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Answer :
Final answer:
This question explores how to model bivariate data using a linear equation, which involves identifying independent and dependent variables, creating a scatter plot, calculating the least-squares line and its equation, determining the correlation coefficient, and making predictions using the equation.
Explanation:
This question pertains to bivariate data and how to model it using a linear equation. The process involves several steps. You first need to decide which variable should be the independent variable and which should be the dependent variable. Based on this decision, you will then draw a scatter plot of the data.
Next, you'll want to calculate the least-squares line and determine its equation, typically in the form ý = a + bx. This line represents the 'best fit' for your data based on minimizing the sum of the squared residuals (the differences between observed and estimated values).
Once you have calculated this line, you'll add it to the scatter plot. Then, compute the correlation coefficient, which measures how strongly the two variables are related. Depending on its exact value, it may be significant or not.
Finally, you'll use your equation to predict a new value. For example, if your equation models the relationship between years of education and annual income, you might use it to predict the average income for someone with a certain number of years of education.
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