Scatterplot Predictions

Front

What is a scatterplot?

Back

A scatterplot is a graphical representation of two variables where each point represents an observation in the dataset, showing the relationship between the variables.

Front

What does a positive correlation in a scatterplot indicate?

Back

A positive correlation indicates that as one variable increases, the other variable also tends to increase.

Front

What does a negative correlation in a scatterplot indicate?

Back

A negative correlation indicates that as one variable increases, the other variable tends to decrease.

Front

What does it mean if a scatterplot shows no correlation?

Back

No correlation means there is no discernible relationship between the two variables; changes in one variable do not predict changes in the other.

Front

What is a non-linear correlation?

Back

A non-linear correlation occurs when the relationship between two variables cannot be accurately represented by a straight line.

Front

How can you determine the strength of a correlation in a scatterplot?

Back

The strength of a correlation can be determined by how closely the points cluster around a line; a tighter cluster indicates a stronger correlation.

Front

What is the purpose of using scatterplots in data analysis?

Back

Scatterplots are used to visually assess the relationship between two quantitative variables, helping to identify trends, correlations, and outliers.

Front

What is the significance of outliers in a scatterplot?

Back

Outliers are data points that differ significantly from other observations; they can affect the correlation and overall interpretation of the data.

Front

What is the difference between correlation and causation?

Back

Correlation indicates a relationship between two variables, while causation implies that one variable directly affects the other.

Front

How do you interpret the slope of a line of best fit in a scatterplot?

Back

The slope indicates the direction and rate of change between the two variables; a positive slope means an increase in one variable corresponds to an increase in the other.