4. Statistical Calculations

4-1. Basic Statistical Calculation Operations

1. Click anywhere on the Paper to display the Sticky Note menu.

2. Click to display a Statistical Data Sticky Note.

・Cell A1 becomes selected for input at this time.

3. Use your computer keyboard to input the data values.

In this example, we will input the data values below.

 A 0.5 1.2 2.4 4.0 5.2
 B -2.1 0.3 1.5 2.0 2.4

3-1. Input 0.5 into cell A1 and then press [Enter].

・Cell A2 becomes selected for input.

3-2. Input 1.2 into cell A2 and then press [Enter].

・Cell A3 becomes selected for input.

3-3. Repeat the above operation to input the required data values.

4. Use your computer mouse to select the data values to be used for statistical calculations.

Here we will select from cell A1 through B5.

5. On the tool palette, select a statistical calculation type.

In this example, we will perform two-variable statistical calculations, and draw a scatter plot and a linear regression graph.

5-1. On the tool palette, click [Calculation].

5-2. Click [Two-Variable] to display two-variable statistical calculation results.

5-3. On the tool palette, click .

5-4. Click [Graph] - [Scatter Plot] to draw a scatter plot.

5-5. On the tool palette, click .

5-6. Click [Regression] - [Linear Regression] to draw a linear regression graph.

4-2. Editing Statistical Data Values

4-2-1 To input data values

1. Display a Statistical Data Sticky Note.

・Cell A1 becomes selected for input at this time.

2. After you input a data value for cell A1, cell A2 will become selected for input.

3. Repeat the above steps to sequentially input data values into column A.

Note

・To input data values into column B, click cell B1. Inputting a data value into cell B1 will automatically add another column (column C).

4-2-2. To correct data values

1. Click the data value you want to correct.

2. Input the new data value.

4-2-3. To insert a row

1. Right-click the number of the row where you want to insert a new row.

・This displays a menu.

2. Click [Insert] to insert the row.

4-2-4. To insert a column

1. Right-click the header of the column where you want to insert a new column.

・This displays a menu.

2. Click [Insert] to insert the column.

4-2-5. To delete a row

1. Right-click the number of the row you want to delete.

・This displays a menu.

2. Click [Delete] to delete the row.

4-2-6. To delete a column

1. Right-click the header of the column you want to delete.

・This displays a menu.

2. Click [Delete] to delete the column.

4-2-7. Assigning a Name to a List

Once you assign a name to a list, you can use the name in tests and other statistical calculations. List names are input into the cells below the column names.

Example: To assign the name "List1" to column A

1. Double-click the cell under A.

・This selects the cells for list name input.

2. Input the list name "List1".

Note

・The following are the rules that apply to list names.

  • - List names can be up to 8 bytes long.
  • - The following characters are allowed in a list name: Upper-case and lower-case characters, subscript characters, numbers.
  • - List names are case-sensitive. For example, each of the following is treated as a different list name: abc, Abc, aBc, ABC.

4-3. Selecting Data Values for Statistical Calculation

You can select a range of cells by dragging the mouse pointer across them.

Data Selection Examples

Note

  • You can select a range of columns by dragging your mouse pointer across column headers.
  • Up to three columns can be used for statistical calculations. Statistical calculations cannot be performed if more than three columns are selected.

4-4. Performing 1-variable Statistical Calculations

As an example, we will use the data values below.

 Data 1 2 3 4 5
 Frequency 1 2 3 2 1

1. Input data values into column A and frequencies into column B.

2. Drag from cell A1 to cell B5 to select the range of cells between them.

3. On the tool palette, click [Calculation] - [One-Variable] to display one-variable statistical calculation results.

4. To display other hidden calculation result items, click [more] on the Statistical Calculation Sticky Note.

・To return the Statistical Calculation Sticky Note to its reduced size configuration, click [hide].

・Performing a one-variable statistical calculation displays the results below.

