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Stats quiz 3 part 4
| Question | Answer |
|---|---|
| The chi squared distribution is | not symmetric (not bell-shaped about the mean like the normal or t distributions) |
| The specific shape of a chi-square distribution depends on | the degrees of freedom (like t distribution) but in general, the distributions are right skewed and approach the shape of a normal distributuin as df approaches infinity |
| a chi squared test cannot be | negative |
| Chi squared test is read the same as the | t-table |
| The chi-square goodness of fit test is used to determine | whether the observed frequency distribution of a categorical variable differs significantly from the theoretical (expected) distribution |
| When to use chi square goodness of fit test | one categorical variable from a single population. You want to check if the observed data are consistent with the hypothesized proportion for each category |
| Conditions for the chi square goodness of fit test | Random sample, large population (10 times larger than the sample size at least), categorical variable, expected counts: each expected category count is at least 5 |
| Null and alternative for goodness of fit test | null: the observed frequencies follow the expected distribution. Alternative: the observed frequencies do not follow expected distribution |
| Chi square test is always | a right tailed test |
| Bivariate data | refers to data collected on two variables for each individual on a sample. Each observation provides a pair of values-one for each variable. Goal: is there relationship or association between the two variables |
| Bivariate data can be | either qualitative or quantitative |
| When to use chi square test of independence | you have two or more qualitative variables you want to compare. You want to know if the variables are related (associated) or independent |
| Hypotheses for independence test | null: the variables are independent. Alternative: the variables are NOT independent |
| Chi square test of homogeneity | This test if a proportion is the same across two independent populations. |
| Homogeneity hypotheses | Null: p1=p2 Alternative: p1 does not equal p2 |