# Summary of chi-square:

Here's a summary of the steps needed to do a chi-square goodness of fit test:

## General Steps |
## In the Dilbert example... |

1. Decide on a null hypothesis -- a "model" that the data should fit |
Dilbert's null hypothesis was that the sick days were randomly distributed. |

2. Note your "expected" and "observed" values |
Since 40% of weekdays fall on Monday or Friday, the same should be true of sick days -- or 40 out of 100. The observed value was 42 out of 100. |

3. Find the chi-square-calc [add up (o-e)^{2} / e ] |
We got 0.166 |

4. Look up the chi-square-crit based on your p-value and degrees of freedom. |
With p=0.05 and df=1, chi-square-crit = 3.84. |

5. Determine whether chi-square-calc < chi-square crit -- if so, we say the model fits the data well. |
Chi-square-calc < chi-square-crit, so the deviations are small and the data fit the null model of random sick days. |

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