  • $x̅$ ... sample mean
  • $\Sigma x$ ... sum of data
  • $\Sigma x^2$ ... sum of squares
  • $\sigma x$ ... population standard deviation
  • $Sx$ ... sample standard deviation
  • $n$ ... sample size
  • $min(x)$ ... minimum
  • $Q_{1}$ ... first quartile
  • $Med$ ... median
  • $Q_{3}$ ... third quartile
  • $max(x)$ ... maximum
  • $Mode$ ... mode
  • $ModeN$ ... number of data mode items
  • $ModeF$ ... data mode frequency
  • When Mode has multiple solutions, they are all displayed.

4-5. Drawing a Regression Graph

In this example, we will use the data values below to draw a scatter plot, linear regression graph, and a quadratic regression graph.

 A 1.0 1.2 1.5 1.6 1.9 2.1 2.4 2.5 2.7 3.0
 B 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 2.0

1. Input data values into columns A and B.

2. Drag from cell A1 to cell B10 to select the range of cells between them.

3. On the tool palette, click [Graph] - [Scatter Plot] to draw a scatter plot.

4. Click .

5. Click [Regression] - [Linear Regression] to draw a linear regression graph.

6. Click [Quadratic Regression] to draw a quadratic regression graph.

4-6. Drawing a Histogram

As an example, we will use the data values below.

 Data 1 2 3 4 5
 Frequency 1 2 3 2 1

1. Input data values into column A and frequencies into column B.

2. Drag from cell A1 to cell B5 to select the range of cells between them.

3. On the tool palette, click [Graph] - [Histogram] to draw a histogram.

Note

  • You can change the histogram draw start value (HStart) and step value (HStep), if you want. On the Statistical Calculation Sticky Note, click HStart or HStep and then input the value you want.

4-7. Drawing a Box-and-whisker Diagram

As an example, we will use the data values below.

 Data 1 5 10 12 14 16 18 20 40

1. Input data values into column A.

2. Drag from cell A1 to cell A9 to select the range of cells between them.

3. On the tool palette, click [Graph] - [Box & Whisker Plot] to draw a box-and-whisker diagram.

Note

  • You can display outlier values, if you want. To do so, select [Identify] for the Outliers item of the Statistical Calculation Sticky Note.

4-8. Drawing a Circle Graph

As an example, we will use the data values below.

 Data 5 10 20 30 40

1. Input data values into column A.

2. Drag from cell A1 to cell A5 to select the range of cells between them.

3. On the tool palette, click [Graph] - [Pie Chart] to draw a circle graph.

4-9. Scatter Plot Operations

4-9-1. To move the points of a scatter plot

As an example, we will use the data values below.

 A 0.5 1.2 2.4 4.0 5.2
 B -2.1 0.3 1.5 2.0 2.4

1. Input the data values, and then drag from cell A1 to cell B5 to select the range of cells between them.

2. On the tool palette, click [Graph] - [Scatter Plot] to draw a scatter plot.

3. To move a scatter plot point, drag it. This will also change the Statistical Data Sticky Note values to the coordinates of the destination.

4-9-2. To lock a cell

Note

  • When a cell is locked, its data value will not change even if you try to move its scatter plot point. For example, if you lock a column A cell, the corresponding scatter plot point cannot be moved along the x-axis.

1. Continuing from the procedure under "4-9-1. To move the points of a scatter plot", select cell A1.

2. Click the Sticky Note Settings button ().

3. Click the lock icon () next to [Lock].

・This locks cell A1. If you drag the scatter plot point that corresponds to cells A1 and B1, movement will not be possible along the x-axis.

4-9-3. To unlock a cell

1. Select the locked cell that you want to unlock.

2. Click the Sticky Note Settings button ().

3. Click the unlock icon () next to [UnLock].

4-10. Performing a One-Sample Z-Test

4-10-1. To specify the number of data samples and then perform a one-sample Z-test

Example:

  • Sample size: $n$ = 48
  • Sample mean: $\overline{x}$ = 24.5
  • Null hypothesis: $\mu \ne 0$
  • Standard deviation: $\sigma = 3$

1. Display a Statistical Data Sticky Note.

2. On the tool palette, click [Test].

・This displays Test types in the Tool Palette.

3. On the Tool Palette, click [One-Sample Z-Test].

・This displays a One-Sample Z-Test Sticky Note.

