Answer:
The cubic regression that fits the given set of points is [tex]y = 2\cdot x^{3}-2\cdot x^{2} -3\cdot x + 7[/tex].
Step-by-step explanation:
Let the cubic regression be represented by the following third order polynomial:
[tex]y = a\cdot x^{3}+b\cdot x^{2}+c\cdot x + d[/tex] (1)
Where:
[tex]x[/tex] - Independent variable.
[tex]y[/tex] - Dependent variable.
[tex]a,b,c,d[/tex] - Coefficients.
By Algebra, we can determine analytically the values of all coefficients by knowing four distinct points. If we know that [tex](x_{1}, y_{1}) = (1,4)[/tex], [tex](x_{2},y_{2}) = (2,9)[/tex], [tex](x_{3}, y_{3}) = (3, 34)[/tex] and [tex](x_{4}, y_{4}) = (-2, -11)[/tex], then the system of linear equations is:
[tex]a+b+c+d = 4[/tex] (1)
[tex]8\cdot a + 4\cdot b + 2\cdot c + d = 9[/tex] (2)
[tex]27\cdot a + 9\cdot b + 3\cdot c + d = 34[/tex] (3)
[tex]-8\cdot a + 4\cdot b -2\cdot c + d = -11[/tex] (4)
The solution of the system of linear equations is: [tex]a = 2[/tex], [tex]b = -2[/tex], [tex]c = -3[/tex], [tex]d = 7[/tex]
The cubic regression that fits the given set of points is [tex]y = 2\cdot x^{3}-2\cdot x^{2} -3\cdot x + 7[/tex].