Scatterplots and Mean Absolute Deviation

Front

What is a scatterplot?

Back

A scatterplot is a graph that shows the relationship between two quantitative variables by displaying data points on a two-dimensional plane.

Front

What does a trend line represent in a scatterplot?

Back

A trend line is a straight line that best represents the data points in a scatterplot, indicating the general direction of the relationship between the variables.

Front

How do you interpret a positive correlation in a scatterplot?

Back

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

Front

What does a negative correlation indicate in a scatterplot?

Back

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

Front

What is the mean absolute deviation (MAD)?

Back

Mean absolute deviation is a measure of the dispersion of a set of data points, calculated as the average of the absolute differences between each data point and the mean of the dataset.

Front

How do you calculate the mean of a dataset?

Back

To calculate the mean, add all the values in the dataset and divide by the number of values.

Front

What is the formula for calculating mean absolute deviation?

Back

MAD = (Σ |x_i - mean|) / n, where x_i represents each data point, mean is the average of the dataset, and n is the number of data points.

Front

What does a strong positive correlation look like on a scatterplot?

Back

A strong positive correlation appears as data points that are closely clustered around a line that slopes upwards from left to right.

Front

What does a weak negative correlation look like on a scatterplot?

Back

A weak negative correlation appears as data points that are loosely scattered but generally trend downwards from left to right.

Front

What is the significance of outliers in a scatterplot?

Back

Outliers are data points that fall far from the trend line and can significantly affect the correlation and interpretation of the data.