4. Configure settings as shown below.

  • $ \mu $ condition: On the menu that appears, select "≠".
  • $ \mu_0 $ : Input 0.
  • $ \sigma $: Input 3.
  • $ \overline{x}$ : Input 24.5.
  • $ n $ : Input 48.

5. Click [EXE].

・This displays the calculation results and the graph.

  • $ \mu \ne $ ... population mean value condition
  • $ z $ ... z value
  • Prob ... p value
  • $ \overline{x} $ ... sample mean
  • $ n $ ... sample size

4-10-2. To use lists to perform a one-sample Z-test

  • List1 ={120, 125, 130, 135, 140, 145}
  • List2 ={1, 2, 4, 1, 1, 1}(frequency)

1. Input the list names and data values for columns A and B.

2. Drag from cell A1 to cell B6 to select the range of cells.

3. On the tool palette, click [Test].

・This displays Test types in the Tool Palette.

4. On the Tool Palette, click [One-Sample Z-Test].

・This displays a One-Sample Z-Test Sticky Note.

5. Click "List".

6. Configure settings as shown below.

  • $ \mu $ condition: On the pulldown menu that appears, select ">".
  • $ \mu_0 $ : Input 120.
  • $ \sigma $: Input 19.
  • Freq: On the menu that appears, select "List2".

7. Click [EXE].

・This displays the calculation results and the graph.

  • $ \mu > $ ... population mean value condition
  • $ z $ ... z value
  • Prob ... p value
  • $ \overline{x} $ ... sample mean
  • $ s_x $ ... sample standard deviation
  • $ n $ ... sample size

4-11. Statistical Calculations and Graphs

One-Variable
This displays the calculation results of single-variable statistics.
$x ̅$ ... sample mean
$\Sigma x$ ... sum of data
$\Sigma x^2$ ... sum of squares
$\sigma x$ ... population standard deviation
$Sx$ ... sample standard deviation
$n$ ... sample size
$minX$ ... minimum
$Q1$ ... first quartile
$Med$ .... median
$Q3$ ... third quartile
$maxX$ ... maximum
$Mode$ ... mode
$ModeN$ ... number of data mode items
$ModeF$ ... data mode frequency
When $Mode$ has multiple solutions, they are all displayed.
Two-Variable
This displays the calculation results of paired-variable statistics.
$x ̅$ ... sample mean
$\Sigma x$ ... sum of data
$\Sigma x^2$ ... sum of squares
$\sigma x$ ... population standard deviation
$Sx$ ... sample standard deviation
$n$ ... sample size
$y ̅$ ... sample mean
$\Sigma y$ ... sum of data
$\Sigma y^2$ ............. sum of squares
$\sigma y$ ... population standard deviation
$Sy$ ... sample standard deviation
$\Sigma xy$ ... sum of the products XList and YList data
$minX$ ... minimum
$maxX$ ... maximum
$minY$... minimum
$maxY$ ... maximum
Linear Regression
Linear regression uses the method of least squares to determine the equation that best fits your data points, and returns values for the slope and y-intercept. The graphic representation of this relationship is a linear regression graph.

$y = ax + b$
$a$ ... regression coefficient (slope)
$b$ ... regression constant term (y-intercept)
$r$ .... correlation coefficient
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
MedMed Regression
When you suspect that the data contains extreme values, you should use the Med-Med graph (which is based on medians) in place of the linear regression graph. Med-Med graph is similar to the linear regression graph, but it also minimizes the effects of extreme values.

$y = ax + b$
$a$ ... regression coefficient (slope)
$b$ ... regression constant term (y-intercept)
Quadratic Regression
Quadratic regression graph uses the method of least squares to draw a curve that passes the vicinity of as many data points as possible. This graph can be expressed as quadratic regression expression.

$y = ax^2 + bx + c$
$a$ ... regression second coefficient
$b$ ... regression first coefficient
$c$ ... regression constant term (y-intercept)
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Cubic Regression
Cubic regression graph uses the method of least squares to draw a curve that passes the vicinity of as many data points as possible. This graph can be expressed as cubic regression expression.

