LECTURE 12 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING

Transcription

1 LECTURE 1 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING

2 INTERVAL ESTIMATION Point estimation of : The inference is a guess of a single value as the value of. No accuracy associated with it. Interval estimation for : Specify an interval in which the unknown parameter,, is likely to lie. It contains measure of accuracy through variance.

3 INTERVAL ESTIMATION An interval with random end points is called a random interval. E.g., 5X 5X Pr X 5X, 8 3 is a random interval that contains the true value of with probability 0.95.

4 An interval (l(x 1,x,,x n ), u(x 1,x,,x n )) is called a 100 % confidence interval (CI) for if Pr l x, x,, x u x, x,, x where 0<<1. 1 n 1 The observed values l(x 1,x,,x n ) is a lower confidence limit and u(x 1,x,,x n ) is an upper confidence limit. The probability is called the confidence coefficient or the confidence level. n

5 If Pr(l(x 1,x,,x n ))=, then l(x 1,x,,x n ) is called a one-sided lower 100 % confidence limit for. If Pr( u(x 1,x,,x n ))=, then u(x 1,x,,x n ) is called a one-sided upper 100 % confidence limit for.

6 APPROXIMATE CI USING CLT Let X 1,X,,X n be a r.s. By CLT, X E X X V X / n Non-normal random sample d N 0,1 The approximate 100(1 )% random interval for μ: PX z/ X z/ 1 n n The approximate 100(1 )% CI for μ: x z x z n / / n

7 Usually, is unknown. So, the approximate 100(1)% CI for : s x t x t n /, n1 /, n1 When the sample size n 30, t /,n-1 ~N(0,1). s Non-normal random sample n x z s x z n / / s n

8 GRAPHICAL DEMONSTRATION OF THE CONFIDENCE INTERVAL FOR 1 - a Confidence level x Lower confidence limit z n Upper confidence limit

9 Example Suppose that = 1.71 and n=100. Use a 90% confidence level x z x x. 8 n 100 Recalculate the confidence interval for 95% confidence level. x x.34

10 The width of the 90% confidence interval = (.8) =.56 The width of the 95% confidence interval = (.34) =.68 Because the 95% confidence interval is wider, it is more likely to include the value of. With 95% confidence interval, we allow ourselves to make 5% error; with 90% CI, we allow for 10%.

11 THE WIDTH OF THE CONFIDENCE INTERVAL The width of the confidence interval is affected by the population standard deviation () To maintain a certain level of confidence, a larger standard deviation requires a larger confidence interval. the confidence level (1-) Larger confidence level produces a wider confidence interval the sample size (n). Increasing the sample size decreases the width of the confidence interval while the confidence level can remain unchanged.

12 INFERENCE ABOUT THE POPULATION MEAN WHEN IS UNKNOWN Standard Normal Student t 0

13 Degrees of Freedom FINDING T-SCORES UNDER A T-DISTRIBUTION (T-TABLES) t.100 t.05 t.05 t.01 t t 0.05, 10 = t

14 EXAMPLE A new breakfast cereal is test-marked for 1 month at stores of a large supermarket chain. The result for a sample of 16 stores indicate average sales of $100 with a sample standard deviation of $180. Set up 99% confidence interval estimate of the true average sales of this new breakfast cereal. Assume normality. n 16,x $100,s $180, 0.01 t t.947 /,n ,15

15 99% CI for : s 180 x t /,n1 n 16 ( , ) With 99% confidence, the limits and cover the true average sales of the new breakfast cereal.

16 HYPOTHESIS TESTING A hypothesis is a statement about a population parameter. The goal of a hypothesis test is to decide which of two complementary hypothesis is true, based on a sample from a population. STATISTICAL TEST: The statistical procedure to draw an appropriate conclusion from sample data about a population parameter. HYPOTHESIS: Any statement concerning an unknown population parameter. Aim of a statistical test: test an hypothesis concerning the values of one or more population parameters.

17 NULL AND ALTERNATIVE HYPOTHESIS NULL HYPOTHESIS=H 0 E.g., a treatment has no effect or there is no change compared with the previous situation. ALTERNATIVE HYPOTHESIS=H A E.g., a treatment has a significant effect or there is development compared with the previous situation.

