# Scatter Plots

### What Are Scatter Plots?

Scatter plots display the relationship between two variables, with each point representing one time period. They help you see if two variables move together, move in opposite directions, or are unrelated.

**Purpose:** Explore correlations between two variables, validate expected relationships, and identify outliers.

### When to Use

**Best For:**

* Checking if two variables are related
* Validating expected correlations (e.g., Spend vs. Sales)
* Detecting non-linear relationships
* Identifying outliers and unusual observations
* Exploring diminishing returns or saturation effects

**Examples:**

* TV Spend vs. KPI
* Price vs. Sales Volume
* Digital Spend vs. Conversion Rate
* Competitor Activity vs. Market Share

### How to Create

**Requirements:**

* Select **exactly 2 variables**
* Click "Scatter Plot" button (📍)
* Click "Generate Chart"

**Chart Layout:**

* X-axis: First selected variable
* Y-axis: Second selected variable
* Each dot: One time period
* Can zoom in both X and Y directions

### Reading Scatter Plots

**Positive Correlation:**

* Points trend upward from left to right
* As X increases, Y increases
* Strong relationship if points are tight
* Example: Higher spend → Higher sales

**Negative Correlation:**

* Points trend downward
* As X increases, Y decreases
* Example: Higher price → Lower volume

**No Correlation:**

* Random scatter, no clear pattern
* Variables are independent
* No predictable relationship

**Non-Linear Pattern:**

* Curved relationship
* Example: Diminishing returns (curve flattens at high spend)
* May need saturation curves in model

### What Variables to Visualize

**Marketing Variables:**

* Channel Spend vs. KPI
* Impressions vs. Conversions
* Reach vs. Sales

**Validation Checks:**

* Expected positive: Spend vs. Sales
* Expected negative: Price vs. Volume
* No expected relationship: Random variables

**Before Modeling:**

* Test assumptions about relationships
* Verify correlations exist
* Identify transformation needs

### Common Patterns

**Linear Relationship:**

* Straight line pattern
* Constant rate of change
* Good for linear regression

**Diminishing Returns:**

* Curve that flattens
* Each additional dollar has less impact
* Needs saturation curve transformation

**Threshold Effect:**

* Flat then steep increase
* Minimum spend needed for impact
* Consider S-curve transformation

**Clustered with Outliers:**

* Most points together
* Few points far away
* Investigate outlier time periods

### Use Cases

**Relationship Validation:**

```
Question: Does TV spend drive sales?
Variables: TV_Spend (X) vs. Sales (Y)
Pattern: Points trend upward
Conclusion: Positive relationship confirmed
```

**Saturation Detection:**

```
Variables: Total_Marketing_Spend vs. KPI
Pattern: Curve flattens at high spend
Insight: Diminishing returns at $500K+ spend
```

**Outlier Investigation:**

```
Variables: Digital_Spend vs. Conversions
Pattern: Most clustered, 2 points far away
Action: Check those time periods for data issues
```

### Interactive Features

**Zoom:**

* Mouse wheel or selection box
* Zoom both X and Y axes
* Focus on specific value ranges

**Tooltip:**

* Hover over any point
* Shows X value, Y value, and date
* Identify specific time periods

**Pan:**

* Click and drag when zoomed
* Navigate to different regions

### Tips

**Variable Selection:**

* Choose variables you expect to be related
* Test one relationship at a time
* Use multiple scatter plots for multiple pairs

**Interpretation:**

* Tight clustering = strong relationship
* Wide scatter = weak relationship
* Look for overall trend, not individual points

**Next Steps:**

* If strong correlation → Include in model
* If no correlation → Reconsider variable
* If non-linear → Apply transformation

**When to Use Other Charts:**

* Time trends → Use Line Charts
* Multiple variables → Use Correlation Matrix
* Multicollinearity → Use Correlation Heatmap

### Summary

**Scatter Plots Show:**

* Relationship between 2 variables
* Correlation strength and direction
* Non-linear patterns
* Outliers

**Use scatter plots to validate assumptions before modeling and identify variables that truly drive your KPI.**
