Launch this Learning Path to accelerate your analytics education journey with the Minitab Education Hub.
Up to 78% of data goes unused within an organization. Bring more science to your decisions by extracting predictive insights from your data. Everyone in an organization is empowered when they can find and share actionable insights to make data driven decisions. This learning path expands your analytics skillset by analyzing patterns found in historical data to gain better insights, identify potential risks, seek out improvement opportunities, and make predictions about the future. You will explore supervised machine learning techniques for discovering relationships for both continuous and categorical key performance indicators. Specifically, discover the fundamentals of important visualization and statistical concepts such as scatterplots, correlation, regression, binary logistic regression, CART® classification, CART® regression, Random Forests® Classification, and TreeNet® Regression. All learning is presented using animated lessons, quizzes, and hands-on exercises to help you easily learn statistics and how to use Minitab Statistical Software.
- Identify and characterize relationships between variables and use the relationships to predict the outcomes of business decisions.
- Use scatterplots and correlation to visualize and quantify the strength and nature or relationships between numeric variables.
- Use regression to define linear relationships between numeric variables mathematically, producing equations to predict one value from another.
- Use a matrix plot to visualize the nature of relationships between numeric variables.
- Use correlation to quantify the strength of linear relationships.
- Use multiple linear regression to model a continuous response variable.
- Learn how to assess model accuracy and check model assumptions.
- Use stepwise and best subsets regression to select a model.
- Use binary logistic regression to model a categorical response variable.
- Learn what is meant by predictive analytics.
- Understand the fundamentals of machine learning.
- Learn how to use validation to assess models.
- Understand the basic concepts of regression trees.
- Use a CART classification tree to model a categorical response or target variable.
- Use a CART regression tree to model a continuous response or target variable.
- Learn what is meant by a bootstrap sample.
- Understand out-of-bag validation for Random Forests.
- Use Random Forests Classification to model a categorical response.
- Understand the basic concepts of TreeNet models.
- Use TreeNet Regression to model a continuous response or target variable.
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