18 Sample Space, A: Set of all possible values of sample values x 1,x,,x n.(x 1,x,,x n ) A Parameter Space, : Set of all possible values of the parameters. = 0 1 H 0 : 0 H 1 : 1 Critical Region, C is a subset of A which leads to rejection region of H 0. Reject H 0 if (x 1,x,,x n )C Not Reject H 0 if (x 1,x,,x n )C A test is a rule which leads to a decision to fail to reject or reject H 0 on the basis of the sample information.

19 Test Statistic: The sample statistic on which we base our decision to reject or not reject the null hypothesis. Rejection Region: Range of values such that, if the test statistic falls in that range, we will decide to reject the null hypothesis, otherwise, we will not reject the null hypothesis. If the hypothesis completely specify the distribution, then it is called a simple hypothesis. Otherwise, it is composite hypothesis.=( 1, ) H 0 : 1 =3f(x;3, ) H 1 : 1 =5f(x;5, ) If is known, simple hypothesis. Composite Hypothesis

20 Reject H 0 H 0 is True Type I error P(Type I error) = H 0 is False Correct Decision Do not reject H 0 Correct Decision 1- Type II error 1- P(Type II error) = Tests are based on the following principle: Fix, minimize. ()=Power function of the test for all. = P(Reject H 0 )=P((x 1,x,,x n )C)

21 Reject H H is true 0 I error P Type P 0 0 Type I error=rejecting H 0 when H 0 is true max 0 max. prob. of Type I error PReject H H is true 1 1 P 0 1 Not Reject H H is true 1 max 1 0 max. prob.of Type II error 1

22 PROCEDURE OF STATISTICAL TEST 1. Determining H 0 and H A.. Choosing the best test statistic. 3. Deciding the rejection region (Decision Rule). 4. Conclusion.

23 HYPOTHESIS TEST FOR POPULATION MEAN, KNOWN AND X~N(, ) OR LARGE SAMPLE CASE: Two-sided Test Test Statistic Rejecting Area H 0 : = 0 H A : 0 / 1- / Reject H 0 if z < z / or z > z /. -z / z / Reject H 0 Reject H 0 Do not reject H 0

24 HYPOTHESIS TEST FOR POPULATION MEAN, One-sided Tests Test Statistic Rejecting Area 1. H 0 : = 0 H A : > 0 1- z Reject H 0 if z > z.. H 0 : = 0 H A : < 0 Reject H 0 if z < z. Do not reject H 0 Reject H z Reject H Do not reject H 0 0

25 POWER OF THE TEST AND P-VALUE 1- = Power of the test = P(Reject H 0 H 0 is not true) p-value = Observed significance level = Probability of obtaining a test statistics at least as extreme as the one that you observed by chance, OR, the smallest level of significance at which the null hypothesis can be rejected OR the maximum value of that you are willing to tolerate.

26 CALCULATION OF P-VALUE Determine the value of the test statistics, For One-Tailed Test: p-value= P(z > z 0 ) if H A : > 0 z 0 x 0 / n p-value p-value= P(z < z 0 ) if H A : < 0 p-value z 0 For Two-Tailed Test p=p-value =.P(z>z 0 ) p=p-value =.P(z<-z 0 ) p/ z 0 p/ -z 0 z 0

27 DECISION RULE BY USING P-VALUES REJECT H 0 IF p-value < p-value DO NOT REJECT H 0 IF p-value

28 Example Do the contents of bottles of catsup have a net weight below an advertised threshold of 16 ounces? To test this 5 bottles of catsup were selected. They gave a net sample mean weight of. It is known that the standard deviation is. We want to test this at significance levels 1% and 5% Z The p-value is the probability of getting a score worse than this (relative to the alternative hypothesis) i.e., P(Z 1.5).1056 Compare the p-value to the significance level. Since it is bigger than both 1% and 5%, we do not reject the null hypothesis.

29 The p-value for this test is Thus, do not reject H 0 at 1% and 5% significance level. We do not have enough evidence to say that the contents of bottles of catsup have a net weight of less than 16 ounces.

30 TEST OF HYPOTHESIS FOR THE POPULATION MEAN ( UNKNOWN) For samples of size n drawn from a Normal Population, the test statistic: Has a Student t-distribution with n1 degrees of freedom

31 Example 5 measurements of the tar content of a certain kind of cigarette yielded 14.5, 14., 14.4, 14.3 and 14.6 mg per cigarette. Show the difference between the mean of this sample x 14.4 and the average tar content claimed by the manufacturer, =14.0, is significant at =0.05.

