ME3620. Theory of Engineering Experimentation. Spring Chapter IV. Decision Making for a Single Sample. Chapter IV
|
|
- Constance McGee
- 5 years ago
- Views:
Transcription
1 Theory of Engineering Experimentation Chapter IV. Decision Making for a Single Sample Chapter IV 1
2 4 1 Statistical Inference The field of statistical inference consists of those methods used to make decisions or draw conclusions about a population. These methods utilize the information contained in a random sample from the population in drawing conclusions. Chapter IV 2
3 4 1 Statistical Inference Statistical Inference is divided into two areas: a) Parameter Estimation and b) Hypothesis Testing Parameter Estimation - Parameters are descriptive measures of an entire population. - Their values are usually unknown because it is unfeasible to measure an entire population. - Instead, a random sample from the population is taken in order to obtain parameter estimates. - Statistical analysis deals with finding estimates of the population parameters along with the amount of error associated with these estimates. These estimates are also known as sample statistics. Chapter IV 3
4 4 1 Statistical Inference Parameter Estimation - There are several types of parameter estimates: Point estimates are the single, most likely value of a parameter. For example, the point estimate of population mean (the parameter) is the sample mean (the parameter estimate). - Confidence intervals are a range of values likely to contain the population parameter. As an example of parameter estimates, consider that a spark plug manufacturer is studying a problem in their spark plug gap. It would be too costly to measure every single spark plug that is made. Instead, a random sample of 100 spark plugs is collected and the gap is measured to be 9.2 mm [this is the point estimate for the population mean (μ)]. Additionally, a 95% confidence interval for μ which is (8.8, 9.6) is determined. This means that with a 95% confidence the true value of the average gap for all the spark plugs is between 8.8 and 9.6. Chapter IV 4
5 4 3 Hypothesis Testing Statistical Hypothesis Many of the problems in engineering require to determine whether to accept or reject a statement about a given parameter. The statement is called hypothesis and the decision-making procedure about the hypothesis is called hypothesis testing. Statistical hypothesis testing is the data analysis stage of a comparative experiment, in which we might be interested in, for example, comparing the mean of a population to a specified value. In this chapter, we will consider comparative experiments involving one population, and with focus on is testing hypothesis concerning the parameters of the population (mean, standard deviation). Chapter IV 5
6 4 3.1 Statistical Hypothesis A statistical hypothesis can arise from physical laws, theoretical knowledge, past experience, or external considerations, such as engineering requirements. Since probability distributions are used to represent populations, a statistical hypothesis can be stated in terms of the probability distribution of a random variable. The hypothesis usually involves one or more parameters of this distribution. Chapter IV 6
7 4 3.1 Statistical Hypothesis For example, suppose that we are interested in the burning rate of a solid propellant used to power aircrew escape systems. The burning rate is a random variable that can be described by a probability distribution. We are interested on the mean burning rate (a parameter of this distribution). Specifically, we want to determine whether or not the mean burning rate is 50 cm 3 /s. This can be expressed as 3 3 The statements H 0 and H 1 are called null hypothesis and alternative hypothesis, respectively. Chapter IV 7
8 4 3.1 Statistical Hypothesis In this particular case, since the alternative hypothesis states H 1 : μ 50 cm 3 /s, H 1 is called a two sided alternative hypothesis. A one sided alternative hypothesis would be stated as, for example in this case: H 0 : μ = 50 cm 3 /s H 1 : μ < 50 cm 3 /s or H 0 : μ = 50 cm 3 /s H 1 : μ > 50 cm 3 /s Hypothesis are always statements about the population or distribution under study, not statements about the sample. Chapter IV 8
9 4 3.1 Statistical Hypothesis The value of the population parameter in the null hypothesis is usually determined as: a) The result of past experience or knowledge of the process, in this case the hypothesis statement will be about whether the parameter has changed. b) Being drawn from some theory or model regarding the process under study, the hypothesis will be about verifying the theory or model. c) The result of external considerations, such as design or engineering considerations. A procedure leading to a decision about a particular hypothesis is called Test of a Hypothesis. Hypothesis-testing procedures rely on using the information in a random sample from the population of interest. Chapter IV 9
10 4 3.1 Statistical Hypothesis A procedure leading to a decision about a particular hypothesis is called Test of a Hypothesis. Hypothesis-testing procedures rely on using the information in a random sample from the population of interest. If this information is consistent with the hypothesis, then the hypothesis is true. If this information is inconsistent with the hypothesis, then the hypothesis is false. In general, what it is tested is the null hypothesis, where, the rejection of the null hypothesis leads to accepting the alternative hypothesis. Null hypothesis are always stated such that an exact value of the parameter is expressed, for example H 0 : μ = 50 cm 3 /s Chapter IV 10
11 4 3.1 Statistical Hypothesis In general, what it is tested is the null hypothesis, where, the rejection of the null hypothesis leads to accepting the alternative hypothesis. Null hypothesis are always stated such that an exact value of the parameter is expressed, for example H 0 : μ = 50 cm 3 /s. Alternative hypothesis allows the parameter to take several values, for example H 0 : μ < 50 cm 3 /s. Testing of a hypothesis requires taking a random sample, computing a test statistic from the sample data, and then using the test statistic to make a decision about the null hypothesis. Chapter IV 11
12 4 3.2 Testing Statistical Hypothesis Consider the burning rate problem, used previously: 3 A sample of 10 specimens is taken and the mean of the sample, x, is determined (x is an estimation of μ). If x is close to μ, then there is evidence to support H 0. If x is considerably different to μ, then there is evidence to support H 1, instead. Since the sample mean, x, can take many values, a range around μ is determined, such that if x falls within this range then H 0 is not rejected (that is H 0 is accepted). Otherwise if x falls outside this pre-established range then H 0 rejected (that is H 1 is accepted). Chapter IV 12 3
