MQM100_HypothesisTest_OnePopulation

# MQM100_HypothesisTest_OnePopulation - MQM-100 Chapter 7...

This preview shows pages 1–4. Sign up to view the full content.

1 MQM-100 Chapter 7: Sampling Distributions ( x , p ˆ , ( 1 x - 2 x ), ( 1 ˆ p - 2 ˆ p ) ) Chapter 8: Estimation of µ & P and determine sample size (n) -- One Population Chapter 9: Hypothesis Tests of µ & P -- One Population Chapter 10: Estimation & Hypothesis Tests of Means & Proportions -- Two Population Chapter 9: Hypothesis Tests about the Mean ( µ ) and Proportion (P)-- One Population 1. Concepts of Hypothesis Test 2. Components (3) of Hypothesis Testing: B) Test Statistic, or P-value, or Confidence Interval. C) Conclusion/Decision. 3. Hypothesis Test about a PARAMETER: e.g., µ , P 4. Hypothesis Test about a population mean ( µ ): i) σ 2 - Known, ii) σ 2 - Unknown but n 30, iii) σ 2 - Unknown but n < 30 5. Example of hypothesis test about a population mean: using three (3) different approach. 6. Hypothesis Test about a population proportion (P).

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 Testing Hypothesis About Population Parameters (pp.377-378) Toothpaste Example: Marketing a new brand-A tooth paste. Hypothesis No. 1 : More than 50% of all consumers prefer brand A. Sample of 100: 99 prefer brand A Conclusion: hypothesis is true. Hypothesis No. 2 : Less than 50% of all consumers prefer brand A. Sample of 100: 99 prefer brand A (a rare event and the probability is small). Conclusion: hypothesis is not true. The Null Hypothesis is the hypothesis being tested and is denoted by H 0 . (p-378) The Alternative Hypothesis is the opposite of the H 0 and describes the desired goal of the experiment denoted by H a (or H 1 ) which is our research hypothesis. (p-378) A test statistic is a statistic which is used in the testing of H 0 , e.g. Z, t, F, etc. Since a test statistic is a random variable, it has a probability distribution. Type I Error and Type II Error. (p-380) Level of significance (or significance level) is denoted by α . Probability (type I error) = α Probability (type II error) = β Probability (reject H 0 /H 0 False) = 1 - β Power of the test = 1 - β Type I and Type II Errors (p-381) H 0 is true H 0 is false Do not Correct decision Type II error Reject H 0 Prob. is 1- α P (Type II error) = β Reject H 0 Type I error Correct decision P (Type I error) = α Prob. is 1- β
3 Components of Hypothesis Testing 1. Null hypothesis: specifies a value of the parameter. Example: H

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 8

MQM100_HypothesisTest_OnePopulation - MQM-100 Chapter 7...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online