32 H 0 : = 14.0 H A : 14.0 x t 5.66 s / n / 5 t t.766 /,n1 0.05,4 Decision Rule: Reject H o if t<-t / or t> t /. Reject H 0 at = Difference is significant Reject H 0 p-value =.P(t > 5.66) = (0.004)= Since p-value = < = 0.05, reject H 0. Reject H 0

33 Example At a certain production facility that assembles computer keyboards, the assembly time is known (from experience) to follow a normal distribution with mean (m) of 130 seconds and standard deviation (s) of 15 seconds. The production supervisor suspects that the average time to assemble the keyboards does not quite follow the specified value. To examine this problem, he measures the times for 100 assemblies and found that the sample mean assembly time ( ) is 16.8 seconds. Can the supervisor conclude at the 5% level of significance that the mean assembly time of 130 seconds is incorrect?

34 We want to prove that the time required to do the assembly is different from what experience dictates: H : 130 The sample mean is A X 16.8 The standard deviation is The standardized test statistic : Rejection Region Z z z. Z. Since.13<-1.96, we reject the null hypothesis that the time required to do the assembly is 130 seconds.

35 DECISION RULE Reject H o if z < or z > In terms of X, reject H 0 if 15 X = or X = p value.p(z.13) (0.0166) So, since < 0.05, we reject the null.

36 CALCULATING THE PROBABILITY OF TYPE II ERROR H o : = 130 H A : 130 Suppose we would like to compute the probability of not rejecting H 0 given that the null hypothesis is false (for instance =135 instead of 130), i.e. =P(not rejecting H o H o is false). Assuming =135 this statement becomes:

37 TESTING HYPOTHESIS ABOUT POPULATION PROPORTION, p ASSUMPTIONS: 1. The experiment is binomial.. The sample size is large enough. x: The number of success The sample proportion is approximately for large n (np 5 and nq 5 ).

38 Two-sided Test Test Statistic Rejecting Area H 0 : p = p 0 H A : p p 0 / 1- / - z / z / Reject H o if z < -z / or z > z /. Reject H 0 Do not reject H 0 Reject H 0

39 One-sided Tests Test Statistic Rejecting Area 1. H 0 : p= p 0 H A : p > p 0 1- Reject H o if z > z. z Do not reject H 0 Reject H 0. H 0 : p = p 0 H A : p < p 0 1- Reject H o if z < - z. -z Reject H 0 Do not reject H 0

40 Example Mom s Home Cokin claims that 70% of the customers are able to dine for less than $5. Mom wishes to test this claim at the 9% level of confidence. A random sample of 110 patrons revealed that 66 paid less than $5 for lunch. H o : p = 0.70 H A : p 0.70 x = 66, n = 110 and p = 0.70 x 66 ˆp 0.6 n 110 = 0.08, z / = z 0.04 = 1.75 Test Statistic: z.89 (0.7)(0.3)/110

41 DECISION RULE: Reject H 0 if z < or z > CONCLUSION: Reject H 0 at = Mom s claim is not true. / / p-value =. P(z < -.89) =(0.011) = 0.0 The smallest value of to reject H 0 is 0.0. Since p-value = 0.0 < = 0.08, reject H 0.

42 CONFIDENCE INTERVAL APPROACH Find the 9% CI for p. pˆqˆ pˆ z / n (0.6)(0.4) 110 9% CI for p: 0.5 p 0.68 p 0.7 Since is not in the above interval, reject H 0. Mom has overestimated the percentage of customers that pay less than 5$ for a meal.

43 INFERENCE ABOUT THE POPULATION Test statistic VARIANCE (s ) Sampling distribution of S which is chi-squared distributed with n - 1 degrees of freedom when the population random variable is normally distributed with variance. Confidence interval estimator: LCL = (n - 1) s / UCL = (n - 1) s 1 - /

44 CHI-SQUARE DISTRIBUTION f( ) A A A A

45 Example Proctor and Gamble told its customers that the variance in the weights of its bottles of Pepto-Bismol is less than 1. ounces squared. As a marketing representative for P&G, you select 5 bottles and find a variance of 1.7. At the 10% level of significance, is P&G maintaining its pledge of product consistency? H 0 : = 1. H A : < 1.

46 n=5, s =1.7, =0.10, Test Statistics: 0.90, Decision Rule: Reject H 0 if Conclusion: Because =34 > , do not reject H 0. We don t have enough evidence that suggests the variability in product weights less than 1. ounces squared.

