Brown yellow red orange green blue total observed

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Brown Yellow Red Orange Green Blue Total Observed Expected (O-E) 2 /E If your bag reflects the distribution advertised in 1995, there should be little difference between the observed and expected counts. To quantify the difference, we’ll calculate a total which we’ll call “Chi-Square” or X 2 . For each color, perform this calculation: (observed - expected) 2 / expected. Enter each value in the last row of the table. Add up all of these “component” values to find X 2 . If this total value is small, we have little evidence to suggest a difference in distributions. However, the larger X 2 gets, the more evidence we have to suggest the company’s claim may no longer be applicable to bags of milk chocolate m&m’s. To determine the likelihood of observing a difference between observed and expected as extreme as the one we observed, we must look up the p-value on a Chi-square table. Chi- square distributions are skewed right and specified by degrees of freedom. In a Goodness of Fit test, the degrees of freedom equal one less than the number of categories. Find the p-value for our test by looking up X 2 for 5 degrees of freedom. Sketch the curve and observed X 2 below. Interpret the result in the context of the problem. Everything I Ever Wanted to Know about AP Statistics I Learned From a Bag of m&m’s 20
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