This bar chart plots each category's contribution to the overall chisquare statistic. You can choose a chart that orders the categories by contribution, from largest contribution to smallest contribution.
Categories with a large difference between observed and expected values make a larger contribution to the overall chisquare statistic.
Use a bar chart that plots the observed and expected values for each category to determine whether there is a difference in a particular category.
If you determined that the difference between the observed and expected counts is statistically significant, then you can use this bar chart to determine which categories have the largest difference between observed and expected values.
Use the individual category contributions to quantify how much of the total chisquare statistic is attributable to each category's difference between observed and expected values.
Minitab calculates each category's contribution to the chisquare statistic as the square of the difference between the observed and expected values for a category, divided by the expected value for that category. The chisquare statistic is the sum of these values for all categories.
Categories with a large difference between observed and expected values make a larger contribution to the overall chisquare statistic.
 

The degrees of freedom for the chisquare goodnessoffit test is the number of categories minus 1.
Minitab uses the degrees of freedom to determine the pvalue. The more categories you have in your study, the more degrees of freedom you have.
N is the total sample size. N equals the sum of all the observed counts.
Usually, with a larger sample size, your statistical estimates are more precise.
The observed values are the actual number of observations in a sample that belong to a category.
The expected values are the number of observations that you would expect to occur, on average, if the test proportions were true. Minitab calculates the expected counts by multiplying the test proportions from each category by the total sample size.
You can compare the observed values and the expected values by using the output table or the bar chart.
 

The pvalue is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.
Use the pvalue to determine whether to reject or fail to reject the null hypothesis, which states that the population proportions in each category are consistent with the specified values in each category.
 
