Understanding Regression Analysis

What is the main purpose of regression analysis?

• To determine the cause of a variable

• To predict or infer a variable based on others

• To classify data into categories

• To measure the exact value of a variable

In regression analysis, what is the term for the variable you want to predict?

• Control variable

• Predictor

• Dependent variable

• Independent variable

Which of the following is an example of using regression analysis for prediction?

• Analyzing the effect of diet on health

• Determining the influence of education on salary

• Predicting hospital stay duration based on patient characteristics

• Studying the impact of weather on crop yield

What distinguishes simple linear regression from multiple linear regression?

• Simple linear requires more data than multiple linear

• Simple linear is more accurate than multiple linear

• Simple linear uses one independent variable, multiple linear uses several

• Simple linear is for categorical data, multiple linear is for metric data

When is logistic regression used?

• When the data is normally distributed

• When there are more than three independent variables

• When the dependent variable is categorical

• When the dependent variable is metric

What type of variable is used in logistic regression?

• Categorical

• Continuous

• Metric

• Ordinal

What is a dummy variable?

• A variable that is not used in the analysis

• A variable used to fill missing data

• A variable that is always zero

• A variable that represents categorical data with more than two characteristics

Which of the following is a metric variable?

• Marital status

• Age

• Favorite color

• Gender

What is the first step in performing a regression analysis online using datadep.net?

• Download the results

• Select the type of regression

• Choose the dependent and independent variables

• Enter the data manually

What should you do if your independent variables have more than two characteristics?

• Ignore them

• Create dummy variables

• Use them as they are

• Convert them to metric variables