Bivariate measures

Bivariate measures

1. Bivariate measures are statistical techniques used to analyze the relationship between two quantitative variables

   • These measures focus on associations between two variables
     • Are used to determine whether there is a relationship and what type of relationship exists between them

2. Linear regression

   • Focuses on finding a line that best fits the data
   • Allows for predicting the values of the dependent variable from the values of the independent variable.
   • Simple Linear Regression
     • An independent variable to make the prediction
   • Multiple Linear Regression
     • Use multiple independent variables that contribute to the dependent variable.

3. Correlation coefficient

   • Statistical measure that indicates the strength
   • Direction of the relationship between two quantitative variables
   • This coefficient is symbolized by r.
   • The correlation coefficient r is a unitless value between -1 and 1
   • The closer r approaches zero, the weaker the linear relationship.
   • Positive r values indicate a positive correlation
     • In which the values of both variables tend to increase together.
   • Negative r values indicate a negative correlation
     • In which the values of one variable tend to increase while the values of the other variable decrease.
   • The values 1 and -1 represent a positive and negative "perfect" correlation

4. Scatter plot

   • Graphical representation
     • Where each point on the graph represents
       • An observation of both variables.
     • Of the values of two quantitative variables

5. Coefficient of Determination

   • Statistical measure
     • indicates the proportion of the variation
     • In the dependent variable that can be explained by the independent variable
     • In a linear regression model, ranging from 0 to 1.

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