Business Analytics 101: Data Visualization and Storytelling Chart Selection Categorical Hierarchical Relational Temporal Spatial

What This Is

Chart selection is a crucial aspect of business analytics that involves choosing the most effective visualization to communicate insights and drive decision-making. By selecting the right chart, analysts can effectively convey complex data relationships, trends, and patterns to stakeholders, ultimately informing business strategy and driving growth. For instance, a retail company might use a bar chart to compare sales across different regions, a scatter plot to analyze the relationship between price and demand, or a time series chart to forecast future sales.

Key Formulas & Metrics

• Mean Absolute Error (MAE) = (1/n) Σ|yᵢ − ŷᵢ|: average absolute forecast error, where yᵢ is the actual value, ŷᵢ is the predicted value, and n is the number of observations.
• R² = 1 − (SS_res / SS_tot): proportion of variance explained by the regression model, where SS_res is the sum of squared residuals and SS_tot is the total sum of squares.
• Coefficient of Determination (R²) = 1 - (SSE / SST): proportion of variance explained by the regression model, where SSE is the sum of squared errors and SST is the total sum of squares.
• Mean Squared Error (MSE) = (1/n) Σ(yᵢ − ŷᵢ)²: average squared difference between actual and predicted values.
• Root Mean Squared Error (RMSE) = √(MSE): square root of the average squared difference between actual and predicted values.
• Coefficient of Variation (CV) = σ / μ: ratio of standard deviation to mean, where σ is the standard deviation and μ is the mean.
• Standard Deviation (σ) = √(Σ(xᵢ - μ)² / (n - 1)): measure of spread, where xᵢ is each data point, μ is the mean, and n is the number of observations.
• Variance (σ²) = Σ(xᵢ - μ)² / (n - 1): measure of spread, where xᵢ is each data point, μ is the mean, and n is the number of observations.

Step-by-Step Procedure

1. Define the business problem: Clearly articulate the question or problem you're trying to solve.
2. Gather and clean the data: Collect relevant data and ensure it's accurate and complete.
3. Explore the data: Use visualization and summary statistics to understand the data distribution and relationships.
4. Choose the right chart: Select a chart that effectively communicates the insights and relationships in the data.
5. Interpret the results: Clearly explain the findings and implications for the business.
6. Refine and iterate: Refine the analysis and visualization based on feedback and new insights.

Common Mistakes

• Mistake: Confusing correlation with causation.
  • Correction: Establish a clear cause-and-effect relationship between variables, and consider alternative explanations.
• Mistake: Misinterpreting p-values.
  • Correction: Understand the p-value as a measure of evidence against the null hypothesis, and consider the context and sample size.
• Mistake: Using the wrong error metric for a business problem.
  • Correction: Choose an error metric that aligns with the business objective, such as mean absolute error (MAE) for forecasting.

Software / Tool Tips

• Python with pandas and scikit-learn: Use the pandas library for data manipulation and the scikit-learn library for machine learning and visualization.
• R: Use the ggplot2 package for visualization and the dplyr package for data manipulation.
• Excel: Use the Analysis ToolPak add-in for statistical analysis and the Power BI add-in for visualization.
• Tableau: Use the Tableau Desktop software for data visualization and the Tableau Server software for sharing and collaboration.

Quick Practice Problem

• Compute the moving average forecast for period 4 given data: 100, 110, 105, 115: The moving average forecast for period 4 is (100 + 110 + 105 + 115) / 4 = 110.
• What does an R² of 0.85 mean?: An R² of 0.85 means that 85% of the variance in the dependent variable is explained by the independent variable.

Last-Minute Cram Sheet

• ⚠️ p-value is NOT the probability that H₀ is true – it’s the probability of observing the data (or more extreme) if H₀ is true.
• R² measures the proportion of variance explained by the regression model.
• MAE is a measure of average absolute forecast error.
• RMSE is the square root of the average squared difference between actual and predicted values.
• CV is the ratio of standard deviation to mean.
• σ is the standard deviation of a distribution.
• σ² is the variance of a distribution.
• Variance is a measure of spread.
• Standard deviation is a measure of spread.
• Coefficient of variation is a measure of relative spread.