$y = ax^3 + bx^2 + cx + d$
$a$ ... regression third coefficient
$b$ ... regression second coefficient
$c$ ... regression first coefficient
$d$ ... regression constant term (y-intercept)
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Quartic Regression
Quartic regression graph uses the method of least squares to draw a curve that passes the vicinity of as many data points as possible. This graph can be expressed as quartic regression expression.

$y = ax^4 + bx^3 + cx^2 + dx + e$
$a$ ... regression fourth coefficient
$b$ ... regression third coefficient
$c$ ... regression second coefficient
$d$ ... regression first coefficient
$e$ ... regression constant term (y-intercept)
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Logarithmic Regression
Logarithmic regression expresses $y$ as a logarithmic function of $x$. The normal logarithmic regression formula is $y=a+b \cdot ln(x)$. If we say that $X=ln(x)$, then this formula corresponds to the linear regression formula $y=a+b \cdot X$.

$y = a + b \cdot ln\ x$
$a$ ... regression constant term
$b$ ... regression coefficient
$r$ .... correlation coefficient
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Exponential Regression
Exponential regression can be used when $y$ is proportional to the exponential function of $x$. The normal exponential regression formula is $y=a \cdot e^{b \cdot x}$. If we obtain the logarithms of both sides, we get $ln(y)=ln(a)+b \cdot x$. Next, if we say that $Y=ln(y)$ and $A=ln(a)$, the formula corresponds to the linear regression formula $Y=A+b \cdot x$.

$y = a \cdot e^{bx}$
$a$ ... regression coefficient
$b$ ... regression constant term
$r$ .... correlation coefficient
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
abExponential Regression
Exponential regression can be used when $y$ is proportional to the exponential function of $x$. The normal exponential regression formula in this case is $y=a \cdot b^x$. If we take the natural logarithms of both sides, we get $ln(y)=ln(a)+(ln(b)) \cdot x$. Next, if we say that $Y=ln(y)$, $A=ln(a)$ and $B=ln(b)$, the formula corresponds to the linear regression formula $Y=A+B \cdot x$.

$y = a \cdot b^x$
$a$ ... regression constant term
$b$ ... regression coefficient
$r$ .... correlation coefficient
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Power Regression
Power regression can be used when $y$ is proportional to the power of $x$. The normal power regression formula is $y=a \cdot x^b$. If we obtain the logarithms of both sides, we get $ln(y)=ln(a)+b \cdot ln(x)$. Next, if we say that $X=ln(x)$, $Y=ln(y)$, and $A=ln(a)$, the formula corresponds to the linear regression formula $Y=A+b \cdot X$.

$y = a \cdot x^b$
$a$ ... regression coefficient
$b$ ... regression power
$r$ .... correlation coefficient
$r^2$ ... coefficient of determination
$MSe$ ... mean square error
Sinusoidal Regression
Sinusoidal regression is best for data that repeats at a regular fixed interval over time.

$y = a \cdot sin( bx + c ) + d$
$a$, $b$, $c$, $d$ ... regression coefficient
$MSe$ ... mean square error
Logistic Regression
Logistic regression is best for data whose values continually increase over time, until a saturation point is reached.

$y=\cfrac{c}{1+ae^{-bx}}$
$a$, $b$, $c$ ... regression coefficient
$MSe$ ... mean square error
One-Sample Z-Test
Tests a single sample mean against the known mean of the null hypothesis when the population standard deviation is known. The normal distribution is used for the One-Sample Z-Test.

$Z=\cfrac{\overline{x}-\mu_{0}}{\cfrac{\sigma}{\sqrt{n}}}$
$\overline{x}$ : sample mean
$\mu_{0}$ : assumed population mean
$\sigma$ : population standard deviation
$n$ : sample size

Data type: Variable

・Input Terms

  • $ \mu $ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • $ \mu_{0} $ : assumed population mean
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • $ \overline{x} $ : sample mean
  • $ n $ : sample size (positive integer)

・Output Terms

  • $ \mu \neq $ : population mean value condition
  • $ {\it z} $ : z value
  • Prob : p value

Data type: List

・Input Terms

  • $ \mu $ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • $ \mu_{0} $ : assumed population mean
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • List : data list
  • Freq : frequency (1 or list name)

・Output Terms

  • $ \mu \neq $ : population mean value condition
  • $ {\it z} $ : z value
  • Prob : p value
  • $ \overline{x} $ : sample mean
  • $ s_{x} $ : sample standard deviation
  • $ n $ : sample size
Two-Sample Z-Test
Tests the difference between two means when the standard deviations of the two populations are known. The normal distribution is used for the Two-Sample Z-Test.

