1. Curving Fitting with 6-9 Polynomial Functions Warm Up
Lesson Presentation
Lesson Quiz
Holt Algebra 2

2. Warm Up
1. For f(x) = x3 + 5, write the rule for g(x) = f(x – 1) – 2 and sketch its graph.
g(x) = (x – 1)3 + 3
2. Write a function that reflects f(x) = 2x3 + 1 across the x-axis and shifts it 3 units down.
h(x) = –2x3 – 4

3. Objectives
Use finite differences to determine the degree of a polynomial that will fit a given set of data.
Use technology to find polynomial models for a given set of data.

5. Example 1A: Using Finite Differences to Determine Degree
Use finite differences to determine the degree of the polynomial that best describes the data. x 4 6 8 10 12 14 y –2
4.3
8.3
10.5
11.4
11.5
The x-values increase by a constant 2. Find the differences of the y-values. y –2
4.3
8.3
10.5
11.4
11.5
First differences: Not constant
Second differences: –2.3 –1.8 –1.3 – Not constant
Third differences: Constant
The third differences are constant. A cubic polynomial best describes the data.

6. Example 1B: Using Finite Differences to Determine Degree
Use finite differences to determine the degree of the polynomial that best describes the data. x –6 –3 3 6 9 y –9 16 26 41 78
151
The x-values increase by a constant 3. Find the differences of the y-values. y –9 16 26 41 78
151
First differences: Not constant
Second differences: – Not constant
Third differences: Not constant
Fourth differences: –3 – Constant
The fourth differences are constant. A quartic polynomial best describes the data.

7. Check It Out! Example 1
Use finite differences to determine the degree of the polynomial that best describes the data. x 12 15 18 21 24 27 y 3 23 29 31 43
The x-values increase by a constant 3. Find the differences of the y-values. y 3 23 29 31 43
First differences: Not constant
Second differences: –14 – Not constant
Third differences: Constant
The third differences are constant. A cubic polynomial best describes the data.

8. Example 2: Using Finite Differences to Write a Function
The table below shows the population of a city from 1960 to Write a polynomial function for the data.
Year
1960
1970
1980
1990
2000
Population (thousands)
4,267
5,185
6,166
7,830
10,812
Step 1 Find the finite differences of the y-values.
First differences:
Second differences:
Third differences: Close

9. Example 2 Continued
Step 2 Determine the degree of the polynomial.
Because the third differences are relatively close, a cubic function should be a good model.
Step 3 Use the cubic regression feature on your calculator.
f(x) ≈ x3 – x x

10. Check It Out! Example 2
The table below shows the gas consumption of a compact car driven a constant distance at various speed. Write a polynomial function for the data.
Speed 25 30 35 40 45 50 55 60
Gas (gal)
23.8
25.2
25.4 27
30.6 37
Step 1 Find the finite differences of the y-values.
First differences: –
Second differences: –1 –
Third differences: Close

11. Check It Out! Example 2 Continued
Step 2 Determine the degree of the polynomial.
Because the third differences are relatively close, a cubic function should be a good model.
Step 3 Use the cubic regression feature on your calculator.
f(x) ≈ 0.001x3 – 0.113x x

12. Often, real-world data can be too irregular for you to use finite differences or find a polynomial function that fits perfectly. In these situations, you can use the regression feature of your graphing calculator. Remember that the closer the R2-value is to 1, the better the function fits the data.

13. Example 3: Curve Fitting Polynomial Models
The table below shows the opening value of a stock index on the first day of trading in various years. Use a polynomial model to estimate the value on the first day of trading in 2000.
Year
1994
1995
1996
1997
1998
1999
Price ($)
683
652
948
1306
863
901
Step 1 Choose the degree of the polynomial model.
Let x represent the number of years since Make a scatter plot of the data.
The function appears to be cubic or quartic. Use the regression feature to check the R2-values.
cubic: R2 ≈ quartic: R2 ≈
The quartic function is more appropriate choice.

14. Example 3 Continued
Step 2 Write the polynomial model. The data can be modeled by f(x) = 32.23x4 – x x2 – x
Step 3 Find the value of the model corresponding to 2000.
2000 is 6 years after Substitute 6 for x in the quartic model.
f(6) = 32.23(6)4 – (6) (6)2 – (6)
Based on the model, the opening value was about $ in 2000.

15. Check It Out! Example 3
The table below shows the opening value of a stock index on the first day of trading in various years. Use a polynomial model to estimate the value on the first day of trading in 1999.
Year
1994
1995
1996
2000
2003
2004
Price ($)
3754
3835
5117
11,497
8342
10,454
Step 1 Choose the degree of the polynomial model.
Let x represent the number of years since Make a scatter plot of the data.
The function appears to be cubic or quartic. Use the regression feature to check the R2-values.
cubic: R2 ≈ quartic: R2 ≈
The quartic function is more appropriate choice.

16. Check It Out! Example 3 Continued
Step 2 Write the polynomial model. The data can be modeled by f(x) = 19.09x4 – x x2 – x
Step 3 Find the value of the model corresponding to 1999.
1999 is 5 years after Substitute 5 for x in the quartic model.
f(5) = 19.09(5)4 – (5) (5)2 – (5)
Based on the model, the opening value was about $11, in 1999.

17. x 8 10 12 14 16 18 y 7.2 1.2 –8.3 –19.1 –29 –35.8 Lesson Quiz: Part I
1. Use finite differences to determine the degree of the polynomial that best describes the data. x 8 10 12 14 16 18 y
7.2
1.2
–8.3
–19.1
–29
–35.8
cubic

18. Lesson Quiz: Part II 2.
The table shows the opening value of a stock index on the first day of trading in various years. Write a polynomial model for the data and use the model to estimate the value on the first day of trading in 2002.
Year
1994
1996
1998
2000
2001
2004
Price ($)
2814
3603
5429
3962
4117
3840
f(x) = 7.08x4 – x x2 – x ; about $