Cubic Regressions
Good Fit Curve with Regression
$1.99
1.0for iPhone, iPad
Age Rating
Cubic Regressions Screenshots
About Cubic Regressions
There are two Cubic regression calculators:
1. The standard Cubic regression calculator.
2. The Cubic regression calculator with predicted value.
They cover the Cubic regression equation, the Quadratic and Linear regression equations(for reference only).
Cubic regression equation function has the form:
y = a + bx + cx² + dx³
where a, b, c, d are called the coefficient of the cubic regression model.
If d = 0, y = a + bx + cx², it will have the dataset with U shape pattern.
If c = 0 and d = 0, y = a + bx , it will have the dataset with linear shape pattern.
If the dataset does not fall into the perfect linear or the parabolic pattern, it will be more appropriate to use the Cubic regression equation.
It is useful in various fields, such as:
finance, physics, engineering, and social sciences, where there have nonlinear relationships between variables.
Note: we need to stick the value of predicted value of x lies within the minimum and maximum values of array of x data.
Together with the linear and quadratic regression equations, it is a great tool to visualize the whole picture of the dataset analysis.
Highlighted features are:
1. A short learning curve.
2. Showing all equations with options for comparison.
3. Data will be rearranged in ascending order internally.
4. An unique data entry method with error detection.
5. A well formatted chart with options for different selections.
6. Results can be emailed.
7. It can handle 40 sets of data.
1. The standard Cubic regression calculator.
2. The Cubic regression calculator with predicted value.
They cover the Cubic regression equation, the Quadratic and Linear regression equations(for reference only).
Cubic regression equation function has the form:
y = a + bx + cx² + dx³
where a, b, c, d are called the coefficient of the cubic regression model.
If d = 0, y = a + bx + cx², it will have the dataset with U shape pattern.
If c = 0 and d = 0, y = a + bx , it will have the dataset with linear shape pattern.
If the dataset does not fall into the perfect linear or the parabolic pattern, it will be more appropriate to use the Cubic regression equation.
It is useful in various fields, such as:
finance, physics, engineering, and social sciences, where there have nonlinear relationships between variables.
Note: we need to stick the value of predicted value of x lies within the minimum and maximum values of array of x data.
Together with the linear and quadratic regression equations, it is a great tool to visualize the whole picture of the dataset analysis.
Highlighted features are:
1. A short learning curve.
2. Showing all equations with options for comparison.
3. Data will be rearranged in ascending order internally.
4. An unique data entry method with error detection.
5. A well formatted chart with options for different selections.
6. Results can be emailed.
7. It can handle 40 sets of data.
Show More
What's New in the Latest Version 1.0
Last updated on Jul 3, 2023
Version History
1.0
Jul 3, 2023
Cubic Regressions FAQ
Click here to learn how to download Cubic Regressions in restricted country or region.
Check the following list to see the minimum requirements of Cubic Regressions.
iPhone
Requires iOS 16.0 or later.
iPad
Requires iPadOS 16.0 or later.
Cubic Regressions supports English