47 Example A random sample of observations from a normal population possessed a variance equal to Find 90% CI for. 90% CI for : 0.05,1 0.95,1 (n 1)s (n 1)s (1)37.3 (1)

48 INFERENCE ABOUT THE DIFFERENCE BETWEEN TWO SAMPLES INDEPENDENT SAMPLES POPULATION 1 POPULATION PARAMETERS: 1, 1 PARAMETERS:, Sample size: n 1 Sample size: n Statistics: 1 1 Statistics: x, s x, s

49 SAMPLING DISTRIBUTION OF Consider random samples of n 1 and n from two normal populations. Then, For non-normal distributions, we can use Central Limit Theorem for n 1 30 and n 30.

50 CONFIDENCE INTERVAL FOR 1-1 and are known for normal distribution or large sample 100(1- C.I. for is given by: If and are unknown and unequal, we can replace them with s 1 and s. We are still using Z table because this is large sample or normal distribution situation.

51 Example Is there any significant difference between mean family incomes of two groups? n 00, x 15530,s 5160 n 50, x 16910,s 5840 Set up a 95% CI for % CI for - 1 : z / z x x s1 s x x x x n1 n s s x x 1.96(s ) xx1 With 95% confidence, mean family income in the second group may exceed that in the first group by between $363 and $397.

52 Test statistic: TEST STATISTIC FOR 1 - WHEN 1 AND ARE KNOWN If and are unknown and unequal, we can replace them with s 1 and s.

53 Example Two different procedures are used to produce battery packs for laptop computers. A major electronics firm tested the packs produced by each method to determine the number of hours they would last before final failure. n 150, x 81hrs,s n 00, x 789hrs,s 7440 The electronics firm wants to know if there is a difference in the mean time before failure of the two battery packs.=0.10

54 H 0 : 1 = H 0 : 1 - = 0 H A : 1 H A : 1-0 Test statistic: Decision Rule = Reject H 0 if z<-z / = or z>z / = Not reject H 0. There is not sufficient evidence to conclude that there is a difference in the mean life of the types of battery packs.

55 1 AND ARE UNKNOWN IF 1 = A 100(1- C.I. for is given by: where

56 Test Statistic: TEST STATISTIC FOR 1 - WHEN 1 = AND UNKNOWN where

57 Example The statistics obtained from random sampling are given as n 8, x 93,s It is thought that 1 <. Test the appropriate hypothesis assuming normality with = n 1 < 30 and n < 30 t-test n 9, x 19,s 4 Because s 1 and s are not much different from each other, use equal-variance t-test (More formally, we can test Ho:σ² 1 =σ² ). H 0 : 1 = H A : 1 < ( 1 - <0)

58 Decision Rule: Reject H 0 if t < -t 0.01,8+9- =-.60 Conclusion: Since t = < -t 0.01,8+9- =-.60, reject H 0 at = 0.01.

59 TEST STATISTIC FOR 1 - WHEN 1 AND UNKNOWN Test Statistic: with the degree of freedom (s / n s / n ) s / n s / n n11 n 1

60 Example Does consuming high fiber cereals entail weight loss? 30 people were randomly selected and asked what they eat for breakfast and lunch. They were divided into those consuming and those not consuming high fiber cereals. The statistics are obtained as n1=10; n=0 x 595.8; x s ; s Because s 1 and s are too different from each other and the population variances are not assumed equal, we can use a t statistic with degrees of freedom 35.7 / / 0 df 35.7 / /

61 H 0 : 1 - = 0 H A : 1 - < 0 Reject H 0 if t < -t,df = -t 0.05, 5 = Since t =-.31< -t 0.05, 5 =-1.708, reject H 0 at = 0.05.

62 INFERENCE ABOUT THE DIFFERENCE OF TWO MEANS: MATCHED PAIRS EXPERIMENT Data are generated from matched pairs; not independent samples. Let X i and Y i denote the measurements for the i-th subject. Thus, (X i, Y i ) is a matched pair observations. Denote D i = Y i -X i or X i -Y i. If there are n subjects studied, we have D 1, D,, D n. Then, n n D i Di nd i1 i1 sd D D D and s s n n 1 n

63 CONFIDENCE INTERVAL FOR D = 1 - A 100(1- C.I. for D = is given by: For n 30, we can use z instead of t.

64 HYPOTHESIS TESTS FOR D = 1 - The test statistic for testing hypothesis about D is given by with degree of freedom n-1.

