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Chi-squared test

Ch 14.2 Chi square part 3 notes

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Statistics
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Statistical hypothesis testing
Probability and statistics
Normal distribution
Statistical tests
Nonparametric statistics
Pearson's chi-squared test
Chi-squared test
P-value
Chi-squared distribution
Categorical variable
Z-test

Chi-Square Test of Indep. - for problems with 2 categorical variables measured across a single population Ex: How does the presence of an exclusive-territory clause in the contract of a business relate to its survival? A study collected data from a sample of 170 franchise firms. (Note: A firm?s contract may or may not contain the entrepreneur?s right to an exclusive territory. This means the new outlet will be the only representative of the franchise in a specified territory and won?t have to compete with other outlets of the same chain.)

ch 14.2 notes part 3 chi square

Subject: 
Statistics
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Statistics
Statistical hypothesis testing
Probability and statistics
Normal distribution
Statistical tests
Nonparametric statistics
Pearson's chi-squared test
Chi-squared test
P-value
Chi-squared distribution
Categorical variable
Z-test

Chi-Square Test of Indep. - for problems with 2 categorical variables measured across a single population Ex: How does the presence of an exclusive-territory clause in the contract of a business relate to its survival? A study collected data from a sample of 170 franchise firms. (Note: A firm?s contract may or may not contain the entrepreneur?s right to an exclusive territory. This means the new outlet will be the only representative of the franchise in a specified territory and won?t have to compete with other outlets of the same chain.)

Biostatistic

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Mann–Whitney U test
Sign test
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Chi-squared test

Biostats Notes Section 2: Error I and II: State of nature: 1% of population has a disease Type I error rate =0.05 (false positive rate) Type II error rate = 0.10 (detection rate = 0.90) If someone has a positive test, what is the probability of the person having the disease? What are consequences of Type I and II errors? Testing a drug for a potential benefit? Testing a drug for harmful side effects? Which error is more serious? 10/24 T-Test: One Sample T-Test: T-statistic: Null Hypothesis: ? = C The sample mean does not differ from the known population mean. Degrees of freedom: df=n-1 Independent samples / Two-sample T-Test: Null Hypothesis: ?1 = ?2 The means do not differ. Degrees of Freedom (df=n1+n2-2):

Statistics-Chi-squared in APES

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Environmental Science
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