Scatter Plots - SAT Math

The horizontal axis representing the independent variable. Also called the independent variable because it's controlled or measured first.

To show the general direction or pattern of the data. Helps visualize the overall relationship and make predictions.

A pair of values for the two variables being compared. One coordinate from each variable creates a single data point.

To visualize the relationship between two quantitative variables. Shows how one variable changes as another changes.

Dependent variable. The y-axis variable responds to changes in the x-variable.

Correlation coefficient. The $r$ value measures strength and direction of correlation.

A relationship involving two variables. Scatter plots specifically examine two-variable relationships.

No correlation. The variables are not related in a predictable way.

Perfect negative linear relationship. All points lie exactly on a downward-sloping line.

Negative correlation. Downward trend indicates one variable decreases as the other increases.

Negative correlation. As one variable increases, the other decreases.

Interpolation. Predicting between existing points is generally more reliable.

Points trend upwards from left to right. As x increases, y also increases, showing a positive relationship.

Correct: indicate strong correlation. Tightly clustered points show strong, not weak correlation.

From -1 to 1. Values closer to $\pm 1$ indicate stronger linear relationships.

A strong relationship between the variables. Large changes in y correspond to small changes in x.

Linear relationship. Points can be approximated by a straight line.

No correlation. Random scatter with no clear pattern means variables are unrelated.

Perfect positive linear relationship. All points lie exactly on an upward-sloping line.

To quantify the strength and direction of a relationship. Measures how closely the data follows a linear pattern.

Extrapolation. Predicting outside the data range carries more uncertainty.

Predicting values outside the range of the data. Extends the trend line beyond the given data points.

By minimizing the distances from all points to the line. Uses least squares method to minimize total squared distances.

A point that lies far from the general pattern of the data. Look for points that don't follow the main cluster or trend.

The vertical axis representing the dependent variable. Also called the dependent variable because it responds to changes in x.

The model is appropriate for the data. Random residuals suggest the linear model is valid.

The rate of change between the variables. Slope shows how much y changes per unit increase in x.

Groups of data points clustered together. Shows distinct groups or categories within the data set.

Points are scattered randomly with no clear trend. No predictable pattern exists between the two variables.

Points trend downwards from left to right. As x increases, y decreases, showing an inverse relationship.

Positive correlation. As one variable increases, the other also increases.

A weak relationship between the variables. Small changes in y correspond to changes in x.

Positive correlation. Positive slope means both variables increase together.

Line of best fit (or trend line). Drawn through data points to show the overall pattern or trend.

Positive correlation. Upward trend indicates both variables increase together.

As one variable increases, the other variable also increases. Both variables move in the same direction together.

Non-linear relationship. The relationship follows a curved pattern, not a line.

The residuals on the y-axis versus the independent variable. Helps assess whether the linear model fits the data well.

No correlation. Zero means no linear relationship exists between the variables.

Weak negative correlation. $-0.2$ is close to $0$, indicating a weak relationship.

No correlation. Zero slope means y doesn't change as x changes.

Strong positive correlation. $0.85$ is close to $1$, indicating a strong positive relationship.

Data points that deviate significantly from the pattern. These unusual points don't follow the overall trend.

The difference between observed and predicted values. Measures how far each point deviates from the trend line.

No correlation. Random scatter indicates no predictable relationship between variables.

No linear relationship. Variables are not linearly related to each other.

To display the relationship between two quantitative variables. Shows how two numerical variables relate or change together.

Independent variable. The x-axis variable is controlled or chosen first.

Predicting values within the range of the data. Uses the trend line between existing data points.

Negative correlation. Negative slope means variables move in opposite directions.

A graph that shows the relationship between two variables using points. Each point plots one x-value against one y-value to visualize patterns.

Linear regression. Method that creates the best-fitting line through data points.

Outliers are data points that deviate significantly from the trend. They can affect trend lines and correlation strength significantly.