$ Z=\cfrac{ \overline{x}_{1} - \overline{x}_{2} }{ \sqrt{ \cfrac{{\sigma_{1}^2}}{n_{1}} + \cfrac{{\sigma_{2}}^2}{n_{2}} } } $
$ \overline{x}_{1} $ : sample mean of sample 1 data
$ \overline{x}_{2} $ : sample mean of sample 2 data
$ \sigma_{1} $ : population standard deviation of sample 1
$ \sigma_{2} $ : population standard deviation of sample 2
$ n_{1} $ : size of sample 1
$ n_{2} $ : size of sample 2

Data type: Variable

・Input Terms

  • $ \mu_{1} $ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is less than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2).
  • $ \sigma_{1} $ : population standard deviation of sample 1 ($ \sigma_{1} > 0 $)
  • $ \sigma_{2} $ : population standard deviation of sample 2 ($ \sigma_{2} > 0 $)
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ n_{1} $ : size of sample 1 (positive integer)
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ n_{2} $ : size of sample 2 (positive integer)

・Output Terms

  • $ {\it z} $ : z value
  • Prob : p value

Data type: List

・Input Terms

  • $ \mu_{1} $ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is less than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2).
  • $ \sigma_{1} $ : population standard deviation of sample 1 ($ \sigma_{1} > 0 $)
  • $ \sigma_{2} $ : population standard deviation of sample 2 ($ \sigma_{2} > 0 $)
  • List(1) : list where sample 1 data is located
  • List(2) : list where sample 2 data is located
  • Freq(1) : frequency of sample 1 (1 or list name)
  • Freq(2) : frequency of sample 2 (1 or list name)

・Output Terms

  • $ {\it z} $ : z value
  • Prob : p value
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ s_{x_{1}} $ : sample standard deviation of sample 1
  • $ s_{x_{2}} $ : sample standard deviation of sample 2
  • $ n_{1} $ : size of sample 1
  • $ n_{2} $ : size of sample 2
One-Prop Z-Test (One-Proportion Z-Test)
Tests a single sample proportion against the known proportion of the null hypothesis. The normal distribution is used for the One-Proportion Z-Test.

$Z = \cfrac{ \cfrac{x}{n} - p_{0} }{ \sqrt{ \cfrac{ p_{0}(1-p_{0}) }{n} }}$
$p_{0}$ : expected sample proportion
$n$ : sample size

・Input Terms

  • Prop cond : sample proportion test condition (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • $p_{0}$ : expected sample proportion ($ 0 < p_{0} < 1 $)
  • $x$ : sample value (integer, $ x \geq 0 $)
  • $n$ : sample size (positive integer)

・Output Terms

  • Prop Cond $\neq$ : sample proportion test condition
  • $z$ : z value
  • Prob : p value
  • $\hat{p}$ : estimated sample proportion
Two-Prop Z-Test (Two-Proportion Z-Test)
Tests the difference between two sample proportions. The normal distribution is used for the Two-Proportion Z-Test.

$ Z = \cfrac{ \cfrac{x_{1}}{n_{1}} - \cfrac{x_{2}}{n_{2}} }{ \sqrt{ \hat{p}(1-\hat{p})( \cfrac{1}{n_{1}} + \cfrac{1}{n_{2}} ) } }$
$x_{1}$ : data value of sample 1
$x_{2}$ : data value of sample 2
$n_{1}$ : size of sample 1
$n_{2}$ : size of sample 2
$\hat{p}$ : estimated sample proportion

・Input Terms

  • $p_{1}$ condition : sample proportion test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is smaller than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2.)
  • $x_{1}$ : data value of sample 1 (integer, $x_{1}$ must be less than or equal to $n_{1}$)
  • $n_{1}$ : size of sample 1 (positive integer)
  • $x_{2}$ : data value of sample 2 (integer, $x_{2}$ must be less than or equal to $n_{2}$)
  • $n_{2}$ : size of sample 2 (positive integer)

・Output Terms

  • $z$ : z value
  • Prob : p value
  • $\hat{p}_{1}$ : estimated proportion of sample 1
  • $\hat{p}_{2}$ : estimated proportion of sample 2
  • $\hat{p}$ : estimated sample proportion
One-Sample t-Test
Tests a single sample mean against the known mean of the null hypothesis when the population standard deviation is unknown. The t distribution is used for the One-Sample t-Test.