13 4 3.2 Testing Statistical Hypothesis Consider the case discussed where 3 3 Assume that the decision rule is: 48.5 < x < Thus values of x less than 48.5 or larger that 51.5 constitute the critical region for the test. The values that define the critical region are called critical values. Chapter IV 13
14 4 3.2 Testing Statistical Hypothesis This decision process can result in two erroneous conclusions: a) The true mean burning rate might be 50 cm 3 /s but the randomly selected testing specimens resulted in a x within the critical region, resulting in the rejection of H 0 in favor of H 1. This type of erroneous conclusion is called Type I Error. b) Assume that μ 50 cm 3 /s but x falls outside the critical region, in this case we would fail to reject H 0 (that is H 0 is accepted), when H 0 is false. This type of erroneous conclusion is called Type II Error. Chapter IV 14
15 4 3.2 Testing Statistical Hypothesis These observations are summarized in the following table: (accepted) Now, the probability of making a Type I error is represented by α: α= P(type I error) = P(reject H 0 when H 0 is true) In the propellant burning example, a type I error occurs if μ = 50 but x < 48.5, or x > 51.5 Chapter IV 15
16 4 3.2 Testing Statistical Hypothesis Assume now that σ = 2.5 cm 3 /s, then if H 0 : μ = 50 cm 3 /s is true, the distribution of the sample mean, x, is approximately normal with mean μ = 50 and standard deviation σ/ 10 = 2.5/ 10 = Thus, the probability of making a Type I error is: α= P(X < 48.5 when μ = 50 ) + P (X > 51.5 when μ = 50 ) Then, the z values corresponding to the critical values of 48.5 and 51.5 are z 1.90 z Which results in, α = P(Z < ) + P (Z > 1.90) = = Chapter IV 16
17 4 3.2 Testing Statistical Hypothesis Which results in, α = P(Z < ) + P (Z > 1.90) = = This result implies that 5.74 % of all random samples will lead to rejection of H 0 when the true mean is actually 50. That is, it is expected to make a type I error 5.74 % of the time, provided that the true mean is actually 50. Chapter IV 17
18 4 3.2 Testing Statistical Hypothesis α can be reduced by using critical values that are farther from the mean μ say for example 48 and 52 α P Z P Z α P Z 2.53 P Z 2.53 Chapter IV 18
19 4 3.2 Testing Statistical Hypothesis α can also be reduced by increasing the sample size, say, n = 16 instead 10. Thus, σ/ 16 = 2.5/ 16 = α P Z P Z α P Z 2.4 P Z 2.4 Chapter IV 19
20 4 3.2 Testing Statistical Hypothesis It is also important to study the probability of Type II error, β: That is, H 0 is accepted when H 0 is false. β= P(type II error) = P(fail to reject H 0 when H 0 is false) To find β it is necessary to have a specific alternative hypothesis, that is a particular value for μ. Suppose that it is important to reject H 0 : μ = 50 when the burning rate is greater than 52 or less than 48. We could calculate the probability of a type II error β for the values μ = 48 and μ = 52 and use this result to draw some conclusions about the test procedure. Chapter IV 20
21 4 3.2 Testing Statistical Hypothesis That is, how would the test procedure work if it is needed to detect that is reject H 0 for a mean value of μ = 48 or μ = 52? Because of symmetry, it is only necessary to evaluate one of the two cases say finding the probability of not rejecting H 0 : μ = 50 when the true mean is μ = 52. The normal distribution on the left corresponds to the test statistics X when H 0 : μ = 50 is true. The normal distribution on the right is the distribution of X when H 1 is true and the value of μ = 52. A type II error will occur when the sample mean x falls between 48.5 and 51.5 (the critical region boundaries) when μ = 52. Chapter IV 21
22 4 3.2 Testing Statistical Hypothesis This is the probability that 48.5 X 51.5 when the true mean is μ = 52. Which is the shaded area of the normal distribution on the right. Therefore: β = P(48.5 X 51.5 when μ = 52) Then, the z values corresponding 48.5 and 51.5 when μ = 52 are z 4.43 z Resulting in β P 4.43 Z 0.63 P Z 0.63 P Z 4.43 β Chapter IV 22
23 4 3.2 Testing Statistical Hypothesis β In this case, testing H 0 : μ = 50 against H 1 : μ 50 with n = 10 and critical values of 48.5 and 51.5, and true mean value of 52 will result in a probability of of failing to reject (that is accept) a false H 0. Due to symmetry if the true value of the mean is μ = 48 then β = The probability of making a Type II error increases rapidly as the true value of μ approaches the hypothesized value. For example, if the true value of μ is 50.5 and H 0 : μ = 50, then β= P(48.5 X 51.5 when μ = 50.5) z 2.53 z Chapter IV 23
24 4 3.2 Testing Statistical Hypothesis Which results in β P 2.53 Z 1.27 P Z 1.27 P Z 2.53 β Type II error probability also depends on the size of the sample. Thus, the following conclusions can be obtained regarding type I and II errors: a) The size of the critical region, and thus the probability of a type I error α can be reduced by adjusting the critical values b) Type I and II errors are related. A decrease in the probability of one type of error results in an increase in the probability of the other (if n remains constant) c) Increases in the sample size will reduce α and β if the critical values are constant. d) When H 0 is false β increases as the true value of the parameter approaches the value hypothesized in H 0. Chapter IV 24
25 4 3.2 Testing Statistical Hypothesis Type I error probability α is controlled through the selection of the critical values. In general a value of α = 0.05 is used in most situations unless there is information available indicating that this is an inappropriate value Finally, we define the Power: Thus, the power is defined as 1 β Chapter IV 25
26 4 3.2 Testing Statistical Hypothesis Type I error probability α is controlled through the selection of the critical values. In general a value of α = 0.05 is used in most situations unless there is information available indicating that this is an inappropriate value P Values in Hypothesis Testing The P value is the probability that the sample average, x, will take on a value that is at least as extreme as the observed value when H 0 is true. That is, P value conveys information about the weight of evidence against H 0. The smaller the P value is, the greater the evidence against H 0. If P value is small enough the H 0 is rejected in favor of H 1. P value approach allows a decision maker to draw conclusions at any level of significance that is appropriate. Chapter IV 26
27 4 3.3 P Values in Hypothesis Testing The P value measures the plausibility of H 0. The smaller the P value is, the greater the evidence against H 0. The P value is the probability of obtaining a sample more extreme than the one observed in the data assuming that H 0 is true. Chapter IV 27
28 4 3.3 P Values in Hypothesis Testing Calculation and Interpretation of the P Value Consider the propellant burning rate example, with σ = 2.5 cm 3 /s: 3 3 Suppose that a random sample with n = 10 and x = 51.8 cm 3 /s is collected. For this example x μ z 2.28, thus, the probability of P(z = 2.28) = σ/ n 0.79 It is also necessary to consider z value being negative (z = 2.28), corresponding to Because of symmetry, P(z 2.28) = Therefore, the P value for this hypothesis testing case is P = = Chapter IV 28
