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Chapter 6: Introduction to Inference 1.

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1 Chapter 6: Introduction to Inference 1

2 Statistical Inference 2 Sampling

3 Sampling Variability What would happen if we took many samples? 3 Population Sample ? ?

4 Conditions for Inference (Chapter 6) 1.The variable we measure has a Normal distribution with mean  and standard deviation σ. 2.We don’t know , but we do know σ. 3. We have an SRS from the population of interest. 4

5 6.1: Estimating with Confidence - Goals Describe a level C confidence interval (CI) for a parameter in terms of an estimate and its margin of error. Be able to construct a level C CI for  for a sample size of n with known σ. Explain how the margin of error changes with confidence level, sample size and sample average. Determine the sample size required to obtain a specified margin of error and confidence level C. Determine when it is proper to use the CI. 5

6 Confidence Interval: Definition 6

7 Example: Confidence Interval 1 Suppose we obtain a SRS of 100 plots of corn which have a mean yield (in bushels) of x̅ = and a standard deviation of σ = What are the plausible values for the (population) mean yield of this variety of corn with a 95% confidence level? 7

8 Confidence Interval: Definition 8

9 Confidence Interval 9

10 Interpretation of CI A level C confidence interval for a parameter is an interval computed from sample data by a method that has probability C of producing an interval containing the true value of the parameter. To say that we are 95% confident is shorthand for “95% of all possible samples of a given size from this population will yield intervals that capture the unknown parameter.” 10

11 Interpretation of CI 11

12 CI conclusion We are 95% (C%) confident that the population (true) mean of […] falls in the interval (a,b) [or is between a and b]. We are 95% confident that the population (true) mean yield of this type of corn falls in the interval (121.4, 126.2) [or is between and bushels]. 12

13 t-Table (Table D) 13

14 Example: Confidence Interval 2 An experimenter is measuring the lifetime of a battery. The distribution of the lifetimes is positively skewed similar to an exponential distribution. A sample of size 196 produces x̅ = The standard deviation is known to be for this population. a) Find and interpret the 95% Confidence Interval. b) Find and interpret the 90% Confidence Interval. c) Find and interpret the 99% Confidence Interval. 14

15 Example: Confidence Interval 2 (cont) We are 95% confident that the population mean lifetime of this battery falls in the interval (1.997, 2.539). We are 90% confident that the population mean lifetime of this battery falls in the interval (2.041, 2.495). We are 99% confident that the population mean lifetime of this battery falls in the interval (1.912, 2.624). 15

16 How Confidence Intervals Behave 16 Cz*CI (2.041, 2.495) (1.997, 2.539) (

17 Example: Confidence Level & Precision The following are two CI’s having a confidence level of 90% and the other has a level of 95% level: (-0.30, 6.30) and (-0.82,6.82). Which one has a confidence level of 95%? 17

18 Impact of Sample Size 18 Sample size n Standard error  ⁄ √n

19 Example: Confidence Interval 2 (cont.) An experimenter is measuring the lifetime of a battery. The distribution of the lifetimes is positively skewed similar to an exponential distribution. A sample of size 196 produces x̅ = and s = a) Find the Confidence Interval for a 95% confidence level. b) Find the Confidence Interval for the 90% confidence level. c) Find the Confidence Interval for the 99% confidence level. d) What sample size would be necessary to obtain a margin of error of 0.2 at a 99% confidence level? 19

20 Practical Procedure 1.Plan your experiment to obtain the lowest  possible. 2.Determine the confidence level that you want. 3.Determine the largest possible width that is acceptable. 4.Calculate what n is required. 20

21 Confidence Bound 21 Cz*

22 Example: Confidence Bound The following is summary data on shear strength (kip) for a sample of 3/8-in. anchor bolds: n = 78, x̅ = 4.25,  = Calculate a lower confidence bound using a confidence level of 90% for the true average shear strength. We are 90% confident that the true average shear strength is greater than …. 22

23 Summary: CI Confidence Interval Upper Confidence Bound Lower Confidence Bound Confidence Level90%95%99% Two-sided z* values One-sided z* values

24 Cautions 1.The data must be an SRS from the population. 2.Be careful about outliers. 3.You need to know the sample size. 4.You are assuming that you know σ. 5.The margin of error covers only random sampling errors! 24

25 Conceptual Question One month the actual unemployment rate in the US was 8.7%. If during that month you took an SRS of 250 people and constructed a 95% CI to estimate the unemployment rate, which of the following would be true: 1) The center of the interval would be ) A 95% confidence interval estimate contains ) If you took 100 SRS of 250 people each, 95% of the intervals would contain

26 Tests of Significance 26

27 6.2: Tests of Significance - Goals State the methodology to perform a test of significance. Be able to state the null and alternative hypothesis. Be able to state the test statistic. Be able to define and calculate the P value. Determine the conclusion of the significance test from the P value and state it in English. Describe the relationships between confidence intervals and hypothesis tests. 27

28 Significance Test A test of significance is a formal procedure for comparing observed data with a claim (also called a hypothesis) whose truth we want to assess. The claim is a statement about a parameter, like the population proportion p or the population mean µ. We express the results of a significance test in terms of a probability, called the P-value, that measures how well the data and the claim agree. 28

