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------------------------------------------------ Search for bivariate data (in a magazine or on the internet) that can be closely modeled by a linear equation.

a. Draw a scatter diagram of the data.

b. Find the equation of the least squares line and the linear correlation coefficient for the data.

c. Graph the least squares line on the scatter diagram from part a.

d. Use the equation of the least squares line to predict a range value for a specific domain value.

To examine bivariate data linearly, select independent and dependent variables, create a scatter plot, find the least-squares regression line with a calculator, examine the variables' relation and significance, and use the line equation for prediction.

Explanation:

Understanding Bivariate Data Analysis

To analyze bivariate data that can be closely modelled by a linear equation, the steps you would follow include:

Decide which variable should be the independent variable and which should be the dependent variable.
Draw a scatter plot of the data, with the independent variable on the x-axis and the dependent variable on the y-axis.
Use a calculator's regression function to find the equation of the least-squares regression line and graph it on the scatter plot.
Examine the relationship between the variables and the significance of the correlation coefficient.
Use the least-squares line equation to predict values and interpret the results.

The least-squares line equation is usually denoted as ý = a + bx, where 'a' is the y-intercept, 'b' is the slope, and ý represents the predicted value of the dependent variable for a given x-value of the independent variable.

For example, if we want to predict future sales based on advertising spend, we could use the number of sales as the dependent variable and the amount spent on advertising as the independent variable. After plotting the data and calculating the least-squares line, we might find an equation like ý = 10 + 2x, which would imply that for every additional dollar spent on advertising, we predict two more sales, on average.

Learn more about Bivariate Data Analysis here:

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