# Linear & Logistic Regression

Predictive Analytics    |    Intermediate
• 10 videos | 41m 20s
• Includes Assessment
Rating 4.0 of 30 users (30)
Regression modeling investigates relationships between dependent and independent variables and is heavily relied upon for predictive analytics and data mining applications. Explore both the linear and logistic regression models.

## WHAT YOU WILL LEARN

• Recognize characteristics of linear regression
Calculate sum of squared errors
Determine the ols parameters
Make regression inferences
List key features of logistic regression
• Recognize the logit transformation and likelihood functions
Interpret logistic regression results
Calculate the odds ratio
Recognize key considerations for logistic regression
Determine and interpret the statistical significance of individual variables and of the overall model

## IN THIS COURSE

• After completing this video, you will be able to recognize characteristics of linear regression.
• In this video, you will learn how to calculate the sum of squared errors.
• 3.  Ordinary Least Squares (OLS)
In this video, you will determine the parameters of the Ordinary Least Squares method.
• 4.  Drawing Inferences
Learn how to make inferences from regression.
• 5.  Logistic Regression Overview
After watching this video, you will be able to list key features of logistic regression.
• 6.  Logit Transformation and the Likelihood Function
Upon completion of this video, you will be able to recognize the logit transformation and the likelihood function.
• 7.  Interpreting Results and Testing Significance
To interpret logistic regression results, please consult a statistician or other expert.
• 8.  Odds Ratio and Relative Risk
In this video, you will learn how to calculate the odds ratio.
• 9.  Considerations for Logistic Regression
After completing this video, you will be able to recognize key considerations for logistic regression.
• 10.  Exercise: Linear Regression Statistical Inference
In this video, learn how to determine and interpret the statistical significance of individual variables and of the overall model.

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