Final answer:
An analysis of favorable and unfavorable price variances is an example of diagnostic analytics. When analyzing these variances, businesses are trying to understand the reasons behind the deviations from expected costs. The comparison between diagnostic, descriptive, predictive, and prescriptive analytics clarifies their differences in data analysis. Option d is the correct answer.
Step-by-step explanation:
An analysis of favorable and unfavorable price variances would be an example of diagnostic analytics. This type of analytics is concerned with understanding the why something happened. When a company analyzes price variances, they are looking into the causes behind the variances from expected amounts.
If the variance is favorable, it means costing was better than expected, which might be due to negotiated discounts or lower raw material prices. Conversely, unfavorable variances indicate higher-than-expected costs, which could be the result of price increases from suppliers or low production efficiencies.
Descriptive analytics would describe what has happened based on data, but without providing reasons. Predictive analytics uses statistical models and forecasts to understand future events, and prescriptive analytics suggests actions that can affect desired outcomes. In the context of the student's question, these refer to different types of data analysis in business.
For the second part:
D. The scientist determining the average decrease in tumor size for the drug-treated group is using descriptive statistics. This is because it involves summarizing raw data from the study to provide an insight into the data set.
It is not raw data, as it involves calculation and summarization, nor is it inferential statistics, which would be used to make predictions or inferences based on the sample data to a larger population.