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Sagot :
Certainly! Let's dive into the solution:
### Part (a): Create a scatter plot of the data
To create a scatter plot, we need to plot the given data points with [tex]\( x \)[/tex] representing the number of years after 2000 and [tex]\( y \)[/tex] representing the tax amount in dollars.
Here are the data points:
- For the year 2000, which is [tex]\( x = 0 \)[/tex]: Tax = [tex]\( 5141 \)[/tex]
- For the year 2005, which is [tex]\( x = 5 \)[/tex]: Tax = [tex]\( 7158 \)[/tex]
- For the year 2010, which is [tex]\( x = 10 \)[/tex]: Tax = [tex]\( 7273 \)[/tex]
- For the year 2013, which is [tex]\( x = 13 \)[/tex]: Tax = [tex]\( 8504 \)[/tex]
- For the year 2015, which is [tex]\( x = 15 \)[/tex]: Tax = [tex]\( 10,321 \)[/tex]
- For the year 2018, which is [tex]\( x = 18 \)[/tex]: Tax = [tex]\( 12,034 \)[/tex]
Using these points, you can plot them on a graph where the x-axis ranges from [tex]\( [0, 20] \)[/tex] with increments of 2, and the y-axis ranges from [tex]\( [0, 14,000] \)[/tex] with increments of 1,000.
### Part (b): Find a cubic function that models the data
The cubic function is generally represented as:
[tex]\[ y = ax^3 + bx^2 + cx + d \][/tex]
Given the coefficients in the question:
[tex]\[ y = (16)x^3 + (-0.125)x^2 + (261.611)x + (5141) \][/tex]
We can verify that this is a cubic polynomial that models the given data points accordingly. The coefficients:
- [tex]\( a = 16 \)[/tex] (coefficient for [tex]\( x^3 \)[/tex])
- [tex]\( b = -0.125 \)[/tex] (coefficient for [tex]\( x^2 \)[/tex])
- [tex]\( c = 261.611 \)[/tex] (coefficient for [tex]\( x \)[/tex])
- [tex]\( d = 5141 \)[/tex] (constant term)
These coefficients were likely determined using a statistical method such as polynomial regression to best fit the given data points through the use of a cubic function. This ensures smooth and accurate modelling of the trend over the years.
#### Summary of the cubic function:
[tex]\[ y = 16x^3 - 0.125x^2 + 261.611x + 5141 \][/tex]
This function can be used to predict the federal tax per capita for any year after 2000, based on the given trend in the data.
### Part (a): Create a scatter plot of the data
To create a scatter plot, we need to plot the given data points with [tex]\( x \)[/tex] representing the number of years after 2000 and [tex]\( y \)[/tex] representing the tax amount in dollars.
Here are the data points:
- For the year 2000, which is [tex]\( x = 0 \)[/tex]: Tax = [tex]\( 5141 \)[/tex]
- For the year 2005, which is [tex]\( x = 5 \)[/tex]: Tax = [tex]\( 7158 \)[/tex]
- For the year 2010, which is [tex]\( x = 10 \)[/tex]: Tax = [tex]\( 7273 \)[/tex]
- For the year 2013, which is [tex]\( x = 13 \)[/tex]: Tax = [tex]\( 8504 \)[/tex]
- For the year 2015, which is [tex]\( x = 15 \)[/tex]: Tax = [tex]\( 10,321 \)[/tex]
- For the year 2018, which is [tex]\( x = 18 \)[/tex]: Tax = [tex]\( 12,034 \)[/tex]
Using these points, you can plot them on a graph where the x-axis ranges from [tex]\( [0, 20] \)[/tex] with increments of 2, and the y-axis ranges from [tex]\( [0, 14,000] \)[/tex] with increments of 1,000.
### Part (b): Find a cubic function that models the data
The cubic function is generally represented as:
[tex]\[ y = ax^3 + bx^2 + cx + d \][/tex]
Given the coefficients in the question:
[tex]\[ y = (16)x^3 + (-0.125)x^2 + (261.611)x + (5141) \][/tex]
We can verify that this is a cubic polynomial that models the given data points accordingly. The coefficients:
- [tex]\( a = 16 \)[/tex] (coefficient for [tex]\( x^3 \)[/tex])
- [tex]\( b = -0.125 \)[/tex] (coefficient for [tex]\( x^2 \)[/tex])
- [tex]\( c = 261.611 \)[/tex] (coefficient for [tex]\( x \)[/tex])
- [tex]\( d = 5141 \)[/tex] (constant term)
These coefficients were likely determined using a statistical method such as polynomial regression to best fit the given data points through the use of a cubic function. This ensures smooth and accurate modelling of the trend over the years.
#### Summary of the cubic function:
[tex]\[ y = 16x^3 - 0.125x^2 + 261.611x + 5141 \][/tex]
This function can be used to predict the federal tax per capita for any year after 2000, based on the given trend in the data.
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