drunkenfool said:1. Find the equation of a cubic relationship given that the graph has only two x intercepts, one at point (3,0) and the other at (-1,0). It is also known that the graph passes through the point (4,-10) and has a y-intercept of (0,-18)
2. Write down the exact co-ordinates of the two turning points of the equation found in question 1.
My calcultor gives the same result. If you are taking calculus, you can approach this problem by taking the dirivative of the function you found in part a), setting it equal to zero, and solving the quadratic equation for x. If you are not taking calculus, that will not mean anything to you and the calculator solution is probably what is expected of you.drunkenfool said:am i right on part b?
Lil`SciWizGirl said:Find The Equation Of A Cubic Relationship Given This Data?
Does this math Have to do with Point-Slope fomulas. Slope=Rise/Run.
Ifso,then I have the anser of how to solve it, for you. If, you want to know the anser , then just let me know.
I'm Hear to help in any way possable. "THANK YOU"
With many Smiles,
Lil`SciWizGirl.
A cubic relationship can be identified by plotting the data points on a graph and observing a curve that resembles the shape of a "cubic" or "cube" (a three-dimensional square). This curve will have a steep increase, followed by a gradual decrease or leveling off.
The general form of a cubic equation is y = ax^3 + bx^2 + cx + d, where a, b, c, and d are constants and x is the independent variable. This equation represents a curve with a degree of 3, which is characteristic of a cubic relationship.
To determine the equation of a cubic relationship, at least four data points are needed. This is because a cubic equation has four unknown constants (a, b, c, and d) that can be solved using four data points.
Yes, a cubic equation can be used to predict future data points, but the accuracy of the prediction may depend on the quality and quantity of the data used to create the equation.
To solve for the constants in a cubic equation, you can use a variety of methods such as substitution, elimination, or graphical methods. The most common method is to use a process called "least squares regression" which involves minimizing the sum of the squared differences between the data points and the curve created by the equation.