Watch Videos Then Choose 3 Cases To Analyze The Purpo 320497 ✓ Solved

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

Analyze three cases based on videos provided, using the JMP software to perform data analysis. For each case, review the accompanying PDF explaining the issues and analysis methods, and analyze the data with JMP, recording your findings in a PDF or Word document. Upload your analysis and the JMP files with graphs and tables. The cases include a Medical Malpractice case involving descriptive statistics and exploratory data analysis, a Credit Card Marketing case utilizing data mining methods like classification trees, validation, confusion matrices, ROC curves, and lift curves, and a Baggage Complaints case comparing complaints for American Eagle, Hawaiian, and United airlines using descriptive statistics and time series plots to explore trends over time and other influencing factors.

Sample Paper For Above instruction

Introduction

Data analytics has become an indispensable tool across various industries, offering insights that drive decision-making, enhance operational efficiency, and improve customer satisfaction. The ability to interpret complex datasets using statistical techniques, visualization tools, and data mining methods empowers organizations to identify patterns, uncover issues, and predict future trends. This paper analyzes three distinct cases—Medical Malpractice, Credit Card Marketing, and Baggage Complaints—to demonstrate how data analytics can be applied effectively to solve specific problems and inform strategic actions.

Medical Malpractice: Descriptive Statistics and Exploratory Data Analysis

The first case involves evaluating medical malpractice data to identify patterns and insights using descriptive statistics and exploratory data analysis (EDA). The dataset includes various features such as the number of malpractice claims over time, types of malpractice, patient demographics, and outcomes. The primary goal is to understand the distribution of malpractice claims, identify any temporal trends, and highlight factors that might contribute to malpractice occurrences.

Using descriptive statistics, measures like mean, median, and standard deviation provide a summary of the claims data. Graphical representations such as histograms, box plots, and time series charts reveal the distribution of claims over time and across different categories. EDA techniques facilitate the detection of outliers, trends, and potential correlations between variables. For instance, a time series plot might demonstrate whether malpractice claims are increasing, decreasing, or remaining stable over several years, while scatter plots can reveal relationships between malpractice types and patient demographics.

These insights enable healthcare providers and regulatory bodies to target specific issues, allocate resources more effectively, and implement policy changes that reduce malpractice risk. Ultimately, the combination of descriptive statistics and EDA provides a comprehensive understanding of the malpractice landscape, laying the foundation for more advanced predictive modeling and decision-making.

Credit Card Marketing: Data Mining with Classification Trees

The second case centers on credit card marketing analysis employing classification trees, a popular data mining technique suitable for predicting categorical outcomes. The dataset comprises customer demographics, transaction histories, credit scores, and marketing responses. The objective is to classify customers into potential responders and non-responders to marketing campaigns, optimize marketing strategies, and improve response rates.

Classification trees segment the data based on variables that best distinguish responders from non-responders, visualized as tree diagrams. Validation methods, including cross-validation, assess the model's accuracy, while confusion matrices evaluate the performance regarding true positives, false positives, true negatives, and false negatives. ROC curves provide a graphical representation of the model's discriminative ability across different thresholds, while lift curves illustrate the effectiveness of targeting specific customer segments.

Applying these techniques enables marketers to identify high-value targets, reduce marketing costs, and increase campaign efficiency. For example, the ROC curve might demonstrate that the model has a high true positive rate with minimal false positives, indicating effective predictive power. Similarly, lift charts can highlight which customer segments yield the greatest response improvements upon targeted marketing strategies.

Baggage Complaints Analysis: Time Series and Factor Examination

The third case compares baggage complaints across three airlines—American Eagle, Hawaiian, and United—utilizing descriptive statistics and time series analysis. The data includes complaints over several months or years, allowing exploration of overall complaint trends and seasonal variations. Additionally, the analysis considers potential influencing factors such as destinations and traveler volume.

Descriptive statistics summarize complaint counts, whereas time series plots reveal patterns over time, indicating whether baggage handling performance is improving or deteriorating. Seasonal decomposition techniques can separate seasonal effects from long-term trends, providing clearer insights into cyclical fluctuations. Further analysis involves examining complaints relative to airline-specific factors, destination types, and passenger volumes to identify correlations.

For instance, if complaints spike during peak travel seasons or specific routes, airlines can develop targeted strategies to address these issues. Understanding whether complaints are decreasing over time might reflect effective quality improvement initiatives. Additionally, correlating complaints with the volume of travelers or seasonal factors can support resource allocation and operational planning.

Conclusion

The three cases exemplify the power of data analytics in solving diverse problems across sectors. Descriptive statistics and EDA in medical malpractice help identify key patterns, guiding policy and clinical practices. Classification trees in credit card marketing facilitate targeted marketing efforts, increasing response rates and reducing costs. Time series and factor analyses in baggage complaints enable airlines to improve service quality by addressing specific issues and seasonal trends. These illustrative examples reinforce the importance of leveraging various analytical techniques to derive actionable insights from data, ultimately enhancing organizational decision-making and performance.

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