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Numerical Computation
Here you'll be introduced to the bare basics of mathematics. Topics include commonly used words and phrases, symbols, and how to follow the order of operations.

Measurements
An introduction to numerical computation. Emphasis is placed on scientific and engineering notation, the rule of significant figures, and converting between SI and Imperial units.

Trigonometry with Right Triangles
Here we focus on right angle triangles within quadrant I of an xy plane. None of the angles we evaluate here are greater than 90°. A unit on trigonometry with oblique triangles is covered later.

Trigonometry with Oblique Triangles
This unit is a continuation of trigonometry with right triangles except we'll extend our understanding to deal with angles *greater* than 90°. Resolving and combining vectors will be covered at the end of this unit.

Vector Analysis

Introduction to Algebra

Factoring

Solving Equations

Functions and Graphs

Geometry
This unit focuses on analyzing and understand the characteristics of various shapes, both 2D and 3D.
 Identify, measure, and calculate different types of straight lines and angles
 Calculate the interior angles of polygons
 Solve problems involving a variety of different types of triangles
 Calculate the area of a variety of different types of quadrilaterals
 Solve problems involving circles
 Calculate the areas and volumes of different solids

Introduction to Statistics
Variance and Standard Deviation
The standard deviation (SD) is a measure of the spread of the data (how far from the normal it is). A number such as the mean or the standard deviation may be found either for an entire population (symbolized as σ or σ_{x}) or for a sample (symbolized as s) drawn from that population.
Basically, when your standard deviation, it means that the values in a statistical data set are close to the mean of the data set, on average, and a large standard deviation means that the values in the data set are farther away from the mean, on average.
The variance (symbolized s² or σ²) is defined as the average of the squared differences from the mean. In other words, once you find the mean, you subtract the mean from each sample, and square that number. You then take the average of these squared numbers by adding them up and dividing by the number of observations. The formula is shown below for a population:
${s}^{2}=\frac{{\displaystyle \sum _{i=1}^{n}}{\left({x}_{i}\u2013\mathrm{x\u0304}\right)}^{2}}{n}or{\sigma}^{2}=\frac{{\displaystyle \sum _{i=1}^{n}{\left({x}_{i}\u2013\mathrm{x\u0304}\right)}^{2}}}{N}\phantom{\rule{0ex}{0ex}}$ Keep in mind that s² and n is used for sample populations while σ² and N for the entire population.
Once we have the variance, it is a simple matter to get the standard deviation (s or σ – notice the lack of the power). As mentioned earlier, SD is the most common measure of dispersion.
$s=\sqrt{\frac{{\displaystyle \sum _{i=1}^{n}{\left({x}_{i}\u2013\mathrm{x\u0304}\right)}^{2}}}{n}}or\sigma =\sqrt{\frac{{\displaystyle \sum _{i=1}^{n}{\left({x}_{i}\u2013\mathrm{x\u0304}\right)}^{2}}}{N}}\phantom{\rule{0ex}{0ex}}\phantom{\rule{0ex}{0ex}}$Let’s look at an example where variance and standard deviation are calculated: