Would you consider each to have strengths and weaknesses?

1. Review the (Maruti Suzuki) case study data related to your research problem and describe potential analysis tools and methods you will use to answer your research question. Be specific: Reference specific descriptive and inferential statistics that you plan to use in your analysis. To support your plan, reference a business research study that implemented this method, and explain why you believe it was appropriate.
2. In your own words, compare and contrast descriptive and predictive analytics. In terms of their application, would you consider each to have strengths and weaknesses? Or would you describe the differences as tools for different purposes?

Much of the reading and the activity in this module focuses on the k-means algorithm.

3. The course refers to cluster analysis and association analysis as search heuristics. In your own words, describe how the term heuristics applies to these two models. After watching the video Data Mining, respond to your peers by discussing your experience with search heuristics.

This experience may include the following:
a. Obvious examples such as shopping suggestions from Amazon.com, music or movie suggestions from Pandora or Netflix, or text suggestions from autocorrect features on mobile devices
b. Discreet or inconspicuous examples such as the Target incident mentioned in the video
c. In addition to sharing your experience, share your personal view on the ethics of using such heuristics.

SAMPLE SOLUTION

The research problem in question centered on the need to determine whether the MSIL would meet consumer demands and, at the same time, maintain a competitive price point, especially for price-conscious young buyers. The plan will involve using the data drawn from the case study (exhibits 7, 8, and 13). The case study exhibits explore the reasons for consumers’ purchase of different vehicle brands. These reasons demonstrated consumer wants and how they can be used to establish for purchase. Jaydeep et al. (2021) gave such reasons to include brand reputation, brand recommendation, and previous experiences with the brand. These reasons are explored in detail in the case study.             The data table will be collected via a survey where the focus will be on understanding the motivating factor of the consumers’ purchase decisions. The essence of different vehicle brand attributes is broken down into the consumer wants using a 1-to-10 numerical system, with 10 being the “most important”. The data set from the case study exhibits are helpful to MSIL by…