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Chapter 9
Chapter 9

Chapter 9
Chapter 9

STATISTICS 8, FINAL EXAM NAME
STATISTICS 8, FINAL EXAM NAME

p - Binus Repository
p - Binus Repository

6. Hypothesis Testing and the Comparison of 2 or More Populations
6. Hypothesis Testing and the Comparison of 2 or More Populations

... 1. Set up the Hypothesis Statement. (Null: all means are equal) 2. Collect the sample data. 3. Select level of significance –> α = 0.05. 4. Calculate the overall average. 5. a) Estimate 2 via sum of squares due to treatments (SSTR). b) estimate of 2 via sum of squares due to error (SSE). 6. If Nul ...
sample size consideration in clinical research
sample size consideration in clinical research

... be able to detect a situation where the treatment mean is 15 mmHg lower than the control group. • The required effect size is Δ= −15. • We specify that such an effect be detected with 80% power (1-β= .80) when the significance level α = .05. • Past experience with similar study-with similar sphygmom ...
cross-tabulation
cross-tabulation

Document
Document

... Probability of Type II Error • Analyst directly selects the type I error probability • Probability of type II error depends on the choice of sample size • Considering a two-sided test, the probability of the type II error is the probability that Z0 falls between Z/2 and Z/2 given that H1 is true ...
Which Standardized Statistical Procedure Should I Use?
Which Standardized Statistical Procedure Should I Use?

Introduction to Hypothesis Testing
Introduction to Hypothesis Testing

Week9
Week9

Reject H 0
Reject H 0

... Hypothesis Tests The logic behind a confidence interval is that if we build an interval around a sample value there is a high likelihood that the population value will be captured in the interval. The logic behind a hypothesis test is that if we build an interval around a hypothesized value and the ...
Solutions to Final Exam F Problem 1. (See Problem 1 Data in the
Solutions to Final Exam F Problem 1. (See Problem 1 Data in the

Multiple choice REVIEW QUESTIONS: Inference for mean difference.
Multiple choice REVIEW QUESTIONS: Inference for mean difference.

31. A new weight-watching company, Weight Reducers International
31. A new weight-watching company, Weight Reducers International

... We cannot reject the null hypothesis. We don’t have sufficient evidence to conclude that there is a difference in the amounts quoted. 23. A real estate agent in the coastal area of Georgia wants to compare the variation in the selling price of homes on the oceanfront with those one to three blocks f ...
t-Test Worksheet Answers
t-Test Worksheet Answers

Hypothesis testing
Hypothesis testing

Hypothesis Testing - Personal.kent.edu
Hypothesis Testing - Personal.kent.edu

Estimation of the mean Central limit theorem
Estimation of the mean Central limit theorem

Estimating the Effect of Crime Risk on Property Values and Time on
Estimating the Effect of Crime Risk on Property Values and Time on

... Hypothesis Testing Problem: A manufacturer wants to increase the shelf life of a line of cake mixes. Past records indicate that the average shelf life of the mix is 216 days. After a revised mix has been developed, a sample of nine boxes of cake mix had a mean of 217.222 and a standard deviation of ...
Answer Key
Answer Key

... INTERPRET: Because our p-value of 0.0127 is less than our significance level of 0.05, we can reject our null hypothesis. We have evidence that the true mean number of nose picks per day is greater than 3. (Although our results are statistically significant, they may not be practically significant. ...
Document
Document

The Concept of Statistical Significance Testing.
The Concept of Statistical Significance Testing.

a An example
a An example

Make Your Summary-Sheet as You Go-II
Make Your Summary-Sheet as You Go-II

... different days of the weeks. Each time we ask 20 students. Let’s say at the end of the experiment we have 40 samples of 20 responses each. The above process is called sampling technique. What comes out of this experiment is we would get 40 averages. From these averages, we would have an average of ...
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Statistical hypothesis testing

A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. The most common selection techniques are based on either Akaike information criterion or Bayes factor.Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
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