Texts: Respond to these questions in about 200 words. ZyBooks, a Wiley brand - QNT/375T: Business Data Analytics. 5.1 Introduction to Linear Regression 5.2 Least Squares Method 5.3 Linear regression assumptions 5.4 Correlation and Coefficient of Determination 5.5 Interpreting fitted models
Scenario: A real estate agent believes she can determine a home's value (response variable) in a community by knowing the home's size (square feet) (predictor variable).
A. Could the agent use a simple linear regression in this case?
B. What other predictor variables would provide a better result? Explain.
C. What are the differences between the response and predictor variables?
D. Please briefly provide an example where you could use linear regression in your workplace, community, or home situation. Please explain in detail.
In this scenario, the real estate agent wants to determine a home's value in a community based on its size in square feet. To accomplish this, the agent can use simple linear regression.
A. Yes, the agent can use simple linear regression in this case. Simple linear regression is appropriate when there is a linear relationship between the predictor variable (home's size) and the response variable (home's value). By fitting a line to the data points, the agent can estimate the relationship between the two variables and make predictions.
B. Other predictor variables that could provide a better result in determining a home's value include the number of bedrooms, number of bathrooms, location, age of the house, and amenities. These variables can provide additional information that may influence the home's value. For example, a larger number of bedrooms and bathrooms, a desirable location, and modern amenities can increase the value of a home.
C. The response variable is the variable of interest, which in this case is the home's value. The predictor variable is the variable used to predict or explain the response variable, which in this case is the home's size. The response variable is dependent on the predictor variable, meaning that changes in the predictor variable can affect the response variable.
D. An example where linear regression can be used is in a workplace to analyze the relationship between employee productivity (response variable) and the number of hours worked (predictor variable). By collecting data on employee productivity and the number of hours worked, a linear regression model can be built to understand how changes in the number of hours worked impact employee productivity. This information can be used to optimize work schedules and improve overall productivity.