65 Example Sample data on attitudes before and after viewing an informational film. Subject Before After Difference i X i Y i D i =Y i -X i

66 x 7.64,s 1,57 90% CI for D = 1 - : D D xd t/,n D s 1.57 n 10 D 1 t 0.05, With 90% confidence, the mean attitude measurement after viewing the film exceeds the mean attitude measurement before viewing by between 0.36 and 14.9 units.

67 Example How can we design an experiment to show which of two types of tires is better? Install one type of tire on one wheel and the other on the other (front) wheels. The average tire (lifetime) distance (in 1000 s of miles) is: X D 4.55 with a sample difference s.d. of s 7. There are a total of n=0 observations D

68 H 0 : D =0 Test Statistics: H A : D >0 xd D t =.8 s / n 7./ 0 D Reject H 0 if t>t.05,19 =1.79, Conclusion: Reject H 0 at =0.05

69 INFERENCE ABOUT THE DIFFERENCE OF TWO POPULATION PROPORTIONS SAMPLING DISTRIBUTION OF A point estimator of p 1 -p is x1 x pˆ 1 pˆ n n 1 The sampling distribution of is if n i p i 5 and n i q i 5, i=1,.

70 Two-tailed test H o :p 1 =p H A :p 1 p STATISTICAL TESTS Reject H 0 if z < -z / or z > z /. One-tailed tests H o :p 1 =p H A :p 1 > p Reject H 0 if z > z (pˆ p ˆ ) 0 x x z where p= ˆ 1 1 n n pq ˆˆ n 1 n H o :p 1 =p H A :p 1 < p Reject H 0 if z < -z

71 Example A manufacturer claims that compared with his closest competitor, fewer of his employees are union members. Of 318 of his employees, 117 are unionists. From a sample of 55 of the competitor s labor force, 109 are union members. Perform a test at = H 0 : p 1 = p H A : p 1 < p ˆp 1 x1 117 n 318 proportion is 1 and ˆp x 109 n 55 x x , so pooled sample 1 ˆp 0.39 n n

72 Test Statistic: (117 / / 55) 0 z (0.39)(1 0.39) Reject H 0 if z < -z 0.05 = Because z = > -z 0.05 =-1.96, not reject H 0 at =0.05. Manufacturer is wrong. There is no significant difference between the proportions of union members in these two companies.

73 Example In a study, doctors discovered that aspirin seems to help prevent heart attacks. Half of,000 male participants took aspirin and the other half took a placebo. After 3 years, 104 of the aspirin and 189 of the placebo group had heart attacks. Test whether this result is significant. p 1 : proportion of all men who regularly take aspirin and suffer from heart attack. p : proportion of all men who do not take aspirin and suffer from heart attack 104 pˆ pˆ ; Pooled sample proportion: p= ˆ

74 Test Statistic: H 0 : p 1 -p = 0 H A : p 1 -p < 0 Conclusion: Reject H 0 since p-value=p(z<-5.0) 0

75 CONFIDENCE INTERVAL FOR p 1 -p A 100(1- C.I. for p p is given by:

76 INFERENCE ABOUT COMPARING TWO POPULATION VARIANCES Sampling distribution of For independent r.s. from normal populations (1-α)100% CI for s s 1 1 / s1 / 1 ~ F(n1 1, n 1) s / 1 [ F(1 /, n 1 1, n 1 / ] 1 1) s s 1 1 [ F( /, n 1, n 1 ] 1)

77 STATISTICAL TESTS Two-tailed test H o : (or ) H A : 1 F s s 1 Reject H 0 if F < F /,n1-1,n-1 or F > F 1- /,n1-1,n-1. One-tailed tests H o : 1 H o : 1 H A : H A : 1 1 Reject H 0 if F > F 1-, n1-1,n-1 Reject H 0 if F < F,n1-1,n-1

78 Example A scientist would like to know whether group discussion tends to affect the closeness of judgments by property appraisers. Out of 16 appraisers, 6 randomly selected appraisers made decisions after a group discussion, and rest of them made their decisions without a group discussion. As a measure of closeness of judgments, scientist used variance. Hypothesis: Groups discussion will reduce the variability of appraisals. Appraisal values (in thousand $) Statistics With discussion 97, 101,10,95,98,103 n1=6 s1²=9.867 Without discussion 118, 109, 84, 85, 100, 11, 115, 93, 91, 11 n=10 s²=194.18

79 Ho: 1 1 H1: 1 1 F s F(0.05,5,9) s Reject Ho. Group discussion reduces the variability in appraisals.