ch 7 slater

Chapter 7: Data Analysis and Presentation - Full Detailed Overview

Learning Objectives:

• Master different analytics types: descriptive, diagnostic, predictive, and prescriptive.

• Determine when and how to use different analytics effectively in business.

• Choose and apply the correct visualization techniques for various data analyses.

• Adhere to design principles for simplification, emphasis, and ethical data presentation.

Data Analysis Techniques:

• Descriptive Analytics: Involves summarizing past data to understand what has happened. Techniques include:

  • Exploratory Data Analysis: Understanding data structures, checking assumptions, identifying outliers, and exploring correlations without formal hypothesis testing.

  • Use Cases: Accountants might analyze year-over-year financial changes or compare metrics against industry averages.

• Diagnostic Analytics: Tries to explain why events happened using data analysis:

  • Informal Analysis: Builds on descriptive analytics to propose reasons for observed patterns, such as changes in product sales mix affecting gross margins.

  • Formal Analysis: Uses hypothesis testing to statistically validate reasons for phenomena.

    • Type I Error (False Positive): Incorrectly rejecting a true null hypothesis (e.g., concluding a treatment has an effect when it does not).

    • Type II Error (False Negative): Failing to reject a false null hypothesis (e.g., concluding a treatment has no effect when it actually does).

• Predictive Analytics: Predicts future outcomes based on historical data:

  • Modeling Steps: Include selecting target outcomes, preparing data, and using statistical models to forecast future events.

  • Model Validation: Ensuring the model accurately predicts new data, avoiding overfitting.

• Prescriptive Analytics: Suggests actions based on predictions:

  • Application: Uses predictive models and incorporates AI to suggest decisions that optimize outcomes, continuously updating recommendations based on new data.

Data Presentation Techniques:

• Visualizing Data Benefits: Visual data is processed faster and more intuitively than text, especially because most people are visual learners.

• Choosing the Right Visualization:

  • Comparison Tools: Use bar charts for categorical comparisons and bullet charts to highlight benchmarks.

  • Correlation Tools: Employ scatter plots and heatmaps to illustrate relationships and intensities.

  • Distribution Tools: Utilize histograms for frequency distribution and boxplots to summarize data spread and central tendency.

  • Trend Tools: Line charts depict changes over time, while area charts help visualize volume changes.

  • Part-to-Whole Tools: Pie charts show proportional data, and treemaps provide hierarchical data breakdowns.

Design Principles for Visualizations:

• Simplification: Reducing complexity to make visualizations easy to understand, considering the amount of data, spatial arrangement, and orientation.

• Emphasis: Highlighting critical data through visual cues like color, weight, and placement.

• Ethical Presentation: Avoiding deceptive practices by properly representing data proportionally and chronologically.