29 Calculation and Interpretation of the P Value P value states whether H 0 is true. In this case, the probability of getting a random sample whose mean is at least as far from 50 as 51.8 (or 48.2) is (very small). Then, a random sample with 51.8 as mean is very rare if the actual mean is 50. Using a level of significance of 0.05 (Confidence interval of 5%), in this case H 0 would be rejected. Chapter IV 29
30 Calculation and Interpretation of the P Value In a practical application, once the P value is computed, its value is compared to a predetermined significance of level to make a conclusion regarding H 0. Typically the level of significance used is 0.05 (Confidence interval of 5%). Thus, the P value provides a level of the credibility for H 0 by measuring the weight of evidence against H 0. Example. Problem Set n = 8 Problem c) Find P value if x 9.09v Chapter IV 30
31 4 4 Inference on the Mean of a Population. Variance Known Under the following Assumptions The Quantity Z: has a standard normal distribution Hypothesis Testing on the Population Mean (μ) Consider the case in which: Chapter IV 31
32 4 4.1 Hypothesis Testing on the Population Mean (μ) Consider the case in which: And a random sample X 1, X 2,, X n, of the population is collected. Using the z test, the P value for this sample can be determined provided that the variance (σ 2 ) of the population is known. The test statistic for the z test is defined by: X is the mean of the sample whereas σ/ n is known as the Standard Error of the Mean (S.E.M) The determination of the P value is function of the definition of H 1. Chapter IV 32
33 4 4.1 Hypothesis Testing on the Population Mean (μ) If H 1 is two sided: H 1 μ 0, then P = 2[1 - (z 0 )] If H 1 is upper tailed: H 1 > μ 0, then P = 1 - (z 0 ) If H 1 is lower tailed: H 1 < μ 0, then P = (z 0 ) Chapter IV 33
34 4 4.1 Hypothesis Testing on the Population Mean (μ) To use significance level testing with z test, it is only necessary to determine the critical regions for H 1, whether H 1 is two sided or one sided. If H 0 : μ = μ 0 is true: a) H 1 is two sided: The probability that Z 0 falls between z α/2 and z α/2 is 1 α. Thus, the probability, α, of: Z 0 < z α/2 or Z 0 > z α/2 when H 0 : μ = μ 0 is true is very small then H 0 must be rejected. b) H 1 is one sided: The probability that Z 0 > z α or Z 0 < z α is 1 α. Thus, the probability, α, of: Z 0 < z α or Z 0 > z α when H 0 : μ = μ 0 is true is very small then H 0 must be rejected. Chapter IV 34
35 4 4.1 Hypothesis Testing on the Population Mean (μ) These results are summarized in the following table, and on Table The significance level, α, is going to be considered 0.05, unless otherwise stated. Thus if P value, is less than 0.05, H 0 must be rejected in favor of H 1. In summary, using P value criteria, if: P > 0.05 then accept H 0 P < 0.05 then reject H 0 Chapter IV 35
36 4 4.1 Hypothesis Testing on the Population Mean (μ) In summary, using the significance level testing criteria, if: For H 1 : μ μ 0 reject H 0 if Z 0 < 1.96 or Z 0 > (α/2 = 0.05/2 = 0.025) For H 1 : μ > μ 0 reject H 0 if Z 0 > (α = 0.05) For H 1 : μ < μ 0 reject H 0 if Z 0 < Chapter IV 36
37 4 4.2 Type II Error and Choice of Sample size Consider the case in which the alternative hypothesis is two sided : Where δ = μ true μ hypothesized and z.05/2 = 1.96 Chapter IV 37
38 Consider now, the case in which the H 1 is one sided, upper tail: > In this case: β Φ Z δ n σ Where δ = μ true μ hypothesized and z α=.05 = Finally, if H 1 : μ < μ 0 then, the probability of type II error, β, is: β 1 Φ Z δ n σ Where δ = μ true μ hypothesized and z α=.05 = Chapter IV 38
39 4 4.2 Type II Error and Choice of Sample size Determination of sample size If it is necessary to determine the sample size, n, to reduce the probability of type II error, to a given value β, then, for H 1 : μ μ 0 : Chapter IV 39
40 4 4.2 Type II Error and Choice of Sample size Determination of sample size If it is necessary to determine the sample size, n, to reduce the probability of type II error, to a given value β, when H 1 is one sided: Chapter IV 40
41 4 4.3 Large Sample Test The test procedure developed for the null hypothesis H 0 : μ = μ 0 was under the assumption that the variance of the population, σ 2, is known. In most practical situations this is not the case, that is σ 2 is unknown. However, if the number of samples is n 30, the sample variance s 2 will be close to σ 2 for most samples, and so s can be substituted for σ in the test procedures without any significant effect. The appropriate approach for the analysis of H 0 when σ 2 is unknown and the sample is small will be discussed in section 4 5. Chapter IV 41
42 4 4.5 Confidence Interval on the Mean In many situations, when making a decision about the mean, H 0 : μ = μ 0 it is more practical to have an interval than a point estimate. One way to finding this interval is by determining a Confidence Interval (CI). A confidence interval is defined, for a two sided alternative hypothesis H 1 : μ μ 0, as: Where Resulting in Which can be rearranged as P z / Z z / 1 α Z X μ σ/ n P z / X μ σ/ n z / 1 α z σ P X n μ X z / σ n 1 α Chapter IV 42
43 Chapter IV 43
44 4 4.5 Confidence Interval on the Mean For the case in which the two sided case of H 1 has a CI is 1 α = 95%, z α/2 = 1.96, thus the previous equation becomes X 1.96 σ If the alternative hypothesis is lower tailed, H 1 : μ < μ 0, then the confidence interval for the upper confidence bound is determined as If the alternative hypothesis is upper tailed, H 1 : μ > μ 0, then the confidence interval for the lower confidence bound is determined as For a CI, (1 α), of 95%, z α = n μ X z σ n X z σ n μ μ X 1.96σ n Chapter IV 44
45 4 5 Inference on the Mean of a Population, Variance Unknown Consider a population with normal distribution for which the mean, μ, and the standard deviation, σ, are unknowns. Assume that it is necessary to test the two sided alternative hypothesis: For the cases in which the sample size is large, n 30, the test statistic is very similar to that of the case in which σ is known: Z X μ S/ n In this case S is the standard deviation of the sample. (Eqn. 4 39). That is for samples with large number of elements σ S. Example. Problem 4 29 (a) and (b) Problem 4 32 Problem 4 37 Chapter IV 45
46 4 5 Inference on the Mean of a Population, Variance Unknown When the sample is small (n 30) and σ 2 unknown, testing the hypothesis on the mean, μ, is performed using the T test. t distribution depends on the number of samples and therefore the t table is function the number of items sampled, n. k = = (n 1) Where k (or ) is the degree of freedom Chapter IV 46
