TAILIEUCHUNG - Lecture Basic statistics for business & economics (8/e): Chapter 10 – Lind, Marchal, Wathen

Chapter 10 - One-sample tests of hypothesis. In this chapter you will be able to: Define a hypothesis, explain the five-step hypothesis-testing procedure, define type i and type ii errors, define the term test statistic and explain how it is used, distinguish between a one-tailed and a two-tailed hypothesis,. | One-Sample Tests of Hypothesis Chapter 10 McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. LEARNING OBJECTIVES LO 10-1 Define a hypothesis. LO 10-2 Explain the five-step hypothesis-testing procedure. LO 10-3 Define Type I and Type II errors. LO 10-4 Define the term test statistic and explain how it is used. LO 10-5 Distinguish between a one-tailed and a two-tailed hypothesis. LO 10-6 Conduct a test of hypothesis about a population mean. LO 10-7 Compute and interpret a p-value. LO 10-8 Conduct a test of hypothesis about a population proportion. 10- The concepts introduced in this chapter as they relate to hypothesis testing procedures are very important to understand as they will be applied to the next several chapters. We’re going to learn the five -step hypothesis testing procedure, understand the difference between a one tailed and the two tailed test of hypothesis and apply this concept to a population mean or proportion. We will also study the two types of errors in hypothesis testing. Hypothesis and Hypothesis Testing HYPOTHESIS A statement about the value of a population parameter developed for the purpose of testing. HYPOTHESIS TESTING A procedure based on sample evidence and probability theory to determine whether the hypothesis is a reasonable statement. TEST STATISTIC A value, determined from sample information, used to determine whether to reject the null hypothesis. CRITICAL VALUE The dividing point between the region where the null hypothesis is rejected and the region where it is not rejected. LO 10-1 Define a hypothesis. LO 10-2 Explain the five-step hypothesis-testing procedure. 10- Decisions and Errors in Hypothesis Testing LO 10-3 Describe the Type I and Type II errors. Type I Error Defined as the probability of rejecting the null hypothesis when it is actually true. This is denoted by the Greek letter .” Also known as the significance level of a test. Type II Error Defined as the .

• Quản trị kinh doanh
• Statistics for business
• Describing data
• Statistical techniques
• One sample tests
• Hypothesis testing
• One sample tests of hypothesis
• Lecture Business statistics
• Bài giảng Thống kê doanh nghiệp
• Business statistics
• Descriptive statistics tabular
• Descriptive statistics graphical presentations
• Summarizing quantitative data
• Essentials of modern business statistics
• Modern business statistics
• Descriptive statistics
• Graphical presentations
• numerical measures
• Exploratory data analysis
• Interval estimation
• Hypothesis tests
• Simple linear regression
• Basic statistics
• Types of statistics
• Quantitative variables
• Ordinal level data
• Business statistics in practice
• Data sources
• Statistical inference
• Illustrate sampling
• Graphical methods
• Tabular methods
• Graphically summarizing qualitative data
• Numerical methods
• Describing central tendency
• Measures of variation
• Sample spaces
• Elementary probability rules
• Compute conditional probabilities
• Discrete random variables
• Discrete probability distributions
• Binomial distribution
• Continuous random variables
• Continuous probability distributions
• Uniform distribution
• Sampling distributions
• Random sampling
• Stratified random
• Confidence intervals
• Sample size determination
• Alternative hypotheses
• Sampling distribution
• Equal variance
• Statistical inferences
• Population variances
• Experimental design
• Analysis of variance
• Randomized block design
• Chi square tests
• Chi square goodness of fit tests
• Multinomial experiment
• Simple linear regression analysis
• Simple linear regression model
• Model assumptions
• Multiple regression
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• Multiple regression model
• Times series forecasting
• Index numbers
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• Statistical process control
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• Control charts
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• Decision theory
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• Posterior probabilities