# Common Formulas

• • Michelle Oja
• Taft College
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The following formulas are in the order in which you learn about them in this textook. Use the Table of Contents to look for a specific equation.

## Descriptive Statistics

### Mean

$\displaystyle \bar{X} = \dfrac{\sum X}{N}$

### Standard Deviation

$s=\sqrt{\dfrac{\sum(X-\overline {X})^{2}}{N-1}}$

Which is also: $$s=\sqrt{\dfrac{\sum(X-\overline {X})^{2}}{N-1}}=\sqrt{\dfrac{S S}{d f}}$$

Some instructors prefer this formula because it is easier to calculate (but more difficult to see what's happening):

$\sqrt{ \dfrac{\left(\sum(X^2) - \dfrac{(\sum{X})^2}{N}\right)}{(N-1)}}$

## z-score

### To find the z-score when you have a raw score:

$z=\frac{X-\bar{X}}{s}$

### To find a raw score when you have a z-score:

$x=z s+\overline{X}$

## t-tests

### One-Sample t-test

These are the same formulas, but formatted slightly differently.

$t = \cfrac{(\bar{X}-\mu)}{\left(\cfrac{s} {\sqrt{n}}\right)}$

### Confidence Interval

$\text {Margin of Error }=t \times \left(\dfrac{s}{\sqrt{N}}\right) \nonumber$

$\text { Confidence Interval }=\overline{X} \pm (t \times \left(\dfrac{s}{\sqrt{N}}\right))$

### Independent Sample t-test

#### Unequal N

You can always use this formula:

$t=\dfrac{(\bar{X}_{1}-\bar{X}_{2})}{\sqrt{\left[\dfrac{\left(n_{1}-1\right) \times s_{1}^{2} + \left(n_{2}-1\right) \times s_{2}^{2}}{n_{1}+n_{2}-2}\right] \times \left(\dfrac{1}{n_{1}} + \dfrac{1}{n_{2}}\right)}}$

#### Equal N

You should only use this formula when your two independent groups are the same size (N), meaning the same number of people in each group.

$\dfrac{(\bar{X_1} - \bar{X_2})}{\sqrt{\left(\frac{s_1^2}{N_1}\right)+\left(\frac{s_2^2}{N_2}\right)}}$

### Dependent Sample t-test

#### Conceptual Formula (symbols)

$t = \cfrac{\overline{X}_{D}}{\left(\cfrac{s_{D}}{\sqrt{N}} \right)}$

#### Full Formula

$t = \cfrac{ \left(\cfrac{\Sigma {D}}{N}\right)} { {\sqrt{\left(\cfrac{\sum\left((X_{D}-\overline{X}_{D})^{2}\right)}{(N-1)}\right)} } /\sqrt{N} }$

$S S_{B}=\sum_{EachGroup} \left[ \left(\overline{X}_{group}-\overline{X}_{T}\right)^{2} \times (n_{group}) \right]$

$S S_{W}=\sum_{EachGroup} \left[ \sum \left(\left(X-\overline{X}_{group}\right)^{2}\right) \right]$

$S S_{T}=\sum \left[ \left(X - \overline{X}_{T}\right)^{2} \right]$

$HSD = q \times \sqrt{\dfrac{MSw}{n_{group}}}$

Same as above.

$SS_{Ps} = \left[\sum{\left(\dfrac{(\sum{X_{Ps}})^2}{k}\right)}\right] -\dfrac{\left((\sum{X})^2\right)}{N}$

$SS_{WG} = SS_{T} - SS_{BG} - S_{P} \nonumber$

Same as above.

## Pearson's r (Correlation)

The following formulas are the same. Use the first one when you already have the standard deviation calculated.

These are paired data, so N is the number of pairs.

$r= \cfrac{ \left( \cfrac{\sum ((x_{Each} - \bar{X_x})\times(y_{Each} - \bar{X_y}) ) }{(N-1)}\right) } {(s_x \times s_y)}$

### SD Not Calculated:

$r = \cfrac{ \left( \cfrac{\sum ((x - \bar{X_x})\times(y - \bar{X_y}) ) }{(N-1)}\right) } {\left( \sqrt{\dfrac{\sum\left((x-\overline {X_x})^{2}\right)}{N-1}} \right) \times \left( \sqrt{\dfrac{\sum\left((y-\overline {X_y})^{2}\right)}{N-1}} \right)}$

## Regression Line Equation

$\widehat{\mathrm{Y}}=\mathrm{a}+(\mathrm{b}\times{X})$

### a (intercept):

$\mathrm{a}=\overline{X_y}- (\mathrm{b} \times \overline{X_x})$

### b (slope):

$\dfrac{\sum(Diff_{x} \times Diff_{y})}{\sum({Diff_{X}}^2)}$

In which "Diff" means the differences between each score and that variable's mean.

## Pearson's $$\chi^2$$ (Chi-Square)

$\chi^{2}=\sum_{Each}\left(\dfrac{\left(E-O\right)^{2}}{E} \right)$

### Expected Frequencies

#### Goodness of Fit:

$\dfrac{N}{k}$

#### Test of Independence:

$E_{EachCell}=\dfrac{RT \times CT}{N}$

In which RT = Row Total and CT = Column Total