47 4 5 Inference on the Mean of a Population, Variance Unknown t distribution is symmetric about zero, and unlike normal distribution, the probability values, α, provided in tables correspond to the right side of the curve. A t distribution table is presented in Table II (Appendix A). t α, k is the value of the random variable T with k degrees of freedom above which we find a probability α. Since t distribution is symmetric t 1 α = t α Example. Problem 4 48 (a), (e); Problem 4 49 (d) Chapter IV 47
48 4 5 Inference on the Mean of a Population, Variance Unknown Finally, a summary of the testing hypotheses on the Mean of a Normal Distribution when the Standard Deviation of the Population is Unknown and the number of samples n 30, is presented next. Chapter IV 48
49 4 5 Inference on the Mean of a Population, Variance Unknown Type Error II and Choice of Sample Size Remember, Type II Error is the probability, β, of Fail to Reject H 0 when H 0 is false. In order to determine β for problems involving the t test a set of charts, called Operating Characteristic (OC) charts have been compiled (Appendix A, charts a, b, c, d). β is function of the type alternative hypothesis (two sided, right or left tail), the level of significance α, and a scale factor defined, for two sided alternative hypothesis as: If H 1 : μ > μ 0, then d μ μ σ. Use chart c) if α =.05, and chart d) if α =.01. If H 1 : μ < μ 0, then d μ μ σ Use chart a) if α =.05, and chart b) if α =.01. d μ μ σ μ 1 is the true mean value and μ 0 is the hypothesized value. Chapter IV 49
50 4 5 Inference on the Mean of a Population, Variance Unknown Confidence Interval on the Mean The probability of the test statistic T, where t α/2, n-1 T t α/2, n-1 is (1 α) and can be written as P t α/2, n 1 T t α/2, n 1 1 α Thus resulting in P where T / t α/2, n 1 X μ S/ n t α/2, n 1 1 α Which after rearranging yields P X t S n μ X t,, Chapter IV 50 S n 1 α
51 4 5.3 Confidence Interval on the Mean If H 1 : μ > μ 0, then X t, Sn μ If H 1 : μ < μ 0, then μ X t, S n Example. Problem 4 54; Problem 4 61 Chapter IV 51
52 4 6 Inference on the Variance of a Normal Population Hypothesis Testing on the Variance of a Normal Population Consider the Null and Alternative Hypothesis to be: The test statistic for this type of problem is: This test statistic is called Chi-Square statistic. Chapter IV 52
53 4 6 Inference on the Variance of a Normal Population Hypothesis Testing on the Variance of a Normal Population The distribution of the Chi square is defined as follows: k is the number of degrees of freedom and Γ is the gamma function. Chapter IV 53
54 4 6 Inference on the Variance of a Normal Population Hypothesis Testing on the Variance of a Normal Population Chi square is not a symmetrical distribution and the shape of the curve is function of the degree of freedom k = = n 1 Chapter IV 54
55 4 6.1 Hypothesis Testing on the Variance of a Normal Population The null and alternative hypothesis tests for the 2 test are expressed as: H 0 : σ 2 = σ 0 2 Two sided alternative hypothesis H 1 : σ 2 σ 0 2 Upper sided alternative hypothesis H 1 : σ 2 > σ 0 2 Lower sided alternative hypothesis H 1 : σ 2 < σ 0 2 Similarly as for hypothesis test on the mean, there are three possibilities to evaluate the null hypothesis, H 0, the P value and Fixed Level criteria are shown next. Chapter IV 55
56 4 6.1 Hypothesis Testing on the Variance of a Normal Population Additionally, the confidence interval criterion can also be applied to test H 0 on the variance. For H 1 : σ 2 σ 0 2 Chapter IV 56
57 4 6.1 Hypothesis Testing on the Variance of a Normal Population If H 1 : σ 2 >σ 02 : n 1 S χ, σ If H 1 : σ 2 < σ 0 2 σ n 1 S χ, Example. Problem 4 66; Problem 4 68 Skip Sections 4.7 through 4.9 Chapter IV 57
58 4 10 Testing for Goodness of Fit Hypothesis testing procedures are designed for problems in which the population or probability distribution is known and the hypotheses involve parameters of the distribution. If instead it is necessary to determine whether a given sample can be considered to fall within a given probability distribution, the test statistic used is: O is the observed count E is the expected count E can be found using the assumed probability distribution followed by the population. For Example: Poisson Distribution or Normal Distribution E can also be estimated from historical count (previous records) Chapter IV 58
59 4 10 Testing for Goodness of Fit 2 table (Table III, p. 489) requires k = = n p 1 as entry value the degree of freedom (DoF) n is the number of samples p is the number of parameters studied The decision on H 0 is made using P value criteria, for a level of significance of 0.05 If P value > 0.05 then H 0 is accepted (fail to reject). If P value < 0.05 then H 0 is rejected. Example. Problem 4 97 Chapter IV 59
60 4 10 Testing for Goodness of Fit Example. A car dealer wants to know whether the sales by color of a particular car model followed this year the expected trend based on historical data, so it can make a proper estimation when placing next year s the order. Car Color Observed Expected (O E) 2 /E Brown Silver 6 7 Red Blue Black Green 4 8 Total 50 Chapter IV 60
280 CHAPTER 9 TESTS OF HYPOTHESES FOR A SINGLE SAMPLE Tests of Statistical Hypotheses
280 CHAPTER 9 TESTS OF HYPOTHESES FOR A SINGLE SAMPLE 9-1.2 Tests of Statistical Hypotheses To illustrate the general concepts, consider the propellant burning rate problem introduced earlier. The null
More informationCH.9 Tests of Hypotheses for a Single Sample
CH.9 Tests of Hypotheses for a Single Sample Hypotheses testing Tests on the mean of a normal distributionvariance known Tests on the mean of a normal distributionvariance unknown Tests on the variance
More informationPractice Problems Section Problems
Practice Problems Section 4-4-3 4-4 4-5 4-6 4-7 4-8 4-10 Supplemental Problems 4-1 to 4-9 4-13, 14, 15, 17, 19, 0 4-3, 34, 36, 38 4-47, 49, 5, 54, 55 4-59, 60, 63 4-66, 68, 69, 70, 74 4-79, 81, 84 4-85,
More informationBusiness Statistics: Lecture 8: Introduction to Estimation & Hypothesis Testing
Business Statistics: Lecture 8: Introduction to Estimation & Hypothesis Testing Agenda Introduction to Estimation Point estimation Interval estimation Introduction to Hypothesis Testing Concepts en terminology
More informationHypothesis Testing: One Sample
Hypothesis Testing: One Sample ELEC 412 PROF. SIRIPONG POTISUK General Procedure Although the exact value of a parameter may be unknown, there is often some idea(s) or hypothesi(e)s about its true value
More informationChapter 5: HYPOTHESIS TESTING
MATH411: Applied Statistics Dr. YU, Chi Wai Chapter 5: HYPOTHESIS TESTING 1 WHAT IS HYPOTHESIS TESTING? As its name indicates, it is about a test of hypothesis. To be more precise, we would first translate
More informationHYPOTHESIS TESTING. Hypothesis Testing
MBA 605 Business Analytics Don Conant, PhD. HYPOTHESIS TESTING Hypothesis testing involves making inferences about the nature of the population on the basis of observations of a sample drawn from the population.
