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Lecture 20. Parameters and statistics Example: A random sample of 1014 voters are asked if they think the President is too liberal, too conservative,

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Presentation on theme: "Lecture 20. Parameters and statistics Example: A random sample of 1014 voters are asked if they think the President is too liberal, too conservative,"— Presentation transcript:

1 Lecture 20

2 Parameters and statistics Example: A random sample of 1014 voters are asked if they think the President is too liberal, too conservative, or about right. 514 (51%) say ‘too liberal’. – The observed percentage in the sample, 51%, is a statistic. – The unknown percent of the population who would say yes is a parameter. Presumably it’s close to 51%, but we will never know. Question we’d like to ask: “How likely is the statistic to be how close to the parameter?”

3 Sampling experiment em/

4 Experiment Population (urn) – our class (x people) Question: Dogs vs Cats We will sample 5 – Assign numbers – Use R to sample Repeat 5 times ascertain truth and run R

5 US Polls 2012 estimate of the number of eligible voters is 206,072,000. We sampled 1014 people at random and got 514 yes.

6 Main idea We learned that given a model we can check if the data is consistent with it Idea: Find models that are consistent with the data.

7 US Polls 2012 estimate of the number of eligible voters is 206,072,000. We sampled 1014 people at random and got 514 yes. We will consider various models: – True proportion is p = 0.05, 0.10, 0.15, … – Which of the models is the data in agreement with?

8 Results

9 “P-value” Consider proportion of fake samples < 514 Values close to 0 or 1 are not consistent with the model

10 Cutoff Using better resolution of models A usual cutoff 0.05 split between both sides (0.975 and.025) Models selected: [.477,.538]

11 Fake data 3 sub-groups – 42,343,562 (R) – 59,280,986 (D) – 104,447,452 (I) We sample roughly in proportion: – Sampled 220, 201 yes (R) – Sampled 284, 31 yes (D) – Sampled 510, 282 yes (I)

12 Issue Too many levers to “fiddle” (three different Ks for each group) Cannot simply look how well the data fits. Needs more sophisticated statistics

13 Naïve parametric bootstrap Compute a number/numbers estimated from the data (point estimate). Use this number to simulate a lot of fake data and see how the fake data can vary. Use this variability to estimate the uncertainty in our estimator

14 Original problem Estimated K=206,072,000 * (514/1014)=104,458,588 Estimated p in (.476,.537)

15 Stratified problem Estimate K for each group – combine to get joint p estimate pcombined=.20* * *.55=.50 – This is not that much different from ignoring stratification – There is a (small) gain in uncertainty – Bootstrap interval (.474,.525)

16 Bootstrap Stratified

17 Bootstrap SRS

18 Homework Due this Thursday 11/19. Modify the bootstrap codes to get the bootstrap intervals for the population proportion of yes in the following two situations: 1.Population of 30000; SRS sample of 100, observe 32 yes. 2.Population of stratified to subpopulations of and Sample 30 observing 27 from fist subpopulation and sample 70 observing 5 from second subpopulation.


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