$t = \cfrac{ \overline{x} - \mu_{0} }{ \cfrac{ s_{x} }{ \sqrt{n} } }$
$\overline{x}$ : sample mean
$\mu_{0}$ : assumed population mean
$s_{x}$ : sample standard deviation
$n$ : sample size

Data type: Variable

・Input Terms

  • $\mu$ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • $\mu_{0}$ : assumed population mean
  • $\overline{x}$ : sample mean
  • $s_{x}$ : sample standard deviation ($ s_{x} > 0 $)
  • $n$ : sample size (positive integer)

・Output Terms

  • $\mu \ne$ : population mean value test conditions
  • $t$ : t value
  • Prob : p value

Data type: List

・Input Terms

  • $\mu$ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • $\mu_{0}$ : assumed population mean
  • List : data list
  • Freq : frequency (1 or list name)

・Output Terms

  • $\mu \ne$ : population mean value test conditions
  • $t$ : t value
  • Prob : p value
  • $\overline{x}$ : sample mean
  • $s_{x}$ : sample standard deviation
  • $n$ : sample size
Two-Sample t-Test
Tests the difference between two means when the standard deviations of the two populations are unknown. The t distribution is used for the Two-Sample t-Test.

・When the two population standard deviations are equal (pooled)
$t=(\overline{x}_{1}-\overline{x}_{2})/\sqrt{{s_{p}}^2(1/n_1+1/n_2)}$
$df=n_1+n_2-2$
$s_p=\sqrt{ ( (n_1-1){s_{x_1}}^2 + (n_2-1){s_{x_2}}^2 ) / (n_1+n_2-2) }$
・When the two population standard deviations are not equal (not pooled)
$t=(\overline{x}_{1}-\overline{x}_{2})/\sqrt{ {s_{x_1}}^2/n_1 + {s_{x_2}}^2/n_2 }$
$df = 1 / ( C^2/(n_1-1) + (1-C)^2/(n_2-1) )$
$C = ({s_{x_1}}^2/n_1)/({s_{x_1}}^2/n_1+{s_{x_2}}^2/n_2)$
$x_1$: sample mean of sample 1 data
$x_2$: sample mean of sample 2 data
$s_{x_1}$ : sample standard deviation of sample 1
$s_{x_2}$ : sample standard deviation of sample 2
$s_p$ : pooled sample standard deviation
$n_1$ : size of sample 1
$n_2$ : size of sample 2

Data type: Variable

・Input Terms

  • $\mu_1$ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is smaller than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2.)
  • $\overline{x}_1$ : sample mean of sample 1 data
  • $s_{x_1}$ : sample standard deviation of sample 1 ($s_{x_1} > 0$)
  • $n_1$ : size of sample 1 (positive integer)
  • $\overline{x}_2$ : sample mean of sample 2 data
  • $s_{x_2}$ : sample standard deviation of sample 2 ($s_{x_2} > 0$)
  • $n_2$ : size of sample 2 (positive integer)

・Output Terms

  • $ {\it t} $ : t value
  • Prob : p value
  • $df$ : degrees of freedom
  • $s_p$ : pooled sample standard deviation

Data type: List

・Input Terms

  • $\mu_1$ condition : population mean value test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is smaller than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2.)
  • List(1) : list where sample 1 data is located
  • List(2) : list where sample 2 data is located
  • Freq(1) : frequency of sample 1 (1 or list name)
  • Freq(2) : frequency of sample 2 (1 or list name)
  • Pooled : On (equal variances) or Off (unequal variances)