More informationST Introduction to Statistics for Engineers. Solutions to Sample Midterm for 2002
ST 314 - Introduction to Statistics for Engineers Solutions to Sample Midterm for 2002 Problem 1. (15 points) The weight of a human joint replacement part is normally distributed with a mean of 2.00 ounces
More information7.2 One-Sample Correlation ( = a) Introduction. Correlation analysis measures the strength and direction of association between
7.2 One-Sample Correlation ( = a) Introduction Correlation analysis measures the strength and direction of association between variables. In this chapter we will test whether the population correlation
More informationSTAT Chapter 8: Hypothesis Tests
STAT 515 -- Chapter 8: Hypothesis Tests CIs are possibly the most useful forms of inference because they give a range of reasonable values for a parameter. But sometimes we want to know whether one particular
More informationStatistics 251: Statistical Methods
Statistics 251: Statistical Methods 1-sample Hypothesis Tests Module 9 2018 Introduction We have learned about estimating parameters by point estimation and interval estimation (specifically confidence
More informationChapter 7: Hypothesis Testing
Chapter 7: Hypothesis Testing *Mathematical statistics with applications; Elsevier Academic Press, 2009 The elements of a statistical hypothesis 1. The null hypothesis, denoted by H 0, is usually the nullification
More informationFirst we look at some terms to be used in this section.
8 Hypothesis Testing 8.1 Introduction MATH1015 Biostatistics Week 8 In Chapter 7, we ve studied the estimation of parameters, point or interval estimates. The construction of CI relies on the sampling
More informationChapter 7: Hypothesis Testing - Solutions
Chapter 7: Hypothesis Testing - Solutions 7.1 Introduction to Hypothesis Testing The problem with applying the techniques learned in Chapter 5 is that typically, the population mean (µ) and standard deviation
More informationCIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8
CIVL - 7904/8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 Chi-square Test How to determine the interval from a continuous distribution I = Range 1 + 3.322(logN) I-> Range of the class interval
More informationECO220Y Review and Introduction to Hypothesis Testing Readings: Chapter 12
ECO220Y Review and Introduction to Hypothesis Testing Readings: Chapter 12 Winter 2012 Lecture 13 (Winter 2011) Estimation Lecture 13 1 / 33 Review of Main Concepts Sampling Distribution of Sample Mean
More informationPerformance Evaluation and Comparison
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Introduction 2 Cross Validation and Resampling 3 Interval Estimation
More informationQuantitative Methods for Economics, Finance and Management (A86050 F86050)
Quantitative Methods for Economics, Finance and Management (A86050 F86050) Matteo Manera matteo.manera@unimib.it Marzio Galeotti marzio.galeotti@unimi.it 1 This material is taken and adapted from Guy Judge
More informationhypothesis a claim about the value of some parameter (like p)
Testing hypotheses hypothesis a claim about the value of some parameter (like p) significance test procedure to assess the strength of evidence provided by a sample of data against the claim of a hypothesized
More informationA3. Statistical Inference
Appendi / A3. Statistical Inference / Mean, One Sample-1 A3. Statistical Inference Population Mean μ of a Random Variable with known standard deviation σ, and random sample of size n 1 Before selecting
More informationChapter 9 Inferences from Two Samples
Chapter 9 Inferences from Two Samples 9-1 Review and Preview 9-2 Two Proportions 9-3 Two Means: Independent Samples 9-4 Two Dependent Samples (Matched Pairs) 9-5 Two Variances or Standard Deviations Review
More informationIntroductory Econometrics. Review of statistics (Part II: Inference)
Introductory Econometrics Review of statistics (Part II: Inference) Jun Ma School of Economics Renmin University of China October 1, 2018 1/16 Null and alternative hypotheses Usually, we have two competing
More informationVisual interpretation with normal approximation
Visual interpretation with normal approximation H 0 is true: H 1 is true: p =0.06 25 33 Reject H 0 α =0.05 (Type I error rate) Fail to reject H 0 β =0.6468 (Type II error rate) 30 Accept H 1 Visual interpretation
More informationStatistical Process Control (contd... )
Statistical Process Control (contd... ) ME522: Quality Engineering Vivek Kumar Mehta November 11, 2016 Note: This lecture is prepared with the help of material available online at https://onlinecourses.science.psu.edu/
More informationClass 24. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 4 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 013 by D.B. Rowe 1 Agenda: Recap Chapter 9. and 9.3 Lecture Chapter 10.1-10.3 Review Exam 6 Problem Solving
More informationF79SM STATISTICAL METHODS
F79SM STATISTICAL METHODS SUMMARY NOTES 9 Hypothesis testing 9.1 Introduction As before we have a random sample x of size n of a population r.v. X with pdf/pf f(x;θ). The distribution we assign to X is
More informationChapter 7 Comparison of two independent samples
Chapter 7 Comparison of two independent samples 7.1 Introduction Population 1 µ σ 1 1 N 1 Sample 1 y s 1 1 n 1 Population µ σ N Sample y s n 1, : population means 1, : population standard deviations N
More informationAMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015
AMS7: WEEK 7. CLASS 1 More on Hypothesis Testing Monday May 11th, 2015 Testing a Claim about a Standard Deviation or a Variance We want to test claims about or 2 Example: Newborn babies from mothers taking
More informationSociology 6Z03 Review II
Sociology 6Z03 Review II John Fox McMaster University Fall 2016 John Fox (McMaster University) Sociology 6Z03 Review II Fall 2016 1 / 35 Outline: Review II Probability Part I Sampling Distributions Probability
More informationCONTINUOUS RANDOM VARIABLES
the Further Mathematics network www.fmnetwork.org.uk V 07 REVISION SHEET STATISTICS (AQA) CONTINUOUS RANDOM VARIABLES The main ideas are: Properties of Continuous Random Variables Mean, Median and Mode
More informationMathematical Statistics
Mathematical Statistics MAS 713 Chapter 8 Previous lecture: 1 Bayesian Inference 2 Decision theory 3 Bayesian Vs. Frequentist 4 Loss functions 5 Conjugate priors Any questions? Mathematical Statistics
More informationSTAT 515 fa 2016 Lec Statistical inference - hypothesis testing
STAT 515 fa 2016 Lec 20-21 Statistical inference - hypothesis testing Karl B. Gregory Wednesday, Oct 12th Contents 1 Statistical inference 1 1.1 Forms of the null and alternate hypothesis for µ and p....................
