Here is a list of 100 command prompts for performing a comprehensive scatter plot analysis, categorized by workflow.
1. Pre-Analysis & Data Preparation
Define the primary hypothesis to be tested (e.g., "Does X affect Y?").Select the independent variable to be plotted on the X-axis.Select the dependent variable to be plotted on the Y-axis.Verify that both X and Y variables are numerical (continuous or discrete).Scan for and count missing (null/NaN) values in the selected columns.Formulate a strategy for handling missing values (e.g., remove rows, impute).Identify potential outliers in X and Y using the IQR or Z-score method.Decide on a strategy for outliers (e.g., investigate, remove, or cap).Check the distribution of X and Y (e.g., using a histogram).Apply a log transformation to [X and/or Y] if the data is highly skewed.Sample the dataset (e.g., 10,000 random points) if it's too large to render clearly.
2. Basic Plot Generation
Generate a 2D scatter plot of [Y variable] vs. [X variable].Set a descriptive title that explains the plot (e.g., "Relationship between...").Label the X-axis clearly with the variable name and its units.Label the Y-axis clearly with the variable name and its units.Set the X-axis scale to start and end at [min/max values].Set the Y-axis scale to start and end at [min/max values].Change the X-axis and/or Y-axis to a logarithmic scale.Add gridlines to the plot background.Adjust the aspect ratio of the plot (e.g., make it square).
3. Pattern & Relationship Analysis
Identify the primary **direction** of the relationship (positive, negative, or no association).Assess the **form** of the relationship (e.g., linear, curvilinear/U-shaped, exponential).Determine the **strength** of the relationship (e.g., strong, moderate, weak, or no correlation).Visually identify any distinct **clusters** or groups of data points.Scan for any **gaps** or "dead zones" in the plot where no data exists.Identify any **global outliers** that deviate from the main pattern.Identify any **local outliers** that deviate from their immediate cluster.Check for **heteroscedasticity** (where the spread of Y changes as X changes, often a "fanning" or "funnel" shape).Check for **homoscedasticity** (where the spread of Y is constant across all values of X).Analyze the **density** of points (e.g., are they concentrated in one area?).Check if the relationship **changes form** at a certain X value (a structural break).Look for any **horizontal or vertical bands** of data, which may indicate discretized or capped values.
4. Statistical Analysis & Quantification
Calculate the **Pearson correlation coefficient (r)** for linear relationships.Calculate the p-value for the Pearson correlation to test for statistical significance.Calculate the **Spearman's rank correlation (rho)** for monotonic, non-linear relationships.Calculate the **covariance** between X and Y.Perform a simple linear regression (Y ~ X) and extract the equation.Plot the **line of best fit** (regression line) over the scatter plot.Interpret the **slope (coefficient)** of the regression line (e.g., "For every 1-unit increase in X...").Interpret the **intercept** of the regression line (e.g., "The value of Y when X is 0...").Calculate the **R-squared (R²)** value to determine the proportion of variance explained.Plot the **95% confidence interval** band around the regression line.Perform a polynomial (non-linear) regression if the pattern is curved.Add a **LOWESS (Locally Weighted Scatterplot Smoothing)** line to visualize a non-linear trend.
5. Multivariate & Advanced Plotting
Map a third **categorical variable** to the **color** of the points.Map a third **categorical variable** to the **marker style** (e.g., circle, square, triangle).Map a third **numerical variable** to the **point size** (creating a bubble chart).Map a third **numerical variable** to the **color gradient** (e.g., low=blue, high=red).Map a fourth variable by using **both size and color**.Generate a **3D scatter plot** using [X, Y, Z] variables.Create a **scatter plot matrix (pairs plot)** to visualize all pairwise relationships between [N] variables.Generate a **joint plot** with marginal histograms or density plots on the X and Y axes.Reduce point **opacity (alpha)** to handle mild overplotting.Switch to a **2D density plot (heatmap)** or **hexbin plot** for severe overplotting.Create a **connected scatter plot** where points are joined by a line (e.g., to show change over time).Add **text labels** or annotations to specific data points (e.g., key outliers).
6. Group-Based & Categorical Analysis
Analyze the scatter plot for [Group A] only, filtering out all others.Compare the **slope** of the relationship for [Group A] vs. [Group B].Compare the **correlation strength** (e.g., Pearson's r) for [Group A] vs. [Group B].Check if one group is consistently **above or below** another group on the Y-axis.Fit and plot **separate regression lines** for each colored group on the same plot.Create **faceted scatter plots** (small multiples) with one plot for each category.Look for **Simpson's Paradox** (where a trend within groups reverses or disappears when groups are combined).Analyze the variance and spread *within* each group.Identify which group contains the most significant outliers.
7. Diagnostic & Model-Based Analysis
Plot **Actual (Observed) values vs. Predicted values** from a model.On the Actual vs. Predicted plot, check if points fall along the **y=x (45-degree) line**.Plot **Residuals vs. Fitted (Predicted) values** to check model assumptions.Check the Residuals vs. Fitted plot for any **"U-shape"** (non-linearity) or **"funnel"** (heteroscedasticity) patterns.Plot **Residuals vs. a specific predictor (X variable)** to check for missed patterns.Plot a **Scale-Location (or Spread-Location)** plot (sqrt(|residuals|) vs. fitted).Plot **Residuals vs. Leverage** to identify influential points.Plot **Cook's Distance vs. Observation Index** to find high-influence points.Generate a **Q-Q (Quantile-Quantile) plot** of residuals to check for normality.Plot a **lag plot** ([variable] at time T vs. time T-1) to check for autocorrelation.Plot the results of a K-means clustering, coloring points by their assigned cluster.Plot the results of a PCA, showing **Principal Component 1 vs. Principal Component 2**.
8. Interpretation & Communication
Summarize the form, direction, and strength of the relationship in one sentence.Translate the correlation coefficient (r) into a plain-language description (e.g., "a strong positive link").Translate the regression slope into a business-relevant statement (e.g., "For every $1 in ad spend, sales increase by $X").**State clearly that "correlation does not imply causation."**Brainstorm potential **lurking (confounding) variables** that could explain the relationship.Formulate a follow-up hypothesis based on the observed patterns (e.g., the U-shape).Recommend a business action based on the plot (e.g., "Invest in X to raise Y," or "Stop investing after X hits [value]").Write a clear and concise caption that explains the plot to a non-technical audience.**Annotate the plot** with callout boxes for outliers, clusters, and key turning points.Isolate a specific, high-leverage outlier and investigate the story behind that data point.Explain *why* the relationship might be non-linear (e.g., "diminishing returns").Compare the plot's findings to a known benchmark, industry standard, or prior research.Explain the implications of the identified heteroscedasticity (e.g., "predictions are less reliable for larger X values").Create a "story" from the plot (e.g., "At first, X and Y rise together, but then the effect levels off...").Prepare the plot for export (set high resolution, clean labels, appropriate theme).Formulate the single most important takeaway (the "elevator pitch") from the scatter plot.Plot a "before" vs. "after" paired scatter plot, checking for points above/below the y=x line.Generate a log-log plot to test for a power-law relationship.Plot [latitude] vs. [longitude] (a geo-scatter plot) and map data to color/size.Analyze the relationship *within* a specific quantile (e.g., only the top 10% of X).Overlay a contour plot on the scatter plot to better visualize density.Create an interactive version of the plot with hover-tooltips for each point.Determine if the relationship is strong enough to be practically significant, not just statistically significant.
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