To best predict the length of night sleep using other variables related to a toddler's sleep, which data mining method is appropriate?

To best predict the length of night sleep using other variables related to a toddler's sleep, which data mining method is appropriate?

• A. Logistic Regression
• B. Decision Trees
• C. Linear Regression
• D. Cluster Analysis

Answer: (C)

Linear regression is the appropriate method for predicting the length of night sleep based on other related variables because it is specifically designed for modeling and predicting continuous numerical outcomes. In this scenario, the length of night sleep is a continuous variable, allowing linear regression to effectively establish a relationship between the independent variables (other factors affecting sleep) and the dependent variable (length of night sleep). This method analyzes how changes in independent variables impact the dependent variable, providing coefficients that quantify these relationships. If the goal is to understand how several factors jointly influence the amount of sleep a toddler gets and to make predictions based on this understanding, linear regression is well-suited for this purpose. Other methods mentioned have different primary functions: logistic regression is intended for binary outcomes, decision trees can be used for both classification and regression but may not provide as straightforward a relationship as linear regression does for continuous outcomes, and cluster analysis is focused on grouping data points rather than making predictions about a variable's value. Thus, for predicting a continuous outcome like sleep duration, linear regression is the most appropriate choice.

Linear regression is the appropriate method for predicting the length of night sleep based on other related variables because it is specifically designed for modeling and predicting continuous numerical outcomes. In this scenario, the length of night sleep is a continuous variable, allowing linear regression to effectively establish a relationship between the independent variables (other factors affecting sleep) and the dependent variable (length of night sleep).

This method analyzes how changes in independent variables impact the dependent variable, providing coefficients that quantify these relationships. If the goal is to understand how several factors jointly influence the amount of sleep a toddler gets and to make predictions based on this understanding, linear regression is well-suited for this purpose.

Other methods mentioned have different primary functions: logistic regression is intended for binary outcomes, decision trees can be used for both classification and regression but may not provide as straightforward a relationship as linear regression does for continuous outcomes, and cluster analysis is focused on grouping data points rather than making predictions about a variable's value. Thus, for predicting a continuous outcome like sleep duration, linear regression is the most appropriate choice.