Section outline

  • Topic 5

    Learning outcomes: Appreciate the importance of quantifying the strength of the linear relationship between two variables. Understand how a linear regression model can be used to predict the values of a dependent variable from one or more independent variables.

    Know when it is appropriate to calculate a correlation coefficient and how to interpret it. Understand the role of the slope, intercept, residual, and predicted value in a linear regression model. Know how to determine how well a regression model fits the observed data.

    • Reflection on Biostatistics Topic 5 Forum
      Students must
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      The reading for this discussion topic is pp. 133-168 in the e-book. 

      Question:

      Much research focuses on the relationships between variables, often with one of the variables considered a dependent variable and the others  independent (predictor) variable.

      • Give an example in your field of a dependent variable and a set of predictor variables that may be related to the dependent variable. For example, you may want to know if ITN use is related to age, gender, education and marital status. 
      • Do you think your predictor variables are related to each other? If so, briefly describe how you think the variables are related.
      • Do you think the relationship between pairs of predictors is linear?   
      • What disadvantages do you see to examining the relationship of each of the predictor variables with the dependent variable, ignoring the other predictor variables?