Understanding Regression Techniques

What is one key assumption of linear regression?

• Independence of independent variables.

• Normal distribution of the dependent variable.

• Homogeneity of variance across all observations.

• Linearity of the relationship between independent and dependent variables.

How do you interpret a positive coefficient in a regression model?

• A positive coefficient indicates no relationship between variables.

• A positive coefficient suggests a direct relationship where an increase in the predictor variable leads to an increase in the response variable.

• A positive coefficient means the predictor variable decreases the response variable.

• A positive coefficient suggests a negative correlation between the variables.

What metric would you use to evaluate the goodness of fit for a regression model?

• R-squared

• Adjusted R-squared

• Root Mean Squared Error

• Mean Absolute Error

What is overfitting in the context of regression analysis?

• Overfitting occurs when a model is too simple and fails to capture the trend.

• Overfitting in regression analysis is when a model is too complex and captures noise instead of the underlying trend.

• Overfitting happens when the model is trained on too few data points.

• Overfitting is when a model perfectly predicts the training data without any errors.

What is the main purpose of logistic regression?

• To analyze time series data.

• To predict continuous outcomes.

• To model the probability of a binary outcome.

• To classify data into multiple categories.

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.

• Logistic regression can only handle binary outcomes; linear regression can handle multiple categories.

What is multicollinearity and why is it a concern in regression?

• Multicollinearity is a concern in regression because it can lead to unreliable estimates and difficulties in interpreting the effects of predictors.

• Multicollinearity is a method to increase the number of predictors in a model.

• It refers to the correlation between the dependent variable and the predictors.

• Multicollinearity improves the accuracy of regression estimates.

What does the R-squared value indicate in a regression model?

• The R-squared value measures the accuracy of predictions.

• The R-squared value indicates the proportion of variance explained by the model.

• The R-squared value shows the correlation between the dependent and independent variables.

• The R-squared value indicates the number of variables in the model.

What is underfitting and how can it be identified?

• Underfitting is when a model is too simplistic to capture the data patterns, identified by high errors on both training and validation datasets.

• Underfitting occurs when a model is overly complex and captures noise in the data.

• It can be identified by low errors on the training dataset but high errors on the validation dataset.

• Underfitting is when a model perfectly fits the training data and generalizes well to new data.

In logistic regression, what does the odds ratio represent?

• The odds ratio is a measure of the correlation between two predictor variables.

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

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

• The odds ratio measures the total number of outcomes in a dataset regardless of predictor variables.