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Test 2 - Flashcards

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Class:BUSN 231 - Statistical Thinking
Subject:Business
University:Christopher Newport University
Term:Fall 2009
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Standard error the amount by which the value obtained in a random sample differs from the counterpart value in the population
Central Limit Theorem If a population has a mean of µ and a standard deviation of σ, then the distribution of sample means ( x bar ) will follow a normal distribution with a mean of µ and a standard deviation of σ/ √n as long as the sample size is large enough (n ≥ 30).
Hypothesis a conjecture about the real world or the “state of nature”
Hypothesis testing the process of validating a belief about the value of the parameter.
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General hypothesis hypothesis which is made up of words or sentences
Statistical hypothesis a hypothesis stated mathematically
Null not any, none, nil, suggesting a zero value
Null hypothesis the point of inference
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Alternate hypothesis a statement of inequality about the value of a parameter, generally the opposite of a null hypothesis
Test statistic a summary measure of a sample used as evidence in the test to either support or discredit the null hypothesis
significant difference a difference between the hypothesized value of a parameter and the observed value of a statistic that is too great to be explained by sampling variation alone.
Level of significance the probability we will conclude there is a significant difference between the statistic and the parameter when the null hypothesis is true.
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decision rule an objective statement that allows the researcher to determine whether the sample evidence supports or discredits the null hypothesis
critical value of the statistic the values of a statistic that separate the acceptance region from the rejection region.
region of rejection a set of unlikely values of the test statistic that imply rejection of the null hypothesis.
region of acceptance a set of values of the test statistic that we can conclude the difference between the sample mean and the hypothesized value of the mean is not a significant difference.
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 Standard errorthe amount by which the value obtained in a random sample differs from the counterpart value in the population
 Central Limit TheoremIf a population has a mean of µ and a standard deviation of σ, then the distribution of sample means ( x bar ) will follow a normal distribution with a mean of µ and a standard deviation of σ/ √n as long as the sample size is large enough (n ≥ 30).
 Hypothesisa conjecture about the real world or the “state of nature”
 Hypothesis testingthe process of validating a belief about the value of the parameter.
 General hypothesishypothesis which is made up of words or sentences
 Statistical hypothesisa hypothesis stated mathematically
 Nullnot any, none, nil, suggesting a zero value
 Null hypothesisthe point of inference
 Alternate hypothesisa statement of inequality about the value of a parameter, generally the opposite of a null hypothesis
 Test statistica summary measure of a sample used as evidence in the test to either support or discredit the null hypothesis
 significant differencea difference between the hypothesized value of a parameter and the observed value of a statistic that is too great to be explained by sampling variation alone.
 Level of significancethe probability we will conclude there is a significant difference between the statistic and the parameter when the null hypothesis is true.
 decision rulean objective statement that allows the researcher to determine whether the sample evidence supports or discredits the null hypothesis
 critical value of the statisticthe values of a statistic that separate the acceptance region from the rejection region.
 region of rejectiona set of unlikely values of the test statistic that imply rejection of the null hypothesis.
 region of acceptancea set of values of the test statistic that we can conclude the difference between the sample mean and the hypothesized value of the mean is not a significant difference.
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