29 Example: Significance Test You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. a)Is the somewhat smaller weight simply due to chance variation? b)Is there evidence that the calibrating machine that sorts cherry tomatoes into packs needs revision? 29

30 General Procedure for Hypothesis Tests 1.State what you want to test. 2.Use the sample data to perform the test. 3.Make a decision using the results from the test. 30

31 Statistical Hypotheses hypothesis: an assumption or a theory about one or more parameters in one or more populations. Null Hypothesis: H 0 : – Initially assumed to be true. Alternative Hypothesis: H a – Contradictory to H 0 Decision: – Reject H 0 – Fail to reject H 0 31

32 Example: Significance Test You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. What are some examples of hypotheses? 32

33 Example: Hypothesis Translate each of the following research questions into appropriate hypothesis. 1. The census bureau data show that the mean household income in the area served by a shopping mall is $62,500 per year. A market research firm questions shoppers at the mall to find out whether the mean household income of mall shoppers is higher than that of the general population. 2. Last year, your company’s service technicians took an average of 2.6 hours to respond to trouble calls from business customers who had purchased service contracts. Do this year’s data show a different average response time? 33

34 Example: Hypothesis (cont) Translate each of the following research questions into appropriate hypothesis. 3. The drying time of paint under a specified test conditions is known to be normally distributed with mean value 75 min and standard deviation 9 min. Chemists have proposed a new additive designed to decrease average drying time. It is believed that the new drying time will still be normally distributed with the same σ = 9 min. Should the company change to the new additive? 34

35 Test Statistic 35

36 Example: Significance Test (con) You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. The packaging process has a known standard deviation of 5 g. c) What is the test statistic? d) What is the probability that 222 is consistent with the null hypothesis? 36

37 Error Probabilities If we reject H 0 when H 0 is true, we have committed a Type I error. If we fail to reject H 0 when H 0 is false, we have committed a Type II error. 37 Truth about the population H 0 trueH 0 false (H a true) Conclusion based on sample Reject H 0 Fail to Reject H 0

38 Tests of Significance 38

39 Type I and Type II errors 39

40 Type I vs. Type II errors (1) 40

41 Type I vs. Type II errors (2) 41

42 Type I vs. Type II errors (3) 42

43 Type I vs. Type II errors (4) 43

44 Type I vs. Type II errors (5) 44

45 Errors  measures the strength of the sample evidence against H 0 The power measures the sensitivity (true negative) of the test 45

46 Example: Significance Test (con) You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. The packaging process has a known standard deviation of 5 g. c) What is the test statistic? d) What is the probability that 222 is consistent with the null hypothesis? 46

47 P-values for t tests 47

48 P-value The probability, computed assuming H 0 is true, that the statistic would take a value as or more extreme than the one actually observed is called the P-value of the test. The smaller the P-value, the stronger the evidence against H 0. Small P-values are evidence against H 0 because they say that the observed result is unlikely to occur when H 0 is true. Large P-values fail to give convincing evidence against H 0 because they say that the observed result is likely to occur by chance when H 0 is true. 48

49 P-values for t tests 49

50 Decision Reject H 0 or Fail to Reject H 0 Note: A fail-to-reject H 0 decision in a significance test does not mean that H 0 is true. For that reason, you should never “accept H 0 ” or use language implying that you believe H 0 is true. In a nutshell, our conclusion in a significance test comes down to: – P-value small --> reject H 0 --> conclude H a (in context) – P-value large --> fail to reject H 0 --> cannot conclude H a (in context) 50

51 Statistically Significant  measures the strength of the sample evidence against H 0 If the P-value is smaller than , we say that the data are statistically significant at level . The quantity  is called the significance level or the level of significance. When we use a fixed level of significance to draw a conclusion in a significance test, – P-value ≤  --> reject H 0 --> conclude H a (in context) – P-value >  --> fail to reject H 0 --> cannot conclude H a (in context) 51

52 Statistically Significant - Comments Significance is a technical term Determine what significance level (  ) you want BEFORE the data is analyzed. Conclusion – P-value ≤  --> reject H 0 – P-value >  --> fail to reject H 0 52

53 Rejection Regions: 53

54 P-value interpretation The probability, computed assuming H 0 is true, that the statistic would take a value as or more extreme than the one actually observed is called the P-value of the test. The P-value (or observed significance level) is the smallest level of significance at which H 0 would be rejected when a specified test procedure is used on a given data set. The P-value is NOT the probability that H 0 is true. 54

55 P-Value Interpretation 55

56 Procedure for Hypothesis Testing 0. Identify the parameter(s) of interest and describe it (them) in the context of the problem. 1. State the Hypotheses. 2. Calculate the appropriate test statistic. 3. Find the P-value. 4. Make the decision and state the conclusion in the problem context. Reject H 0 or fail to reject H 0 and why. The data does [not] give strong support (P- value = [x]) to the claim that the [statement of H a in words]. 56