・Output Terms

  • $ {\it t} $ : t value
  • Prob : p value
  • $df$ : degrees of freedom
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ s_{x_{1}} $ : sample standard deviation of sample 1
  • $ s_{x_{2}} $ : sample standard deviation of sample 2
  • $s_p$ : Pooled sample standard deviation
  • $ n_{1} $ : size of sample 1
  • $ n_{2} $ : size of sample 2
Linear Reg t-Test (Linear Regression t-Test)
Tests the linear relationship between the paired variables (x, y). The method of least squares is used to determine a and b, which are the coefficients of the regression formula $y = a + bx$. The p-value is the probability of the sample regression slope (b) provided that the null hypothesis is true, $\beta=0$. The t distribution is used for the Linear Regression t-Test.

$t=r\sqrt{\cfrac{n-2}{1-r^2}}$
$b=\sum_{i=1}^{n}(x_i-\overline{x})(y_i-\overline{y})/\sum_{i=1}^{n}(x_i-\overline{x})^2$
$a=\overline{y}-b\overline{x}$
$a$ : regression constant term (y-intercept)
$b$ : regression coefficient (slope)
$n$ : sample size (n ≥ 3)
$r$ : correlation coefficient
$r^2$ : coefficient of determination

・Input Terms

  • $\beta \&p\ cond$ : test conditions (“≠” specifies two-tail test, “<” specifies lower one-tail test, “>” specifies upper one-tail test.)
  • XList : x-data list
  • YList : y-data list
  • Freq : frequency (1 or list name)

・Output Terms

  • $t$ : t value
  • Prob : p value
  • $df$ : degrees of freedom
  • $a$ : regression constant term (y-intercept)
  • $b$ : regression coefficient (slope)
  • se : standard error of estimate about the least-squares regression line
  • $r$ : correlation coefficient
  • $r^2$ : coefficient of determination
  • SEb: standard error of the least squares slope
χ2 Test
Tests the independence of two categorical variables arranged in matrix form. The χ2 test for independence compares the observed matrix to the expected theoretical matrix. The χ2 distribution is used for the χ2 test.

・The minimum size of the matrix is 1×2. An error occurs if the matrix has only one column.
・The result of the expected frequency calculation is stored in the system variable named “Expected”.
$$ \chi^2 = \sum_{i=1}^{k}\sum_{j=1}^{l} \cfrac{(x_{ij}-F_{ij})^2}{F_{ij}} $$
$$ F_{ij}=\cfrac{ \sum_{i=1}^{k}x_{ij} \times \sum_{j=1}^{l}x_{ij} }{ \sum_{i=1}^{k} \sum_{j=1}^{l}x_{ij} } $$
$ x_{ij}$ : The element at row i, column j of the observed matrix
$ F_{ij}$ : The element at row i, column j of the expected matrix

・Input Terms

  • Matrix: name of matrix containing observed values (positive integers in all cells for 2×2 and larger matrices; positive real numbers for one row matrices)

・Output Terms

  • $\chi^2$ : $\chi^2$ value
  • Prob : p value
  • $df$ : degrees of freedom
  • Observed : the input matrix of observed values
  • Expected : the calculated matrix of expected values
χ2 GOF Test (χ2 Goodness-Of-Fit Test)
Tests whether the observed count of sample data fits a certain distribution. For example, it can be used to determine conformance with normal distribution or binomial distribution.

$\chi^2=\sum_{i}^{k} \cfrac{ (O_i - E_i )^2 }{E_i}$
$$Contrib = \{ \cfrac{ (O_1 - E_1 )^2 }{E_1} \ \cfrac{ (O_2 - E_2 )^2 }{E_2} \cdots \cfrac{ (O_k - E_k )^2 }{E_k} \} $$
$O_i$ : The i-th element of the observed list
$E_i$ : The i-th element of the expected list

・Input Terms

  • Observed list : name of list containing observed counts (all cells positive integers)
  • Expected list : name of list that is for saving expected frequency
  • $df$ : degrees of freedom

・Output Terms

  • $\chi^2$ : $\chi^2$ value
  • Prob : p value
  • $df$ : degrees of freedom
  • Contrib : name of list specifying the contribution of each observed count
Two-Sample F-Test
Tests the ratio between sample variances of two independent random samples. The F distribution is used for the Two-Sample F-Test.

