Final answer:
Without specific data, we can hypothesize that if the observed sales proportion from new Midwest stores is outside the sales director's model prediction range, the data is inconsistent with the model. A statistical analysis involving a hypothesis test would confirm this consistency.
Step-by-step explanation:
To address the question regarding the proportion of sales from new stores in the Midwest region, we would need specific data from the case study to provide an exact percentage. However, based on the provided reference information, similar exercises involve comparing observed data to an expected model or population proportion using hypothesis testing and confidence intervals.
For instance, if a statistics model predicts that new stores should contribute 30% to the sales, but observed data show they contribute a different percentage, then the sales proportion from new stores can be said to be either higher or lower than the model's expectation. This deviation would then render the data inconsistent with the model if the observed proportion falls outside the model's prediction range or confidence interval. To provide an exact answer, you would perform a hypothesis test. For instance, if the model's prediction is significantly different from the observed data at a 5 percent significance level, then we would say that the data is not in agreement with the model.
For example, in a hypothetical analysis, we might find that the true proportion of sales from new stores in the Midwest is 35%. If the sales director's statistical model had predicted a proportion of 30% with a confidence interval of 28% to 32%, the observed proportion would be higher than expected. Consequently, the sales data would be inconsistent with the model. Utilizing a statistical analysis, we'd test our hypothesis and determine the consistency of the observed data with the statistical model.