57 Example: Significance Test (cont) You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. The packaging process has a known standard deviation of 5 g. d) Perform the appropriate significance test at a 0.05 significance level to determine if the calibrating machine that sorts cherry tomatoes needs to be recalibrated. 57

58 Single mean test: Summary Null hypothesis: H 0 : μ = μ 0 Test statistic: Alternative Hypothesis P-Value One-sided: upper-tailedH a : μ > μ 0 P(Z ≥ z) One-sided: lower-tailedH a : μ < μ 0 P(Z ≤ z) two-sidedH a : μ ≠ μ 0 2P(Z ≥ |z|) 58

59 CI and HT 59

60 Example: HT vs. CI You are in charge of quality control in your food company. You sample randomly four packs of cherry tomatoes, each labeled 1/2 lb. (227 g). The average weight from your four boxes is 222 g. The packaging process has a known standard deviation of 5 g. e) Determine the 95% CI. f) How do the results of part d) and e) compare? 60

61 Example: HT vs. CI (2) Suppose we are interested in how many credit cards that people own. Let’s obtain a SRS of 100 people who own credit cards. In this sample, the sample mean is 4 and the sample standard deviation is 2. If someone claims that he thinks that μ > 2, is that person correct? a) Construct a 99% lower bound for μ. b) Perform an appropriate hypothesis test with significance level of c) How would the conclusion have changed if H a : µ < 2? 61

62 Example: HT vs. CI (2) b) The data does give strong support (P = 0) to the claim that the population average number of credit cards is greater than 2. 62

63 P-values for t tests 63

64 Example: HT vs. CI (2) c) The data does not give strong support (P > 0.5) to the claim that the population average number of credit cards is less than 2. 64

65 Example 1: Extra Practice A group of 15 male executives in the age group 35 – 44 have a mean systolic blood pressure of and population standard deviation of 15. a)Is this career group’s mean pressure different from that of the general population of males in this age group which have a mean systolic blood pressure of 128 at a significance level of 0.01? b)Calculate and interpret the appropriate confidence interval. c)Are the answers to part a) and b) the same or different? Explain your answer. 65

66 Example 2: Extra Practice A new billing system will be cost effective only if the mean monthly account is more than $170. Accounts have a population standard deviation of $65. A survey of 41 monthly accounts gave a mean of $187. a)Will the new system be cost effective at a significance of 0.05? b)Calculate the appropriate confidence bound. c)Are the answers to part a) and b) the same or different? Explain your answer. d)What would the conclusion in part a) be if the monthly accounts gave a mean of $160? Please perform the hypothesis tests. 66

67 Confidence interval vs. Significance Test Confidence IntervalSignificance Test Range of values at one confidence level. Yes or no with a measure of how close you are to the cutoff. 67

68 More on P-value When you report a significance test, always report the P-value. The P-value is the smallest level of  at which the data is significant. P-value is calculated from the data,  is chosen by each individual. 68

69 6.3: Use and Abuse of Tests - Goals Be able to describe the factors involved in determining an appropriate significance level. Be able to differentiate between practical (or scientific) significance and statistical significance. Be able to determine when statistical inference can be used. State the problems involved with searching for statistical significance. 69

70 How small a P is convincing? (factors involved in choosing  ) How plausible is H 0 ? What are the consequences of your conclusion? Are you conducting a preliminary study? 70

71 Notes for choosing the significance level Use the cut-off that is standard in your field There is no sharp border between “significant” and “not significant” It is the order of magnitude of the P-value that matters. Do not use  = 0.05 as the default value! (Sir R.A. Fisher said, “A scientific fact should be regarded as experimentally established only if a properly designed experiment rarely fails to give this level of significance.” 71

72 Statistical vs. Practical Significance Statistical significance: the effect observed is not likely to be due to chance alone. Practical significance: the effect has some practical consequence. 72

73 Statistical vs. Practical Significance An Illustration of the Effect of Sample Size on P- values 73

74 Lack of Evidence Consider this provocative title from the British Medical Journal: “Absence of evidence is not evidence of absence.” 74

75 Beware of Searching for Significance Decide the experiment BEFORE you look at the data. All of our tests involve errors. The previous two points do not imply that exploratory data analysis is a bad thing. Exploratory analysis often leads to interesting discoveries. However, if the data at hand suggest an interesting theory, then test that theory on a new set of data! 75

76 6.4: Power and Inference as a Decision- Goals Describe the two types of possible errors and the relationship between them. Define the power of a test. Be able to calculate the power of a test given the sample size, significance level and true value of the mean. 76

77 Types of Errors If we reject H 0 when H 0 is true, we have committed a Type I error. If we fail to reject H 0 when H 0 is false, we have committed a Type II error. 77 Truth about the population H 0 true H 0 false (H a true) Conclusion based on sample Reject H 0 Type I error Correct conclusion Fail to reject H 0 Correct conclusion Type II error

78 Type I vs. Type II errors (5) 78

79 Type I vs. Type II errors (4) 79

80 Increase the power   ’  n 80

81 Effect Size % of Control = Average Exp P(guess which group) P(exp > control) 050% % % % % % % % X


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