Explanatory and Response Variables

When looking at relationships between variables, it helps to know which one might be influencing the other. An explanatory variable potentially explains or influences changes in another variable, while a response variable measures the outcome we're interested in.

For example, if Pam wants to predict a student's weight based on height, height is the explanatory variable and weight is the response variable. But if Jim just wants to know if there's a relationship between height and weight without predicting one from the other, we can't designate explanatory and response variables.

Creating a scatterplot is the first step in visualizing relationships between two quantitative variables. To make one:

1. Put the explanatory variable on the x-axis
2. Label and scale both axes appropriately
3. Plot each data point as a single dot on the graph

💡 Always remember this rule: the explanatory variable goes on the x-axis and the response variable goes on the y-axis!

Describing Scatterplots

Once you've created a scatterplot, you need to analyze what it shows. Look for overall patterns and any unusual points that stand out. Describe the relationship by examining three key characteristics:

Direction: Is the relationship positive (points trend upward), negative (points trend downward), or is there no clear direction?

Form: Do the points roughly follow a straight line (linear), a curve, or are they scattered randomly?

Strength: How closely do the points follow the pattern? Strong relationships have points clustered tightly around a pattern, while weak ones have more scattered points.

You should also look for any outliers (individual points that fall outside the overall pattern) and clusters (groups of points that seem to form their own pattern).

For instance, a scatterplot showing SAT scores and the percentage of students taking the exam might reveal "a moderately strong, negative, curved relationship" with "two distinct clusters and two possible outliers."

Remember that even when two variables show a strong relationship, correlation does not imply causation. Just because two things are associated doesn't mean one causes the other!

Interpreting Relationships

When you describe a relationship as positive, linear, and strong, each term has a specific meaning. Let's break it down:

Positive means that as one variable increases, the other tends to increase too. For example, with wolves, longer wolves tend to be heavier.

Linear means that the relationship follows a straight-line pattern. As one variable increases by one unit, the other variable tends to change by a constant amount, on average.

Strong means the data points fall close to a line or pattern, showing a consistent relationship with minimal scatter.

You might notice distinct patterns in your scatterplots. For example, a scatterplot of SAT math scores versus the percentage of students taking the SAT shows a negative relationship - as more students take the test, average scores tend to decrease.

🔍 When analyzing scatterplots in class, practice identifying the direction, form, and strength of relationships. This skill will help you ace questions on tests and understand real data in the world around you.

Understanding Correlation

Correlation (r) measures both the direction and strength of a linear relationship between two quantitative variables. This single number tells us a lot about how two variables relate:

• r is always between -1 and +1
• Positive r indicates a positive association (both variables increase together)
• Negative r indicates a negative association (one increases as the other decreases)
• Values near 0 show weak relationships, while values near ±1 show strong relationships
• Perfect correlations $r = ±1$ only occur with perfect linear relationships

A helpful visual trick: imagine drawing an oval around all points in your scatterplot. A circular shape suggests correlation near 0 (weak), while a long, skinny oval suggests correlation close to ±1 (strong).

Two important limitations to remember about correlation:

1. Correlation doesn't tell you about the form of the association $linear vs. non-linear$
2. A single outlier can dramatically affect the correlation value

Correlation is powerful but has limitations. Always look at your scatterplot first before relying solely on the r-value to understand a relationship.

Properties of Correlation

Correlation has several important properties you should know:

1. Switching x and y variables won't change r
2. Both variables must be quantitative for correlation to work
3. Units don't affect r, and r itself has no units
4. The sign of r tells you the direction of the relationship
5. Correlation values always fall between -1 and +1
6. Correlation measures only linear relationships
7. Outliers can significantly affect correlation

You can calculate correlation using a complex formula that involves standardized values of each variable, but you'll typically use technology like calculators or software to find r in practice.

When interpreting correlation in context, be specific about what the relationship means. For example, "r ≈ 0.9 indicates a strong, positive linear relationship between the number of boats registered in Florida and the number of manatees killed" gives much more information than simply stating the r-value.

🌟 When interpreting correlation, always connect the numbers to the real-world context. This shows deeper understanding than just stating "r equals 0.9."

More Correlation Examples

Let's practice interpreting correlation in different scenarios:

With healing rates in newts, "r ≈ 0.3 indicates a weak, positive linear relationship between the healing rate of limb 1 and limb 2." This means newts that heal quickly in one limb tend to heal somewhat quickly in the other limb, but the relationship isn't very strong.

For stock market returns, "r ≈ -0.1 shows a weak, negative linear relationship between last year's percent return and this year's percent return." This slight negative correlation suggests that good returns last year might slightly predict poorer returns this year, but the relationship is very weak.

Remember to look at the whole picture. Two datasets can have identical correlation values but look completely different when plotted. Correlation is just one tool for understanding relationships, not the complete story.

When describing correlation, include:

• The approximate r-value
• The strength (strong, moderate, or weak)
• The direction (positive or negative)
• The form (linear)
• The context of the variables

This complete description helps you fully understand and communicate what the data is showing.

AP Exam Question Skills

The AP Statistics exam often asks you to interpret statistical relationships. Here's what they're looking for:

When explaining terms like positive, linear, and strong:

• Positive: Show you understand that higher values of one variable appear with higher values of the other (e.g., "wolves with higher values of length also tend to have higher weights")
• Linear: Demonstrate that as one variable increases, the other changes by a consistent amount (e.g., "as length increases by one meter, weight tends to change by a constant amount")
• Strong: Explain that data points fall close to the pattern line

For interpreting slope in a regression equation:

• The slope of 35.02 means "two wolves that differ by one meter in length are predicted to differ by 35.02 kilograms in weight, with the longer wolf having the greater weight"

You might also need to calculate values using regression equations and residuals:

• Remember that residual = actual value - predicted value
• So actual value = predicted value + residual

📝 On the AP exam, always include context in your answers. Don't just define terms abstractly - refer to the specific variables mentioned in the problem.

AP Scoring Guidelines

The AP exam rewards precise statistical language and contextual understanding. Here's what earns full credit:

When defining terms like positive, linear, and strong:

• Positive relationship: Clearly show that low values of one variable appear with low values of the other, and high with high (e.g., "As length increases, so does weight")
• Linear relationship: Describe either the visual line pattern or the constant rate of change concept
• Strong relationship: Explain how closely points follow the pattern line

What doesn't work:

• Vague descriptions like "both variables get bigger" or "points are close together"
• Just stating "correlation is greater than 0" without explaining what that means
• Drawing sketches without written explanations

For interpreting slope:

• Include the numerical value (35.02)
• Explain what happens to one variable when the other increases by one unit
• Use qualifying language like "on average" or "predicted" to show the relationship isn't perfect

Remember to always connect statistical concepts to the context of the problem. Generic definitions without application to the specific scenario won't earn full credit on the AP exam.