# 2.9: Chapter Formula Review


## 2.2 Measures of the Location of the Data

$$i=\left(\frac{k}{100}\right)(n+1)$$

where $$i$$ = the ranking or position of a data value,

$$k$$ = the $$k$$th percentile,

$$n$$ = total number of data.

Expression for finding the percentile of a data value: $$\left(\frac{x+0.5 y}{n}\right)(100)$$

where $$x$$ = the number of values counting from the bottom of the data list up to but not including the data value for which you want to find the percentile,

$$y$$ = the number of data values equal to the data value for which you want to find the percentile,

$$n$$ = total number of data

## 2.3 Measures of the Center of the Data

$$\mu=\frac{\sum f m}{\sum f}$$ Where $$f$$ = interval frequencies and $$m$$ = interval midpoints.

The arithmetic mean for a sample (denoted by $$\overline{x}$$) is $$\overline{x}=\frac{\text { Sum of all values in the sample }}{\text { Number of values in the sample }}$$

The arithmetic mean for a population (denoted by μ) is $$\boldsymbol{\mu}=\frac{\text { Sum of all values in the population }}{\text { Number of values in the population }}$$

## 2.5 Geometric Mean

The Geometric Mean: $$\overline{x}=\left(\prod_{i=1}^{n} x_{i}\right)^{\frac{1}{n}}=\sqrt[n]{x_{1} \cdot x_{2} \cdots x_{n}}=\left(x_{1} \cdot x_{2} \cdots x_{n}\right)^{\frac{1}{n}}$$

## 2.6 Skewness and the Mean, Median, and Mode

Formula for skewness: $$a_{3}=\sum \frac{\left(x_{i}-\overline{x}\right)^{3}}{n s^{2}}$$
Formula for Coefficient of Variation:$$C V=\frac{s}{\overline{x}} \cdot 100 \text { conditioned upon } \overline{x} \neq 0$$

## 2.7 Measures of the Spread of the Data

$$s_{x}=\sqrt{\frac{\sum f m^{2}}{n}-\overline{x}^{2}} \text { where }$$ $$\begin{array}{l}{s_{x}=\text { sample standard deviation }} \\ {\overline{x}=\text { sample mean }}\end{array}$$

Formulas for Sample Standard Deviation $$s=\sqrt{\frac{\Sigma(x-\overline{x})^{2}}{n-1}} \text { or } s=\sqrt{\frac{\Sigma f(x-\overline{x})^{2}}{n-1}} \text { or } s=\sqrt{\frac{\left(\sum_{t=1}^{n} x^{2}\right)-n x^{2}}{n-1}}$$ For the sample standard deviation, the denominator is n - 1, that is the sample size - 1.

Formulas for Population Standard Deviation $$\sigma=\sqrt{\frac{\Sigma(x-\mu)^{2}}{N}} \text { or } \sigma=\sqrt{\frac{\Sigma f(x \mu)^{2}}{N}} \text { or } \sigma=\sqrt{\frac{\sum_{i=1}^{N} x_{i}^{2}}{N}-\mu^{2} F}$$ For the population standard deviation, the denominator is N, the number of items in the population.

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