Watch Videos: Choose 3 Cases To Analyze The Purpose

Watch Videosthen Choose 3 Case To Analyzethe Purpose Of This Topic Is

Watch videos, then choose 3 cases to analyze the purpose of this topic is for you to be introduced to the power of data analytics. They can be very simple or very complicated. We are going to use the commercial software JMP to demonstrate via a few cases. For each case, there are a PDF file explaining the issues and how to analyze, and a JMP file with the data. You are to view the video, then analyze it while keeping a record of your analysis with a PDF or Word document. When finished, upload the PDF/Word file along with the new JMP file containing your graphs and tables to Blackboard. You must complete a minimum of three cases. Recommended cases include Baggage Complaints and Credit Card Marketing. Credit Card Marketing involves the use of AI/Data Mining in analytics.

Paper For Above instruction

Data analytics has become an essential tool across many industries, providing valuable insights that enhance decision-making processes. This paper examines three distinct cases to illustrate the purpose and potency of data analytics: Medical Malpractice claims analysis, Credit Card Marketing via Data Mining, and Baggage Complaints in the airline industry. By exploring these cases, we demonstrate how statistical and data mining techniques can uncover hidden patterns, improve operational efficiency, and inform strategic decisions.

Medical Malpractice: Descriptive Statistics and Exploratory Data Analysis

The Medical Malpractice case emphasizes understanding the nature and distribution of malpractice claims through descriptive statistics and visual representation. Analyzing variables such as claim frequency, types of malpractice, geographic distribution, and temporal patterns provides foundational insights. Descriptive statistics reveal measures like mean, median, mode, and standard deviation, which describe the central tendency and variability in malpractice claims. Visual tools such as histograms, box plots, and scatter plots aid in identifying outliers, trends, and potential correlations between variables.

For example, time series analysis may uncover periods with increased malpractice claims, possibly correlated with regulatory changes or systemic issues within healthcare providers. Geographic analysis can help identify high-risk regions, prompting targeted interventions or policy adjustments. These insights assist healthcare administrators and policymakers in allocating resources more efficiently, improving patient safety, and reducing malpractice instances over time.

Exploratory Data Analysis (EDA) is crucial in the initial phases of data investigation, helping to formulate hypotheses and guide deeper analysis. Techniques such as correlation matrices or principal component analysis (PCA) can reveal complex relationships among variables, facilitating a comprehensive understanding of malpractice trends and drivers.

Credit Card Marketing: Data Mining Using Classification Trees

The Credit Card Marketing case illustrates the utilization of data mining techniques, particularly classification trees, to segment customers and predict their response to marketing campaigns. Classification trees are useful for handling complex, non-linear data and provide interpretable models that business managers can easily understand. In this context, data features such as demographics, transaction history, credit scores, and prior engagement are used to categorize customers into groups such as likely responders or non-responders.

Model validation is performed using confusion matrices, which evaluate the accuracy of classifications by showing true positives, true negatives, false positives, and false negatives. Metrics such as misclassification rates, ROC (Receiver Operating Characteristic) curves, and lift charts further assess the model's predictive power. ROC curves plot the true positive rate against the false positive rate across different thresholds, indicating the model's discriminative ability.

The use of lift curves helps quantify the increase in response rate achieved by targeting a specific segment compared to random selection. This approach ensures marketing efforts are focused on high-potential customers, leading to improved campaign ROI and customer engagement. Data mining drives strategic decisions in marketing strategies, enabling tailored offers and enhanced customer lifetime value.

Baggage Complaints: Descriptive and Time Series Analysis

The Baggage Complaints case demonstrates how descriptive statistics and time series plots can uncover operational challenges and performance trends of airlines. By comparing complaints across three airlines—American Eagle, Hawaiian, and United—analysts can identify which airline has higher complaint rates and examine fluctuations over time.

Descriptive statistics such as mean, median, and standard deviation are calculated for complaints per airline, providing a quantitative basis for comparison. Time series plots display complaint trends, revealing whether complaints are improving or deteriorating at specific airlines. Seasonal effects are investigated by plotting complaints against time segments like months or quarters, which may correspond to seasonal travel peaks or off-peak periods.

Additional factors influencing baggage complaints include destinations and the volume of travelers. For instance, higher complaints during peak travel seasons or for certain destinations may indicate operational stress. Analyzing the volume of travelers helps determine if increased complaints simply correlate with higher passenger numbers or if underlying service issues exist. This insight guides airlines in process improvement, resource allocation, and strategic planning to enhance customer satisfaction.

Conclusion

The three cases exemplify how data analytic techniques—descriptive statistics, visual analysis, and data mining—are instrumental in deriving meaningful insights. Whether used in healthcare for malpractice reduction, in marketing for customer segmentation, or in airline operations for service improvement, data analytics plays a pivotal role in informed decision-making. As data continues to grow exponentially, mastering these analytical tools becomes increasingly vital for businesses and organizations aiming to optimize performance and achieve competitive advantage.

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