List three variables (X1, X2, X3) you’d include in a Multiple Regression Model in order to better predict an outcome (Y) variable.

  1. List three variables (X1, X2, X3) you’d include in a Multiple Regression Model in order to better predict an outcome (Y) variable. For example, you might list three variables that could be related to how long a person will live (Y). Or you might list three variables that contribute to a successful restaurant. Your Regression Model should have three variables that will act as “predictors” (X1, X2, X3) of a “criterion” (Y’). Note that the outcome or criterion variable (e.g. how long a person would live, or the success/profit made by a restaurant measured) in must be a “Measurement” variable, that is something that is measured on a scale like inches, pounds, IQ, lifespan, stock value, etc. But that the predictors (X variables) can be either a measurement variable OR a categorical variable such as gender, political party, location, etc.

2. Go to another student’s post, and for each predictor variable (X1, X2, X3) describe what the expected direction of the relationship between each X and the criterion or Y variable would be. For example if someone used gender as a predictor (X1) of longevity (Y’), would you expect women or men to live longer? Make a directional prediction (positive or negative) for each of their three predictor variables and state the rationale for each of your predictions.2. Post a question about this or a prior week’s statistics post data for a correlation problem for another student to solve. Then answer another student’s question and correlation problem. Suggestion: your questions may be about the concepts and exercises found on online stat book referenced in Week 3. (Be sure to reference the site in your questions when you post them.)

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