Identify variables and sample size

Data Element Characteristics in Linear Regression Analysis

Variables Overview

• Independent Variable (X): Click Through Rate

  • Definition: The rate at which customers click on an online advertisement leading them to a point of sale or product information.

  • Purpose: Used to predict the dependent variable.

• Dependent Variable (Y): Weekly Sales

  • Definition: The revenue generated in a single week from sales.

  • Purpose: This is the outcome we are interested in predicting as it relates to business success and profit.

Establishing Relationships

• Determining Dependency:

  • The dependent variable (Y) is what we aim to predict—in this case, weekly sales.

  • The independent variable (X) is used for predictions; here, it is the click through rate.

  • The relationship can be summarized as: X's predict Y's.

• Visual Representation:

  • The independent variable (X) is plotted along the horizontal axis (X-axis), and the dependent variable (Y) is plotted on the vertical axis (Y-axis).

Data Element Characteristics Definition

• Independent Variable:

  • Click Through Rate (utilize the column name as it appears in the regression analysis for consistency)

• Dependent Variable:

  • Weekly Sales (focus on sales as opposed to profit or revenue for clarity)

• Sample Size:

  • Defined as the number of observations: 52 weeks measured, leading to a sample size of 52.

  • Care should be taken to count observations, not define them in terms of time frames (i.e., weeks).

Level of Measurement

• Both the independent and dependent variables are measured at a ratio level.

  • The levels of measurement should be categorized as nominal, ordinal, interval, or ratio. To understand this better, refer to Section One reading on Levels of Measurement.