Exploring Predictive Statistical Methods

What is the primary purpose of regression analysis?

• To determine the mode of a dataset.

• To identify relationships and make predictions.

• To calculate the mean of a dataset.

• To visualize data in a graph.

Explain the difference between simple and multiple regression.

• Simple regression requires a larger dataset; multiple regression can work with smaller datasets.

• Simple regression uses one independent variable; multiple regression uses multiple independent variables.

• Simple regression predicts a single outcome; multiple regression predicts multiple outcomes.

• Simple regression is used for categorical data; multiple regression is used for continuous data.

What does the coefficient of determination (R²) indicate in regression analysis?

• R² indicates the number of variables in the regression model.

• R² shows the strength of the relationship between independent variables.

• R² indicates the proportion of variance explained by the regression model.

• R² measures the accuracy of predictions made by the model.

In logistic regression, what is the outcome variable?

• The outcome variable is a time series.

• The outcome variable is always a percentage.

• The outcome variable is categorical, often binary.

• The outcome variable is continuous.

How does logistic regression differ from linear regression?

• Logistic regression is used for time series forecasting; linear regression is not.

• Logistic regression predicts probabilities for categorical outcomes; linear regression predicts continuous values.

• Logistic regression requires normally distributed data; linear regression does not.

• Linear regression can only handle binary outcomes; logistic regression can handle multiple outcomes.

What is the significance of the odds ratio in logistic regression?

• The odds ratio indicates the change in odds of the outcome for a one-unit increase in the predictor variable.

• The odds ratio is used to calculate the mean of the outcome variable.

• The odds ratio indicates the probability of the outcome occurring without any predictor variable.

• The odds ratio measures the average value of the predictor variable.

Define Bayesian statistics in your own words.

• Bayesian statistics is a method of statistical inference that updates the probability of a hypothesis as more evidence or information becomes available.

• Bayesian statistics is a technique that only applies to large datasets and ignores small samples.

• Bayesian statistics is a fixed approach that does not change with new information.

• Bayesian statistics is a method that only uses past data without considering new evidence.

What is a prior distribution in Bayesian statistics?

• A prior distribution is a method for hypothesis testing.

• A prior distribution is the final conclusion about a parameter.

• A prior distribution is the data collected from experiments.

• A prior distribution is the initial belief about a parameter in Bayesian statistics.

How does Bayesian inference differ from frequentist inference?

• Bayesian inference only uses data from the current experiment.

• Frequentist inference incorporates prior beliefs into its analysis.

• Bayesian inference does not allow for updating beliefs based on new data.

• Bayesian inference uses prior information and updates beliefs, whereas frequentist inference focuses on the data at hand without prior beliefs.

What are some common predictive modeling techniques?

• Logistic Regression

• Linear Regression, Decision Trees, Random Forests, Support Vector Machines, Neural Networks

• Principal Component Analysis

• K-Means Clustering