Final answer:
To find the linear model that best fits a set of data using regression, you create a scatter plot, calculate the least-squares regression line using a calculator, and then compare the resulting equation to the given options.
Step-by-step explanation:
To determine the linear model that best fits a given set of data using regression, you would typically follow these steps:
- Enter the data into a calculator and create a scatter plot to visualize the data points.
- Use the calculator's regression function to calculate the least-squares regression line, which is the line that minimizes the sum of the squares of the residuals (the differences between the observed values and the values predicted by the line).
- The equation you obtain from this is of the form ý = a + bx, where 'a' is the y-intercept and 'b' is the slope of the line. This equation represents the line of best fit.
- Compare this equation to the given options and choose the one that most closely matches your calculated equation.
- Optional steps may include calculating the correlation coefficient (r) to assess the strength and direction of the linear relationship, and finding the coefficient of determination (r-squared) to interpret the proportion of the variance in the dependent variable that is predictable from the independent variable.
Without the specific data points, we cannot calculate the exact line of best fit or determine which option (A, B, C, or D) is correct. However, this process will yield a linear equation with parameters rounded as requested.