Regressions without calculator

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Discussion Overview

The discussion centers around performing regression analysis without the use of a calculator, specifically focusing on linear, quadratic, cubic, and quartic regressions. Participants explore methods for deriving regression equations from multiple data points and higher-degree polynomials.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant seeks guidance on performing regressions without a calculator, indicating familiarity with linear equations but uncertainty about higher-degree polynomials.
  • Another participant introduces the Least Squares method as a common approach, mentioning the need for algebraic work to minimize the difference between the function and given points.
  • A further contribution details a matrix approach for simple regression, defining the model parameters and how to compute them using matrix operations.
  • Additionally, a participant provides explicit formulas for calculating the slope and y-intercept of a linear regression, relating them to the means of the data points.

Areas of Agreement / Disagreement

Participants present various methods for regression analysis, but there is no consensus on a single approach or resolution of the discussion. Multiple techniques are suggested, indicating a diversity of perspectives on the topic.

Contextual Notes

The discussion includes assumptions about the familiarity with algebra and regression concepts, and it does not resolve the complexities involved in applying these methods to different types of data sets.

Who May Find This Useful

Students and individuals interested in learning about regression analysis techniques, particularly those looking to understand the mathematical foundations without relying on calculators.

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im a senior in high school taking ap calculus, I've done regressions here and there with the calculator but i was wondering how do do it without the calculator, i obviously know how to get a linear equation out of 2 points, but how is it done with more points and higher degrees?

i understand that you need 2 points for a line, 3 for a quadradic, 4 for a cubic and 5 for a quartic but i was hoping someone could show me how it is done.:confused:
 
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The simplest method i know is the Least Squares, there are many others.

I can show you the general form for any number of points and any type of equation, but it takes a little hard work and algebra.

In short therms, you have to find the parameters that minimize the difference [tex]\sum [f(x)-q(x)]^2[/tex], where [tex]q(x)[/tex] is the function you want to minimize, and [tex]f(x)[/tex] are the points you are given.

I'll try to post something more elaborate this week.
 
Let the model be y = a + b x + u. Parameters of the model are a and b, u is the error term.

Variables y, x (and u) are each N-by-1 vectors.

Let X = [1 x] be the N-by-2 matrix. The first column of X is a vector of 1's. The second column of X is identical to vector x.

Let Z be the inverse of X'X. Z is a 2-by-2 matrix.

Then we can write [a b]' = ZX'y, which is 2-by-1. Parameter a is the first (top) element of ZX'y. Parameter b is the second element of ZX'y.

For higher order polynomials, substitute [x x^2 x^3 ...] for x, and [b1 b2 b3 ...] for b.
 
Of course, for simple regression, the matrix approach mentioned above is equivalent to the following equations:
The slope is given by

[tex] b = \frac{\sum{(x-\bar x)(y - \bar y)}}{\sum (x-\bar x)^2}[/tex]

and the y-intercept is given by

[tex] a = \bar y - b \bar x[/tex]
 

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