1. Confidence Intervals with Means

2. What is the purpose of a confidence interval? To estimate an unknown population parameter

3. Formula: statistic Critical value Standard deviation of statistic Margin of error

4. In a randomized comparative experiment on the effects of calcium on blood pressure, researchers divided 54 healthy, white males at random into two groups, taking calcium or placebo. The paper reports a mean seated systolic blood pressure of 114.9 with standard deviation of 9.3 for the placebo group. Assume systolic blood pressure is normally distributed. Can you find a z-interval for this problem? Why or why not?

5. Student’s t- distribution Continuous distribution Unimodal, symmetrical, bell-shaped density curve Above the horizontal axis Area under the curve equals 1 Based on degrees of freedom df = n - 1

6. Formula: statistic Critical value Standard deviation of statistic Margin of error Standard error – when you substitute s for .

7. How to find t* Use Table B for t distributions Look up confidence level at bottom & df on the sides df = n – 1 Find these t* 90% confidence when n = 5 95% confidence when n = 15 t* = 2.132 t* = 2.145 Can also use invT on the calculator! Need upper t* value with 5% is above – so 95% is below invT(p,df)

8. Steps for doing a confidence interval: 1)Assumptions – 2)Calculate the interval 3)Write a statement about the interval in the context of the problem.

9. Statement: (memorize!!) We are ________% confident that the true mean context is between ______ and ______.

10. Assumptions for t-inference Have an SRS from population (or randomly assigned treatments)  unknown Normal (or approx. normal) distribution –Given –Large sample size –Check graph of data Use only one of these methods to check normality

11. Ex. 1) Find a 95% confidence interval for the true mean systolic blood pressure of the placebo group. Assumptions: Have randomly assigned males to treatment Systolic blood pressure is normally distributed (given).  is unknown We are 95% confident that the true mean systolic blood pressure is between 111.22 and 118.58.

12. Find a sample size: If a certain margin of error is wanted, then to find the sample size necessary for that margin of error use: Always round up to the nearest person!

13. Ex 4) The heights of PWSH male students is normally distributed with  = 2.5 inches. How large a sample is necessary to be accurate within +.75 inches with a 95% confidence interval? n = 43

14. Hypothesis Tests Hypothesis Tests One Sample Means

15. A government agency has received numerous complaints that a particular restaurant has been selling underweight hamburgers. The restaurant advertises that it’s patties are “a quarter pound” (4 ounces). How can I tell if they really are underweight? Take a sample & find x. expect unlikely But how do I know if this x is one that I expect to happen or is it one that is unlikely to happen? A hypothesis test will allow me to decide if the claim is true or not!

16. Steps for doing a hypothesis test 1)Assumptions 2)Write hypotheses & define parameter 3)Calculate the test statistic & p-value 4)Write a statement in the context of the problem. H 0 :  = 12 vs H a :  (, or ≠) 12 “Since the p-value ) , I reject (fail to reject) the H 0. There is (is not) sufficient evidence to suggest that H a (in context).”

17. Formulas:  unknown: t = 

18. Calculating p-values For z-test statistic – –Use normalcdf(lb,rb) –[using standard normal curve] For t-test statistic – –Use tcdf(lb, rb, df)

19. Draw & shade a curve & calculate the p-value: 1) right-tail test t = 1.6; n = 20 2) two-tail testt = 2.3; n = 25 P-value =.0630 P-value = (.0152)2 =.0304

20. Example 1: Bottles of a popular cola are supposed to contain 300 mL of cola. There is some variation from bottle to bottle. An inspector, who suspects that the bottler is under-filling, measures the contents of six randomly selected bottles. Is there sufficient evidence that the bottler is under-filling the bottles? Use  =.1 299.4 297.7 298.9 300.2 297 301

21. I have an SRS of bottles Since the boxplot is approximately symmetrical with no outliers, the sampling distribution is approximately normally distributed  is unknown SRS? p-value =.0880  =.1 Normal? How do you know? H 0 :  = 300where  is the true mean amount H a :  < 300 of cola in bottles What are your hypothesis statements? Is there a key word? Plug values into formula. Do you know  ? Since p-value < , I reject the null hypothesis. There is sufficient evidence to suggest that the true mean cola in the bottles is less than 300 mL. Compare your p-value to  & make decision Write conclusion in context in terms of H a.

22. Matched Pairs Test A special type of t-inference

23. Matched Pairs – two forms Pair individuals by certain characteristics Randomly select treatment for individual A Individual B is assigned to other treatment Assignment of B is dependent on assignment of A Individual persons or items receive both treatments Order of treatments are randomly assigned before & after measurements are taken The two measures are dependent on the individual

24. Is this an example of matched pairs? 1)A college wants to see if there’s a difference in time it took last year’s class to find a job after graduation and the time it took the class from five years ago to find work after graduation. Researchers take a random sample from both classes and measure the number of days between graduation and first day of employment No, there is no pairing of individuals, you have two independent samples

25. Is this an example of matched pairs? 2) In a taste test, a researcher asks people in a random sample to taste a certain brand of spring water and rate it. Another random sample of people is asked to taste a different brand of water and rate it. The researcher wants to compare these samples No, there is no pairing of individuals, you have two independent samples – If you would have the same people taste both brands in random order, then it would be an example of matched pairs.

26. Is this an example of matched pairs? 3) A pharmaceutical company wants to test its new weight-loss drug. Before giving the drug to a random sample, company researchers take a weight measurement on each person. After a month of using the drug, each person’s weight is measured again. Yes, you have two measurements that are dependent on each individual.

27. A whale-watching company noticed that many customers wanted to know whether it was better to book an excursion in the morning or the afternoon. To test this question, the company collected the following data on 15 randomly selected days over the past month. (Note: days were not consecutive.) Day123456789101112131415 Morning 897910131082577687 After- noon 810989118104789669 First, you must find the differences for each day. Since you have two values for each day, they are dependent on the day – making this data matched pairs You may subtract either way – just be careful when writing H a

28. Day 123456789101112131415 Morning 897910131082577687 After- noon 810989118104789669 Differenc es 0-21122 -202 Assumptions: Have an SRS of days for whale-watching  unknown Since the normal probability plot is approximately linear, the distribution of difference is approximately normal. I subtracted: Morning – afternoon You could subtract the other way! You need to state assumptions using the differences! Notice the granularity in this plot, it is still displays a nice linear relationship!

29. Differences 0-21122 -202 Is there sufficient evidence that more whales are sighted in the afternoon? Be careful writing your H a ! Think about how you subtracted: M-A If afternoon is more should the differences be + or -? Don’t look at numbers!!!! H 0 :  D = 0 H a :  D < 0 Where  D is the true mean difference in whale sightings from morning minus afternoon Notice we used  D for differences & it equals 0 since the null should be that there is NO difference. If you subtract afternoon – morning; then H a :  D >0

30. finishing the hypothesis test: Since p-value > , I fail to reject H 0. There is insufficient evidence to suggest that more whales are sighted in the afternoon than in the morning. Notice that if you subtracted A-M, then your test statistic t = +.945, but p- value would be the same In your calculator, perform a t-test using the differences (L3) Differences 0-21122 -202 How could I increase the power of this test?