution Normal?

20. Consider a population distribution of means that come from a distribution of the weights of men and women. The mean of the score distribution (μ)=150 and the standard deviation = 10 and the means are from sample size = 36. What is the proportion of means above a mean of 152? Proportion Problem for Means

22. t distributions Area problems so far use standard error form population scores But we usually don’t have population information but we do have sample information What if we estimate standard error with..

23. t distributions This is the ‘estimated standard error’ which we can use to calculate how far a mean is away from the mean of mean distribution Example: –Sample mean =102 –Population mean = 100 –Estimated standard error = 2 –This sample mean is (102-100)/2=1 estimated standard errors away from the population mean

24. t distributions The term for this distance of a mean away from mean of means distribution is called the t statistics Similar to a z-score for means, but used with estimated standard errors instead of actual population standard errors. Can calculate areas under t distribution with t statistics similar to area under normal curve for z

25. t distribution PROBLEM: Because we estimated the standard error, this t statistic has error in it, and the error depends upon the sample size EFFECT: t distribution that defines areas for the t statistic is affected by sample size.

27. t distribution BIG PROBLEM: If we want to calculate areas for t distributions, then we need 1 complete table of values (like z area table) for each sample size. SOLUTION: Only provide info for certain important areas that are useful for hypothesis testing and confidence intervals

28. T critical values.05(2) = 5% in two tails

29. Range is specified by: Lower value < μ < Higher value Examples 100 < μ < 110 0 < μ < 120 Use of Sample Means: Confidence Intervals

30. What if we have a score distribution with a standard deviation If we collect a sample mean X, then how accurate is that mean? In other words, can we specify a range of population means that are likely to be true? Use of Sample Means: Confidence Intervals _

31. Confidence intervals can specify different degrees of confidence, for example: 95% (want to be 95% sure μ is in range) 99% (want to be 99% sure μ is in range) 99.9% (want to be 99.9% sure μ is in range) Use of Sample Means: Confidence Intervals

32. σ X known. Z distribution. (Not typical -- easier) σ X unknown. t distributions. (Typical - harder) Solving Confidence Intervals Two types

33. Use normal distribution Start with score distribution Mean distribution is times skinnier Formula Solving Confidence Intervals When σ x is known

34. If we have a sample mean of 85 and sample size of 16 and a σ X =10, then what range of population values can we be 95% sure contain the true population mean? Use of Sample Means: Confidence Intervals With Z distribution

37. If we have a sample mean of 85 and sample size of 16 and a σ X =10, then what range of population values can we be 99% sure contain the true population mean? Use of Sample Means: Confidence Intervals With Z distribution

40. If we have a sample mean of 85 and sample size of 16 and a S X =10, then what range of population values can we be 95% sure contain the true population mean? Use of Sample Means: Confidence Intervals With t Distribution

41. If we have a sample mean of 85 and sample size of 16 and a S X =10, then what range of population values can we be 95% sure contain the true population mean? Solution: