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Mathematic definitions and Formulas

António Rafael C. Paiva


Date: December 13, 2004


Contents


Arithmetic progression

Definition:

$\displaystyle u_n = u_1 + (n-1) \times r $

Sum of $ n$ elements of the progression:

$\displaystyle S_n = \frac{u_1+u_n}{2} \times n $


Geometric progression

Definition:

$\displaystyle u_n = u_1 \times r^{n-1} $

Sum of $ n$ elements of the progression:

$\displaystyle S_n = u_1 \times \frac{1-r^n}{1-r} $

Sum of all the elements:

$\displaystyle S = \lim_{n\rightarrow\infty} S_n = \frac{u_1}{1-r},\ $   $\displaystyle \mbox{if $\vert r\vert<1$}$$\displaystyle $


Counting formulas (for probabilities)

Factorial:

$\displaystyle n!= n(n-1)(n-2)\cdots1 $

Ordered combinations without repetitions:

$\displaystyle {}^nA_p = \frac{n!}{(n-p)!} $

Ordered combinations with repetitions:

$\displaystyle {}^n{A'}_p = n^p $

Unordered combinations without repetitions:

$\displaystyle {}^nC_p = \frac{n!}{p!(n-p)!} $

Some properties of unordered combinations without repetitions:


Probabilities

Some properties:

$\displaystyle p(\emptyset) = 0 $

$\displaystyle p(\overline{A}) = 1 - p(A) $

$\displaystyle p(A \cup B) = p(A) + p(B) - p(A \cap B) $

If two events are incompatibles or dijoint:

$\displaystyle p(A \cap B) = 0 $

If two events are independent:

$\displaystyle p(A \cap B) = p(A)p(B) $

Conditional probability:

$\displaystyle p(B/A) = \frac{p(A \cap B)}{p(A)} $

Binomial probability law:

$\displaystyle P = C_k^n p^k (1-p)^{n-k} $


Newton expansion

General formula:

$\displaystyle (a+b)^n = {}^nC_0 a^n + {}^nC_1 a^{n-1} b + {}^nC_2 a^{n-1} b^2 + %
\cdots + {}^nC_{n-1} a b^{n-1} + {}^nC_n b^n $

Some useful formulas from the particular case, $ n = 2$:

$\displaystyle (a+b)^2 = a^2 + 2ab + b^2 $

$\displaystyle (a-b)^2 = a^2 - 2ab + b^2 $

$\displaystyle (a+b)(a-b) = a^2 - b^2 $


Limits

Definition:

$\displaystyle \exists \lim_{x \rightarrow a} f(x) = b \quad \Leftrightarrow \quad
\lim_{x \rightarrow a^-} f(x) = \lim_{x \rightarrow a^+} f(x) = b $

Some trigonometric limits:

The Neper number related limits:

and the following theorems


Continuity

A function $ f(x)$ is said to be continous at some point $ a$ iff:

$\displaystyle \exists \lim_{x \rightarrow a} f(x) = f(a) $


Derivates

Definition:

$\displaystyle f'(x_0) = \lim_{x \rightarrow x_0} \frac{f(x) - f(x_0)}{x - x_0},\quad x \in D $

where $ f'(x_0)$ is the slope of a line tangent to function $ f$, at $ x_0$.

A function is said to have derivable at a point $ x_0$ if it has finite derivate at that point, which happens if the side derivates are equal. In that case

$\displaystyle f'(x_0^-) = f'(x_0^+) = f'(x_0). $

Properties of the derivatives:

Derivates of some trigonometric functions:


Trigonometrics

The fundamental equation of trigonometrics:

$\displaystyle \sin^2(x) + \cos^2(x) = 1 $

Sine and cosine as linear combinations of complex exponentials:

$\displaystyle \sin(x) = \frac{e^{ix} - e^{-ix}}{2i} \quad,%
\quad \cos(x) = \frac{e^{ix} + e^{-ix}}{2} $

Useful formulas:

$\displaystyle \sin(a+b) = \sin(a)\cos(b) + \cos(a)\sin(b) $

$\displaystyle \sin(a-b) = \sin(a)\cos(b) - \cos(a)\sin(b) $

$\displaystyle \cos(a+b) = \cos(a)\cos(b) - \sin(a)\sin(b) $

$\displaystyle \cos(a-b) = \cos(a)\cos(b) + \sin(a)\sin(b) $

$\displaystyle \sin(a) - \sin(b) =
2\sin\left(\frac{a-b}{2}\right) \cos\left(\frac{a+b}{2}\right) $

$\displaystyle \sin(a) + \sin(b) =
2\sin\left(\frac{a+b}{2}\right) \cos\left(\frac{a-b}{2}\right) $

$\displaystyle \cos(a) + \cos(b) =
2\cos\left(\frac{a+b}{2}\right) \cos\left(\frac{a-b}{2}\right) $

$\displaystyle \cos(a) - \cos(b) =
-2\sin\left(\frac{a-b}{2}\right) \cos\left(\frac{a+b}{2}\right) $

$\displaystyle \sin(2x) = 2\sin(x)\cos(x) $

$\displaystyle \cos(2x) = 2\cos^2(x) - 1 $


Experimental statistics

Variance of some data around the mean:

$\displaystyle \sigma^2 = \frac{n\sum x_i^2 - (\sum x_i)^2}{(n-1)n^2} $

Principle of the error propagation:

$\displaystyle \Delta z = \sqrt{
\left(\frac{\partial z}{\partial x}\right)^2\!\Delta x^2 +
\left(\frac{\partial z}{\partial y}\right)^2\!\Delta y^2 +
\cdots} $

Linear regression ($ y=mx+b$):

$\displaystyle m = \frac{n\sum x_i y_i - (\sum x_i)(\sum y_i)}%
{n\sum x_i^2 - (\sum x_i)^2} $

$\displaystyle b = \frac{(\sum x_i^2)(\sum y_i) - (\sum x_i)(\sum x_i y_i)}%
{n\sum x_i^2 - (\sum x_i)^2} $

$\displaystyle \Delta m = \sqrt{\frac{n\sum y_i^2 - (\sum y_i)^2 -
\frac{\left(...
...n\sum x_i^2 - (\sum x_i)^2}}%
{(n-2)\left(n\sum x_i^2 - (\sum x_i)^2\right)}} $

$\displaystyle \Delta b = \sqrt{\frac{\sum x_i^2}{n}}\:\Delta m $

$\displaystyle r = \frac{n\sum x_i y_i - (\sum x_i)(\sum y_i)}
{\sqrt{\left(n\sum x_i^2 - (\sum x_i)^2\right)%
\left(n\sum y_i^2 - (\sum y_i)^2\right)}} $

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Mathematic definitions and Formulas

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The translation was initiated by Antonio Rafael Paiva on 2004-12-13


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Antonio Rafael Paiva 2004-12-13