BrownYellowRedOrangeGreenBlueTotalObservedExpected(O-E)2/EIf 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 X2. •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 X2.•If this total value is small, we have little evidence to suggest a difference in distributions. However, the larger X2gets, 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 X2for 5 degrees of freedom. Sketch the curve and observed X2below. 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’s20
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