Understanding Linear Regression

To establish a causal relationship between variables

To find a nonlinear relationship between variables

To calculate the average of a data set

To find a linear model that best fits a set of data

There is no relationship between x and y values

As the x value increases, the y value increases

As the x value increases, the y value remains constant

As the x value increases, the y value decreases

y = ax + b/x

y = a/x + b

y = mx + b

y = ax^2 + bx + c

It is time-consuming and subjective

It can only be done with small data sets

It is always accurate

It requires advanced software

To calculate the median of the data set

To find the average of the data points

To minimize the total vertical distance between data points and the line

To maximize the distance between data points and the line

The difference between x and y values

The product of all x and y values

The sum of all y values and their cubes

The sum of all x values and their squares

A weak linear correlation

A strong linear correlation

A nonlinear correlation

No correlation

The data is highly correlated

The data is not very correlated

The data is perfectly linear

The data is perfectly nonlinear

Because causation does not imply correlation

Because correlation does not imply causation

Because correlation always implies causation

Because causation always implies correlation

They indicate a weak relationship

They always indicate a causal relationship

They never indicate a causal relationship

They are irrelevant to data analysis