More informationPurposes of Data Analysis. Variables and Samples. Parameters and Statistics. Part 1: Probability Distributions
Part 1: Probability Distributions Purposes of Data Analysis True Distributions or Relationships in the Earths System Probability Distribution Normal Distribution Student-t Distribution Chi Square Distribution
More information2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008
MIT OpenCourseWare http://ocw.mit.edu 2.830J / 6.780J / ESD.63J Control of Processes (SMA 6303) Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
More informationInference for the mean of a population. Testing hypotheses about a single mean (the one sample t-test). The sign test for matched pairs
Stat 528 (Autumn 2008) Inference for the mean of a population (One sample t procedures) Reading: Section 7.1. Inference for the mean of a population. The t distribution for a normal population. Small sample
More informationMath 152. Rumbos Fall Solutions to Exam #2
Math 152. Rumbos Fall 2009 1 Solutions to Exam #2 1. Define the following terms: (a) Significance level of a hypothesis test. Answer: The significance level, α, of a hypothesis test is the largest probability
More informationStat 529 (Winter 2011) Experimental Design for the Two-Sample Problem. Motivation: Designing a new silver coins experiment
Stat 529 (Winter 2011) Experimental Design for the Two-Sample Problem Reading: 2.4 2.6. Motivation: Designing a new silver coins experiment Sample size calculations Margin of error for the pooled two sample
More informationHYPOTHESIS TESTING II TESTS ON MEANS. Sorana D. Bolboacă
HYPOTHESIS TESTING II TESTS ON MEANS Sorana D. Bolboacă OBJECTIVES Significance value vs p value Parametric vs non parametric tests Tests on means: 1 Dec 14 2 SIGNIFICANCE LEVEL VS. p VALUE Materials and
More informationHypothesis for Means and Proportions
November 14, 2012 Hypothesis Tests - Basic Ideas Often we are interested not in estimating an unknown parameter but in testing some claim or hypothesis concerning a population. For example we may wish
More informationTests about a population mean
October 2 nd, 2017 Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 1: Descriptive statistics Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation Chapter 8: Confidence
More informationHypothesis Tests and Estimation for Population Variances. Copyright 2014 Pearson Education, Inc.
Hypothesis Tests and Estimation for Population Variances 11-1 Learning Outcomes Outcome 1. Formulate and carry out hypothesis tests for a single population variance. Outcome 2. Develop and interpret confidence
More informationChapter Three. Hypothesis Testing
3.1 Introduction The final phase of analyzing data is to make a decision concerning a set of choices or options. Should I invest in stocks or bonds? Should a new product be marketed? Are my products being
More informationIntroduction to Statistical Hypothesis Testing
Introduction to Statistical Hypothesis Testing Arun K. Tangirala Power of Hypothesis Tests Arun K. Tangirala, IIT Madras Intro to Statistical Hypothesis Testing 1 Learning objectives I Computing Pr(Type
More informationINTERVAL ESTIMATION AND HYPOTHESES TESTING
INTERVAL ESTIMATION AND HYPOTHESES TESTING 1. IDEA An interval rather than a point estimate is often of interest. Confidence intervals are thus important in empirical work. To construct interval estimates,
More informationLast two weeks: Sample, population and sampling distributions finished with estimation & confidence intervals
Past weeks: Measures of central tendency (mean, mode, median) Measures of dispersion (standard deviation, variance, range, etc). Working with the normal curve Last two weeks: Sample, population and sampling
More informationSingle Sample Means. SOCY601 Alan Neustadtl
Single Sample Means SOCY601 Alan Neustadtl The Central Limit Theorem If we have a population measured by a variable with a mean µ and a standard deviation σ, and if all possible random samples of size
More informationEC2001 Econometrics 1 Dr. Jose Olmo Room D309
EC2001 Econometrics 1 Dr. Jose Olmo Room D309 J.Olmo@City.ac.uk 1 Revision of Statistical Inference 1.1 Sample, observations, population A sample is a number of observations drawn from a population. Population:
More informationECO220Y Hypothesis Testing: Type I and Type II Errors and Power Readings: Chapter 12,
ECO220Y Hypothesis Testing: Type I and Type II Errors and Power Readings: Chapter 12, 12.7-12.9 Winter 2012 Lecture 15 (Winter 2011) Estimation Lecture 15 1 / 25 Linking Two Approaches to Hypothesis Testing
More informationSummary: the confidence interval for the mean (σ 2 known) with gaussian assumption
Summary: the confidence interval for the mean (σ known) with gaussian assumption on X Let X be a Gaussian r.v. with mean µ and variance σ. If X 1, X,..., X n is a random sample drawn from X then the confidence
More informationT.I.H.E. IT 233 Statistics and Probability: Sem. 1: 2013 ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS
ESTIMATION AND HYPOTHESIS TESTING OF TWO POPULATIONS In our work on hypothesis testing, we used the value of a sample statistic to challenge an accepted value of a population parameter. We focused only
More informationEcon 325: Introduction to Empirical Economics
Econ 325: Introduction to Empirical Economics Chapter 9 Hypothesis Testing: Single Population Ch. 9-1 9.1 What is a Hypothesis? A hypothesis is a claim (assumption) about a population parameter: population
More informationNon-parametric Hypothesis Testing
Non-parametric Hypothesis Testing Procedures Hypothesis Testing General Procedure for Hypothesis Tests 1. Identify the parameter of interest.. Formulate the null hypothesis, H 0. 3. Specify an appropriate
More informationSection 9.4. Notation. Requirements. Definition. Inferences About Two Means (Matched Pairs) Examples
Objective Section 9.4 Inferences About Two Means (Matched Pairs) Compare of two matched-paired means using two samples from each population. Hypothesis Tests and Confidence Intervals of two dependent means
More informationChapter 12: Inference about One Population
Chapter 1: Inference about One Population 1.1 Introduction In this chapter, we presented the statistical inference methods used when the problem objective is to describe a single population. Sections 1.
