Analyzing Bivariate Data
Student Summary
People often collect data in two variables to investigate possible associations between two numerical variables and use the connections that they find to predict more values of the variables.
Data analysis usually follows these steps:
- Collect data
- Organize and represent the data, then look for an association
- Identify any outliers and try to explain why these data points are exceptions to the trend that describes the association
- Find a function that fits the data well
Although computational systems can help with data analysis by graphing the data, finding a function that might fit the data, and using that function to make predictions, it is important to understand the process and think about what is happening. A computational system may find a function that does not make sense or use a line when the situation suggests that a different model would be more appropriate.
Visual / Anchor Chart
Standards
8.SP.1
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
8.SP.2
Understand that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
8.SP.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept.
8.SP.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept.