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| 13329338618 | chi-square goodness of fit test | tests the H0 that a categorical variable has a specified distribution in the population point of interest one way table | 0 | |
| 13329352289 | observed counts | Actual numbers of individuals in the sample that fall in each cell of the one-way or two-way table. | 1 | |
| 13329357672 | expected counts | Number of individuals in the sample that would fall in each cell of the table if null were true (find by multiplying sample size by proportion of each category according to the H0) |  | 2 |
| 13329369015 | chi square statistic | a measure of how far the observed counts are from the expected counts |  | 3 |
| 13329374595 | chi square distribution | density curve that has only nonnegative values and skewed right. Chi-square distribution is specified by giving degrees of freedom. The chi-square test for goodness of fit uses the chi-square distribution with degrees of freedom = the number of categories −1 Mean of a particular chi-square distribution = to its degrees of freedom. For df > 2, the mode (peak) of the chi-square density curve is at df − 2. become more "normal" looking as df increases |  | 4 |
| 13329394299 | conditions for performing a chi square goodness of fit test | Random: The data come from a well-designed random sample or randomized experiment 10%: When sampling without replacement, check that n0.10N Large counts: All expected counts≥5. | 5 | |
| 13329404193 | hypotheses for goodness of fit | H0: The stated distribution of the categorical variable in the population of interest is correct. Ha: at least one of The stated distribution of the categorical variable in the population of interest is not correct. | 6 | |
| 13329414362 | find p value using chi square curve | chi square cdf lb: chi^2 value ub:1E99 df: categories-1 (goodness of fit) or # rows-1 x # columns-1 (homogeneity or independent) | 7 | |
| 13329453043 | performing chi square goodness of fit test calculator | stat, test, x^2 GOF test (D) gives x^2. df, contribution to x^2 2nd distr, x^2 cdf | 8 | |
| 13329473762 | chi square test for homogeneity | test to determine whether the distribution of a categorical variable is the same for each of several populations or treatments |  | 9 |
| 13329488233 | chi square test for homogeneity hypotheses | H0: There's no difference in distribution of a categorical variable for several pops or treatments Ha: There's a difference in distribution of a categorical variable for several pops or treatments | 10 | |
| 13329497013 | chi square for independence | test to determine whether there is convincing evidence of an association between the row and the column variables in a 2-way table COMES FROM SINGLE SAMPLE | 11 | |
| 13329504588 | chi-square test for independence hypotheses | H0: There is no association between two categorical variables in the population of interest. Ha: There is an association between two categorical variables in the population of interest. or H0: Two categorical variables are independent in the population of interest. Ha: Two categorical variables are not independent in the population of interest. | 12 | |
| 13329521212 | find expected counts for homogeneity and Independence | when H0 is true | | 13 |
| 13329597687 | finding degrees of freedom for chi square | | 14 | |
| 13329539592 | conditions for performing a chi square for homogeneity or independence | Random: The data come from a well-designed random sample or randomized experiment 10%: When sampling without replacement, check that n0.10N Large counts: All expected counts≥5. | 15 | |
| 13329552724 | difference between homogeneity and Independence | Homogeneity tests whether the distribution of a categorical variable is the same for each of several populations or treatments. Independence tests whether two categorical variables are associated in some population of interest. If data come from 2+ independent random samples or treatment groups in a randomized experiment, then do homogeneity. If the data come from a SRS, with individuals classified according to two categorical variables, use independence. | 16 | |
| 13329566388 | Interpreting P-Value | if the true mean/proportion of the population is (null), the probability of getting a sample mean/proportion of _____ is (p-value) by chance alone. assuming H0, there is a p value probability of observing ... by change alone | 17 |