More informationPsychology 282 Lecture #4 Outline Inferences in SLR
Psychology 282 Lecture #4 Outline Inferences in SLR Assumptions To this point we have not had to make any distributional assumptions. Principle of least squares requires no assumptions. Can use correlations
More informationThe Chi-Square Distributions
MATH 183 The Chi-Square Distributions Dr. Neal, WKU The chi-square distributions can be used in statistics to analyze the standard deviation σ of a normally distributed measurement and to test the goodness
More informationThe Chi-Square Distributions
MATH 03 The Chi-Square Distributions Dr. Neal, Spring 009 The chi-square distributions can be used in statistics to analyze the standard deviation of a normally distributed measurement and to test the
More informationLast week: Sample, population and sampling distributions finished with estimation & confidence intervals
Past weeks: Measures of central tendency (mean, mode, median) Measures of dispersion (standard deviation, variance, range, etc). Working with the normal curve Last week: Sample, population and sampling
More informationLecture 9. ANOVA: Random-effects model, sample size
Lecture 9. ANOVA: Random-effects model, sample size Jesper Rydén Matematiska institutionen, Uppsala universitet jesper@math.uu.se Regressions and Analysis of Variance fall 2015 Fixed or random? Is it reasonable
More informationPreliminary Statistics Lecture 5: Hypothesis Testing (Outline)
1 School of Oriental and African Studies September 2015 Department of Economics Preliminary Statistics Lecture 5: Hypothesis Testing (Outline) Gujarati D. Basic Econometrics, Appendix A.8 Barrow M. Statistics
More informationDifference between means - t-test /25
Difference between means - t-test 1 Discussion Question p492 Ex 9-4 p492 1-3, 6-8, 12 Assume all variances are not equal. Ignore the test for variance. 2 Students will perform hypothesis tests for two
More informationProbability and Statistics Notes
Probability and Statistics Notes Chapter Seven Jesse Crawford Department of Mathematics Tarleton State University Spring 2011 (Tarleton State University) Chapter Seven Notes Spring 2011 1 / 42 Outline
More informationIntroduction to Statistics
MTH4106 Introduction to Statistics Notes 15 Spring 2013 Testing hypotheses about the mean Earlier, we saw how to test hypotheses about a proportion, using properties of the Binomial distribution It is
More information2008 Winton. Statistical Testing of RNGs
1 Statistical Testing of RNGs Criteria for Randomness For a sequence of numbers to be considered a sequence of randomly acquired numbers, it must have two basic statistical properties: Uniformly distributed
More informationChapter 10. Chapter 10. Multinomial Experiments and. Multinomial Experiments and Contingency Tables. Contingency Tables.
Chapter 10 Multinomial Experiments and Contingency Tables 1 Chapter 10 Multinomial Experiments and Contingency Tables 10-1 1 Overview 10-2 2 Multinomial Experiments: of-fitfit 10-3 3 Contingency Tables:
More informationChapter 8 of Devore , H 1 :
Chapter 8 of Devore TESTING A STATISTICAL HYPOTHESIS Maghsoodloo A statistical hypothesis is an assumption about the frequency function(s) (i.e., PDF or pdf) of one or more random variables. Stated in
More informationDesign of Engineering Experiments
Design of Engineering Experiments Hussam Alshraideh Chapter 2: Some Basic Statistical Concepts October 4, 2015 Hussam Alshraideh (JUST) Basic Stats October 4, 2015 1 / 29 Overview 1 Introduction Basic
More informationMath 2000 Practice Final Exam: Homework problems to review. Problem numbers
Math 2000 Practice Final Exam: Homework problems to review Pages: Problem numbers 52 20 65 1 181 14 189 23, 30 245 56 256 13 280 4, 15 301 21 315 18 379 14 388 13 441 13 450 10 461 1 553 13, 16 561 13,
More informationChapter 24. Comparing Means
Chapter 4 Comparing Means!1 /34 Homework p579, 5, 7, 8, 10, 11, 17, 31, 3! /34 !3 /34 Objective Students test null and alternate hypothesis about two!4 /34 Plot the Data The intuitive display for comparing
More informationAPPENDICES APPENDIX A. STATISTICAL TABLES AND CHARTS 651 APPENDIX B. BIBLIOGRAPHY 677 APPENDIX C. ANSWERS TO SELECTED EXERCISES 679
APPENDICES APPENDIX A. STATISTICAL TABLES AND CHARTS 1 Table I Summary of Common Probability Distributions 2 Table II Cumulative Standard Normal Distribution Table III Percentage Points, 2 of the Chi-Squared
More informationChapter 10: Inferences based on two samples
November 16 th, 2017 Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 12 Chapter 1: Descriptive statistics Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation Chapter 8: Confidence
More informationHypothesis testing. Data to decisions
Hypothesis testing Data to decisions The idea Null hypothesis: H 0 : the DGP/population has property P Under the null, a sample statistic has a known distribution If, under that that distribution, the
More informationLECTURE 12 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING
LECTURE 1 CONFIDENCE INTERVAL AND HYPOTHESIS TESTING 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
More informationPreface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of
Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of Probability Sampling Procedures Collection of Data Measures
More informationMathematical statistics
October 20 th, 2018 Lecture 17: Tests of Hypotheses Overview Week 1 Week 2 Week 4 Week 7 Week 10 Week 14 Probability reviews Chapter 6: Statistics and Sampling Distributions Chapter 7: Point Estimation
More informationTwo-Sample Inferential Statistics
The t Test for Two Independent Samples 1 Two-Sample Inferential Statistics In an experiment there are two or more conditions One condition is often called the control condition in which the treatment is
More informationEXAM 3 Math 1342 Elementary Statistics 6-7
EXAM 3 Math 1342 Elementary Statistics 6-7 Name Date ********************************************************************************************************************************************** MULTIPLE
More information10/4/2013. Hypothesis Testing & z-test. Hypothesis Testing. Hypothesis Testing
& z-test Lecture Set 11 We have a coin and are trying to determine if it is biased or unbiased What should we assume? Why? Flip coin n = 100 times E(Heads) = 50 Why? Assume we count 53 Heads... What could
More informationHYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC
1 HYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC 7 steps of Hypothesis Testing 1. State the hypotheses 2. Identify level of significant 3. Identify the critical values 4. Calculate test statistics 5. Compare
More informationInferences about central values (.)
