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Introduction The fit of a linear function to a set of data can be assessed by analyzing residuals. A residual is the vertical distance between an observed.

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Presentation on theme: "Introduction The fit of a linear function to a set of data can be assessed by analyzing residuals. A residual is the vertical distance between an observed."— Presentation transcript:

1 Introduction The fit of a linear function to a set of data can be assessed by analyzing residuals. A residual is the vertical distance between an observed data value and an estimated data value on a line of best fit. Representing residuals on a residual plot provides a visual representation of the residuals for a set of data. A residual plot contains the points: (x, residual for x). A random residual plot, with both positive and negative residual values, indicates that the line is a good fit for the data. If the residual plot follows a pattern, such as a U- shape, the line is likely not a good fit for the data : Analyzing Residuals

2 Key Concepts A residual is the distance between an observed data point and an estimated data value on a line of best fit. For the observed data point (x, y) and the estimated data value on a line of best fit (x, y 0 ), the residual is y – y 0. A residual plot is a plot of each x-value and its corresponding residual. For the observed data point (x, y) and the estimated data value on a line of best fit (x, y 0 ), the point on a residual plot is (x, y – y 0 ) : Analyzing Residuals

3 Key Concepts, continued A residual plot with a random pattern indicates that the line of best fit is a good approximation for the data. A residual plot with a U-shape indicates that the line of best fit is not a good approximation for the data : Analyzing Residuals

4 Common Errors/Misconceptions incorrectly finding the residual incorrectly plotting points on the residual plot : Analyzing Residuals

5 Guided Practice Example 1 Pablo’s science class is growing plants. He recorded the height of his plant each day for 10 days. The plant’s height, in centimeters, over that time is listed in the table to the right : Analyzing Residuals DayHeight in centimeters

6 Guided Practice: Example 1, continued Pablo determines that the function y = 1.73x is a good fit for the data. How close is his estimate to the actual data? Approximately how much does the plant grow each day? : Analyzing Residuals

7 Guided Practice: Example 1, continued 1.Create a scatter plot of the data. Let the x-axis represent days and the y-axis represent height in centimeters : Analyzing Residuals Height Days

8 Guided Practice: Example 1, continued 2.Draw the line of best fit through two of the data points. A good line of best fit will have some points below the line and some above the line. Use the graph to initially determine if the function is a good fit for the data : Analyzing Residuals

9 Guided Practice: Example 1, continued : Analyzing Residuals Height Days

10 Guided Practice: Example 1, continued 3.Find the residuals for each data point. The residual for each data point is the difference between the observed value and the estimated value using a line of best fit. Evaluate the equation of the line at each value of x : Analyzing Residuals

11 Guided Practice: Example 1, continued : Analyzing Residuals xy = 1.73x y = 1.73(1) = 3.6 2y = 1.73(2) = y = 1.73(3) = y = 1.73(4) = y = 1.73(5) = y = 1.73(6) = y = 1.73(7) = y = 1.73(8) = y = 1.73(9) = y = 1.73(10) = 19.17

12 Guided Practice: Example 1, continued Next, find the difference between each observed value and each calculated value for each value of x : Analyzing Residuals xResidual 1 3 – 3.6 = – – 5.33 = – – 7.06 = – 8.79 = – = – – = – = – = – = – – = –0.27

13 Guided Practice: Example 1, continued 4.Plot the residuals on a residual plot. Plot the points (x, residual for x) : Analyzing Residuals

14 Guided Practice: Example 1, continued 5.Describe the fit of the line based on the shape of the residual plot. The plot of the residuals appears to be random, with some negative and some positive values. This indicates that the line is a good line of fit : Analyzing Residuals

15 Guided Practice: Example 1, continued 6.Use the equation to estimate the centimeters grown each day. The change in the height per day is the centimeters grown each day. In the equation of the line, the slope is the change in height per day. The plant is growing approximately 1.73 centimeters each day : Analyzing Residuals ✔

16 Guided Practice: Example 1, continued : Analyzing Residuals

17 Guided Practice Example 2 Lindsay created the table to the right showing the population of fruit flies over the last 10 weeks : Analyzing Residuals Week Number of flies

18 Guided Practice: Example 2, continued She estimates that the population of fruit flies can be represented by the equation y = 46x – 40. Using residuals, determine if her representation is a good estimate : Analyzing Residuals

19 Guided Practice: Example 2, continued 1.Find the estimated population at each x-value. Evaluate the equation at each value of x : Analyzing Residuals xy = 46x – (1) – 40 = 6 246(2) – 40 = (3) – 40 = (4) – 40 = (5) – 40 = (6) – 40 = (7) – 40 = (8) – 40 = (9) – 40 = (10) – 40 = 420

20 Guided Practice: Example 2, continued 2.Find the residuals by finding each difference between the observed population and estimated population : Analyzing Residuals xResidual 150 – 6 = – 52 = – 98 = – 144 = – – 190 = – – 236 = – – 282 = – – 328 = – – 374 = – – 420 = 46

21 Guided Practice: Example 2, continued 3.Create a residual plot. Plot the points (x, residual for x) : Analyzing Residuals

22 Guided Practice: Example 2, continued 4.Analyze the residual plot to determine if the equation is a good estimate for the population. The residual plot has a U-shape. This indicates that a non-linear estimation would be a better fit for this data set. The shape of the residual plot indicates that the equation y = 46x – 40 is not a good estimate for this data set : Analyzing Residuals ✔

23 Guided Practice: Example 2, continued : Analyzing Residuals


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