Linear Regression Skills

Identify the independent variable in a linear regression formula.

• The independent variable in a linear regression formula is the error term.

• The independent variable in a linear regression formula is the constant term.

• The independent variable in a linear regression formula is the variable being manipulated or controlled.

• The independent variable in a linear regression formula is the outcome variable.

Identify the dependent variable in a linear regression formula.

• Y

• Z

• X

• A

Calculate the slope (B1) of a linear regression line given data points.

• slope = Sxx/Sxy

• slope = Sxy/Sxx

• slope = Syy/Sxx

• slope = Syy/Sxy

Calculate the intercept (B0) of a linear regression line given data points.

• intercept = mean(y) + slope * mean(x)

• intercept = mean(y) - slope * mean(x)

• intercept = mean(y) * slope * mean(x)

• intercept = mean(y) / slope * mean(x)

What does the slope (B0) of a linear regression line represent?

• The average of the dependent variable

• Rate of change in the dependent variable for a one-unit change in the independent variable

• The intercept of the line

• The standard deviation of the data points

What does the intercept of a linear regression line represent?

• The average of all data points in the dataset.

• The slope of the regression line.

• Value where the line crosses the x-axis when y is equal to zero.

• Value where the line crosses the y-axis when x is equal to zero.

Interpret the regression coefficient of an independent variable.

• The regression coefficient of an independent variable (X) shows the impact of that variable on the dependent variable (Y)

• The regression coefficient of an independent variable (X) is constant across different models

• The regression coefficient of an independent variable (X) is not related to the dependent variable (Y)

• The regression coefficient of an independent (X) variable is always positive

What is the formula for a simple linear regression?

• y = ax + b

• y = mx - b

• y = mx + b

• y = mx + c

Calculate the predicted value using a linear regression equation. Note: intercept is B0, slope is B1

• Predicted value = (slope + input value) + intercept

• Predicted value = (slope / input value) + intercept

• Predicted value = (slope * input value) + intercept

• Predicted value = (slope * input value) - intercept

What is the purpose of using a regression line in data analysis?

• To create chaos in the data

• To show the relationship between variables and make predictions.

• To add unnecessary complexity

• To confuse the audience