Final answer:
The best equation for the line of best fit is found using least-squares regression or the median-median line method and is generalized as y = mx + b, where m is the slope and b is the y-intercept. For a specific example with a slope of approximately 6.9 and a y-intercept of approximately -316.3, the equation would be y = 6.9x - 316.3.
Step-by-step explanation:
To find the best equation for the line of best fit for a data set, a statistically derived equation that minimizes the sum of the squares of the vertical deviations of the points from the line (least-squares regression line) is often used.
You can plot the data on a scatter plot, use a graphing calculator to find the equation, or calculate it manually using the median-median line method or other statistical formulas.
Once the scatter plot is made, you can visually draw a line that seems to best fit the data, calculate the slope (rate of change) from two convenient points, and find the y-intercept (the value of y when x is zero) where the line crosses the y-axis. The general equation of the line of best fit is y = mx + b, where m is the slope and b is the y-intercept.
In the context provided, the slope was found to be approximately 6.9, and the y-intercept was approximately -316.3, giving the equation y = 6.9x - 316.3. It should be noted that different methods or data points might yield slightly different equations.
For the given pinky finger length of 2.5 inches, we substitute x with 2.5 in the equation to predict the person's height. The predicted height would be y = 6.9(2.5) - 316.3.
"Your complete question is: What is the best equation for the line of best fit for the data set? "