A straight line that best represents the data on a scatter plot. Also called a trend line, it summarizes the data's relationship.

Correlation coefficient. The $r$ value measures strength and direction of correlation.

Linear regression. Method that creates the best-fitting line through data points.

Correct: indicate strong correlation. Tightly clustered points show strong, not weak correlation.

To visualize the relationship between two quantitative variables. Shows how one variable changes as another changes.

Linear relationship. Points can be approximated by a straight line.

No correlation. The variables are not related in a predictable way.

A graph that shows the relationship between two variables using points. Each point plots one x-value against one y-value to visualize patterns.

A straight line that best represents the data on a scatter plot. Also called a trend line, it summarizes the data's relationship.

By minimizing the distances from all points to the line. Uses least squares method to minimize total squared distances.

A strong relationship between the variables. Large changes in y correspond to small changes in x.

A weak relationship between the variables. Small changes in y correspond to changes in x.

No correlation. Zero slope means y doesn't change as x changes.

Predicting values within the range of the data. Uses the trend line between existing data points.

Extrapolation. Predicting outside the data range carries more uncertainty.

To quantify the strength and direction of a relationship. Measures how closely the data follows a linear pattern.

Perfect positive linear relationship. All points lie exactly on an upward-sloping line.

Strong positive correlation. $0.85$ is close to $1$, indicating a strong positive relationship.

Weak negative correlation. $-0.2$ is close to $0$, indicating a weak relationship.

The residuals on the y-axis versus the independent variable. Helps assess whether the linear model fits the data well.

The model is appropriate for the data. Random residuals suggest the linear model is valid.

Independent variable. The x-axis variable is controlled or chosen first.

The horizontal axis representing the independent variable. Also called the independent variable because it's controlled or measured first.

To show the general direction or pattern of the data. Helps visualize the overall relationship and make predictions.

Points are scattered randomly with no clear trend. No predictable pattern exists between the two variables.

No correlation. Random scatter indicates no predictable relationship between variables.

A point that lies far from the general pattern of the data. Look for points that don't follow the main cluster or trend.

Positive correlation. Positive slope means both variables increase together.

Predicting values outside the range of the data. Extends the trend line beyond the given data points.

Interpolation. Predicting between existing points is generally more reliable.

Perfect negative linear relationship. All points lie exactly on a downward-sloping line.

No correlation. Zero means no linear relationship exists between the variables.

The difference between observed and predicted values. Measures how far each point deviates from the trend line.

The vertical axis representing the dependent variable. Also called the dependent variable because it responds to changes in x.

The rate of change between the variables. Slope shows how much y changes per unit increase in x.

No linear relationship. Variables are not linearly related to each other.

From -1 to 1. Values closer to $\pm 1$ indicate stronger linear relationships.

A relationship involving two variables. Scatter plots specifically examine two-variable relationships.

Groups of data points clustered together. Shows distinct groups or categories within the data set.

Dependent variable. The y-axis variable responds to changes in the x-variable.

Negative correlation. Negative slope means variables move in opposite directions.

Points trend downwards from left to right. As x increases, y decreases, showing an inverse relationship.

Outliers are data points that deviate significantly from the trend. They can affect trend lines and correlation strength significantly.

A pair of values for the two variables being compared. One coordinate from each variable creates a single data point.

Points trend upwards from left to right. As x increases, y also increases, showing a positive relationship.

Positive correlation. Upward trend indicates both variables increase together.

Negative correlation. Downward trend indicates one variable decreases as the other increases.

Negative correlation. Downward slope indicates variables move in opposite directions.

Line of best fit (or trend line). Drawn through data points to show the overall pattern or trend.

To display the relationship between two quantitative variables. Shows how two numerical variables relate or change together.

No correlation. Random scatter with no clear pattern means variables are unrelated.

As one variable increases, the other variable also increases. Both variables move in the same direction together.