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21 Cards in this Set
- Front
- Back
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binomial test
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tests whether a population proportion (p) matches a null expectation (p0) for the population.
assumes a random sample test statistic: the observed number of successes, X formula: P = 2(sum of all the probabilities of obtaining i or more successes from n trials given by the binomial distribution) |
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X2 goodness-of-fit test
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compares observed frequencies in categories of a single variable to the expected frequencies under a random model.
assumes random samples, and the expected count of each cell is greater than one and that no more than 20% of the cells have expected counts less than five. test statistic: X2 with df = number of categories - 1 - number of parameters estimated from the data formula: sum (observed-expected)^2/expected |
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X2 contingency test
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testing the null hypothesis of no association between two or more categorical variables
assume random samples, the expected frequency of each cell is greater than one, no more than 20% of the cells have expected frequencies less than five. test statistic: X2 with df = (r-1)(c-1) formula: sum (observed-expected)^2/expected |
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one-sample t-test
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compares the sample mean of a numerical variable to a hypothesized value
assumes individuals are randomly samples from a population that is normally distributed test statistic: t with df = n - 1 |
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paired t-test
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test whether the mean difference in a population equals a null hypothesized value,
assumes pairs are randomly sampled from a population and the differences are normally distributed test statistic: t with n-1 degrees of freedom, where n is the number of pairs |
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binomial test
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tests whether a population proportion (p) matches a null expectation (p0) for the population.
assumes a random sample test statistic: the observed number of successes, X formula: P = 2(sum of all the probabilities of obtaining i or more successes from n trials given by the binomial distribution) |
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X2 goodness-of-fit test
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compares observed frequencies in categories of a single variable to the expected frequencies under a random model.
assumes random samples, and the expected count of each cell is greater than one and that no more than 20% of the cells have expected counts less than five. test statistic: X2 with df = number of categories - 1 - number of parameters estimated from the data formula: sum (observed-expected)^2/expected |
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X2 contingency test
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testing the null hypothesis of no association between two or more categorical variables
assume random samples, the expected frequency of each cell is greater than one, no more than 20% of the cells have expected frequencies less than five. test statistic: X2 with df = (r-1)(c-1) formula: sum (observed-expected)^2/expected |
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one-sample t-test
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compares the sample mean of a numerical variable to a hypothesized value
assumes individuals are randomly samples from a population that is normally distributed test statistic: t with df = n - 1 |
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paired t-test
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test whether the mean difference in a population equals a null hypothesized value,
assumes pairs are randomly sampled from a population and the differences are normally distributed test statistic: t with n-1 degrees of freedom, where n is the number of pairs |
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binomial test
|
tests whether a population proportion (p) matches a null expectation (p0) for the population.
assumes a random sample test statistic: the observed number of successes, X formula: P = 2(sum of all the probabilities of obtaining i or more successes from n trials given by the binomial distribution) |
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X2 goodness-of-fit test
|
compares observed frequencies in categories of a single variable to the expected frequencies under a random model.
assumes random samples, and the expected count of each cell is greater than one and that no more than 20% of the cells have expected counts less than five. test statistic: X2 with df = number of categories - 1 - number of parameters estimated from the data formula: sum (observed-expected)^2/expected |
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X2 contingency test
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testing the null hypothesis of no association between two or more categorical variables
assume random samples, the expected frequency of each cell is greater than one, no more than 20% of the cells have expected frequencies less than five. test statistic: X2 with df = (r-1)(c-1) formula: sum (observed-expected)^2/expected |
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one-sample t-test
|
compares the sample mean of a numerical variable to a hypothesized value
assumes individuals are randomly samples from a population that is normally distributed test statistic: t with df = n - 1 |
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|
paired t-test
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test whether the mean difference in a population equals a null hypothesized value,
assumes pairs are randomly sampled from a population and the differences are normally distributed test statistic: t with n-1 degrees of freedom, where n is the number of pairs |
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two-sample t-test
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tests whether the difference between the means of two groups equal a null hypothesized value for the difference
assumes both samples are random samples. the numerical variable is normally distributed within both populations. the standard deviation of the distribution si the same in the two populations. test statistic: t with df = n1 + n2 - 2 |
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sign test
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a nonparametric test of whether the median of a population equals a specified constant
assumes random samples test statistic: the number of measurements greater than (or less than) the median, according to the null hypothesis. formula: identical to a binomial test with H0: p = 0.5 |
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single factor ANOVA
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testing the difference among means of k groups simultaneously.
assume the variable is normally distributed with equal standard deviations (and therefore equal variances) in all k populations. each sample is a random sample. test statistic: F with df = k-1, n-k. use the right tail of the F-distribution in ANOVA. |
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correlation coefficient
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measuring the strength of a linear association between two numerical variables
assume bivariate normality and random sampling df = n - 2 |
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t-test of zero linear correlation
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test the null hypothesis that the population parameter is zero
assume bivariate normality and random sampling test statistic: t with df = n - 2 formula: t = r / SEr |
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t-test of a regression slope
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test the null hypothesis that the population parameter b equals a null hypothesized value b.
assume the relationship between X and Y is linear. each Y-measurement at a given X is a random sample from a population of Y-measurements. the distribution of Y-values at each value of X is normal. the variance of Y-values is the same at all values of X. test statistic: t with df = n-2 formula: (b - b0) / SEb |