Inferences about central values (.) ]µnormal., 5 # Inferences about. using data: C", C#,..., C8 (collected as a random sample) Point estimate How good is the estimate?.s œc 1 œ C" C# âc8 8 Confidence interval
More informationBasic Concepts of Inference
Basic Concepts of Inference Corresponds to Chapter 6 of Tamhane and Dunlop Slides prepared by Elizabeth Newton (MIT) with some slides by Jacqueline Telford (Johns Hopkins University) and Roy Welsch (MIT).
More informationStatistics for IT Managers
Statistics for IT Managers 95-796, Fall 2012 Module 2: Hypothesis Testing and Statistical Inference (5 lectures) Reading: Statistics for Business and Economics, Ch. 5-7 Confidence intervals Given the sample
More information1; (f) H 0 : = 55 db, H 1 : < 55.
Reference: Chapter 8 of J. L. Devore s 8 th Edition By S. Maghsoodloo TESTING a STATISTICAL HYPOTHESIS A statistical hypothesis is an assumption about the frequency function(s) (i.e., pmf or pdf) of one
More informationLecture 2: Basic Concepts and Simple Comparative Experiments Montgomery: Chapter 2
Lecture 2: Basic Concepts and Simple Comparative Experiments Montgomery: Chapter 2 Fall, 2013 Page 1 Random Variable and Probability Distribution Discrete random variable Y : Finite possible values {y
More informationCHAPTER 17 CHI-SQUARE AND OTHER NONPARAMETRIC TESTS FROM: PAGANO, R. R. (2007)
FROM: PAGANO, R. R. (007) I. INTRODUCTION: DISTINCTION BETWEEN PARAMETRIC AND NON-PARAMETRIC TESTS Statistical inference tests are often classified as to whether they are parametric or nonparametric Parameter
More informationReview of Statistics 101
Review of Statistics 101 We review some important themes from the course 1. Introduction Statistics- Set of methods for collecting/analyzing data (the art and science of learning from data). Provides methods
More informationMTMS Mathematical Statistics
MTMS.01.099 Mathematical Statistics Lecture 12. Hypothesis testing. Power function. Approximation of Normal distribution and application to Binomial distribution Tõnu Kollo Fall 2016 Hypothesis Testing
More informationAnalysis Of Variance Compiled by T.O. Antwi-Asare, U.G
Analysis Of Variance Compiled by T.O. Antwi-Asare, U.G 1 ANOVA Analysis of variance compares two or more population means of interval data. Specifically, we are interested in determining whether differences
More informationDr. Maddah ENMG 617 EM Statistics 10/12/12. Nonparametric Statistics (Chapter 16, Hines)
Dr. Maddah ENMG 617 EM Statistics 10/12/12 Nonparametric Statistics (Chapter 16, Hines) Introduction Most of the hypothesis testing presented so far assumes normally distributed data. These approaches
More informationThe t-statistic. Student s t Test
The t-statistic 1 Student s t Test When the population standard deviation is not known, you cannot use a z score hypothesis test Use Student s t test instead Student s t, or t test is, conceptually, very
More informationInference for Proportions, Variance and Standard Deviation
Inference for Proportions, Variance and Standard Deviation Sections 7.10 & 7.6 Cathy Poliak, Ph.D. cathy@math.uh.edu Office Fleming 11c Department of Mathematics University of Houston Lecture 12 Cathy
More informationSection 10.1 (Part 2 of 2) Significance Tests: Power of a Test
1 Section 10.1 (Part 2 of 2) Significance Tests: Power of a Test Learning Objectives After this section, you should be able to DESCRIBE the relationship between the significance level of a test, P(Type
More informationHypothesis Testing. ) the hypothesis that suggests no change from previous experience
Hypothesis Testing Definitions Hypothesis a claim about something Null hypothesis ( H 0 ) the hypothesis that suggests no change from previous experience Alternative hypothesis ( H 1 ) the hypothesis that
More informationLAB 2. HYPOTHESIS TESTING IN THE BIOLOGICAL SCIENCES- Part 2
LAB 2. HYPOTHESIS TESTING IN THE BIOLOGICAL SCIENCES- Part 2 Data Analysis: The mean egg masses (g) of the two different types of eggs may be exactly the same, in which case you may be tempted to accept
More informationNull Hypothesis Significance Testing p-values, significance level, power, t-tests Spring 2017
Null Hypothesis Significance Testing p-values, significance level, power, t-tests 18.05 Spring 2017 Understand this figure f(x H 0 ) x reject H 0 don t reject H 0 reject H 0 x = test statistic f (x H 0
More informationLecture 7: Hypothesis Testing and ANOVA
Lecture 7: Hypothesis Testing and ANOVA Goals Overview of key elements of hypothesis testing Review of common one and two sample tests Introduction to ANOVA Hypothesis Testing The intent of hypothesis
More informationChapter 23. Inference About Means
Chapter 23 Inference About Means 1 /57 Homework p554 2, 4, 9, 10, 13, 15, 17, 33, 34 2 /57 Objective Students test null and alternate hypotheses about a population mean. 3 /57 Here We Go Again Now that
More informationChapter 23. Inferences About Means. Monday, May 6, 13. Copyright 2009 Pearson Education, Inc.
Chapter 23 Inferences About Means Sampling Distributions of Means Now that we know how to create confidence intervals and test hypotheses about proportions, we do the same for means. Just as we did before,
More informationSection 9.1 (Part 2) (pp ) Type I and Type II Errors
Section 9.1 (Part 2) (pp. 547-551) Type I and Type II Errors Because we are basing our conclusion in a significance test on sample data, there is always a chance that our conclusions will be in error.
More informationProbability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institution of Technology, Kharagpur
Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institution of Technology, Kharagpur Lecture No. # 36 Sampling Distribution and Parameter Estimation
More information