To provide you with an approximate line of best fit based on your description of the scatter plot, I would need the key points or additional data about the dot positions. However, I can guide you on how to derive your answer based on a standard approach to finding a line of best fit:
(a) Write an approximate equation of the line of best fit.
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Identify two points on the scatter plot: Use the coordinates of two points that you can clearly see, ideally where the data points are spread out.
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Calculate the slope (m) of the line of best fit using the formula:
\[ m = \frac{y_2 - y_1}{x_2 - x_1} \] where \((x_1, y_1)\) and \((x_2, y_2)\) are coordinates of the two points. -
Use one of the points (x, y) to calculate the y-intercept (b) by rearranging the linear equation \(y = mx + b\): \[ b = y - mx \]
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Write your equation: The equation will be in the form of \(y = mx + b\). Round \(m\) and \(b\) to the nearest hundredth.
(b) Using your equation, predict the amount charged per hour by a dog sitter with 10 years of experience.
- Plug \(X = 10\) (years of experience) into your equation \(y = mx + b\).
- Solve for \(y\) to find the predicted amount charged.
If you can provide the exact coordinates from the graph, I can help you with calculations. Otherwise, this guide should allow you to approximate the equation and make the prediction based on the scatter plot.
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