You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic…

Question

Options

• ALinear regression
• BLogistic regression
• CDecision trees
• DTF-IDF

• A
  76% (32)
• B
  14% (6)
• C
  2% (1)
• D
  7% (3)

Explanation

A data model explicitly describes a relationship between predictor and response variables. Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation. If you need to fit data with a nonlinear model, transform the variables to make the relationship linear. Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.

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