$F=\cfrac{{S_{x_1}}^2}{{S_{x_2}}^2}$

Data type: Variable

・Input Terms

  • $\sigma_1$ condition: population standard deviation test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is smaller than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2.)
  • $s_{x_1}$ : sample standard deviation of sample 1 ($s_{x_1} > 0$)
  • $n_1$ : size of sample 1 (positive integer)
  • $s_{x_2}$ : sample standard deviation of sample 2 ($s_{x_2} > 0$)
  • $n_2$ : size of sample 2 (positive integer)

・Output Terms

  • $F$ : F value
  • Prob : p value

Data type: List

・Input Terms

  • $\sigma_1$ condition: population standard deviation test conditions (“≠” specifies two-tail test, “<” specifies one-tail test where sample 1 is smaller than sample 2, “>” specifies one-tail test where sample 1 is greater than sample 2.)
  • List(1) : list where sample 1 data is located
  • List(2) : list where sample 2 data is located
  • Freq(1) : frequency of sample 1 (1 or list name)
  • Freq(2) : frequency of sample 2 (1 or list name)

・Output Terms

  • $F$ : F value
  • Prob : p value
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ s_{x_{1}} $ : sample standard deviation of sample 1
  • $ s_{x_{2}} $ : sample standard deviation of sample 2
  • $ n_{1} $ : size of sample 1
  • $ n_{2} $ : size of sample 2
One-Way ANOVA (One-Way analysis of variance)
Tests the hypothesis that the population means of multiple populations are equal. It compares the mean of one or more groups based on one independent variable or factor.

・Input Terms

  • FactorList(A): list where levels of Factor A are located
  • DependentList: list where sample data is located

・Output Terms

  • A df : degrees of freedom of Factor A
  • A MS : mean square of Factor A
  • A SS : sum of squares of Factor A
  • A F : F value of Factor A
  • A p : p value of Factor A
  • Errdf : degrees of freedom of error
  • ErrMS : mean square of error
  • ErrSS : sum of squares of error
Two-Way ANOVA (Two-Way analysis of variance)
Tests the hypothesis that the population means of multiple populations are equal. It examines the effect of each variable independently as well as their interaction with each other based on a dependent variable.

・Input Terms

  • 2×2: data table type
  • FactorList(A): list where levels of Factor A are located
  • FactorList(B) : list where levels of Factor B are located
  • DependentList: list where sample data is located

・Output Terms

  • A df : degrees of freedom of Factor A
  • A MS : mean square of Factor A
  • A SS : sum of squares of Factor A
  • A F : F value of Factor A
  • A p : p value of Factor A
  • B df : degrees of freedom of Factor B
  • B MS : mean square of Factor B
  • B SS : sum of squares of Factor B
  • B F : F value of Factor B
  • B p : p value of Factor B
  • AB df : degrees of freedom of Factor A×Factor B
  • AB MS : mean square of Factor A×Factor B
  • AB SS : sum of squares of Factor A×Factor B
  • AB F : F value of Factor A×Factor B
  • AB p : p value of Factor A×Factor B
  • Errdf : degrees of freedom of error
  • ErrMS : mean square of error
  • ErrSS : sum of squares of error
One-Sample Z Interval
Calculates the confidence interval for the population mean based on a sample mean and known population standard deviation.
$Lower = \overline{x}-Z(\cfrac{\alpha}{2})\cfrac{\sigma}{\sqrt{n}}$
$Upper = \overline{x}+Z(\cfrac{\alpha}{2})\cfrac{\sigma}{\sqrt{n}}$
α is the significance level, and 100 (1 – α)% is the confidence level. When the confidence level is 95%, for example, you would input 0.95, which produces α = 1 – 0.95 = 0.05.

Data type: Variable

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • $ \overline{x} $ : sample mean
  • $ n $ : sample size (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)

Data type: List

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • List: list where sample data is located
  • Freq : frequency of sample (1 or list name)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $ \overline{x} $ : sample mean
  • $ s_{x} $ : sample standard deviation
  • $ n $ : sample size
Two-Sample Z Interval
Calculates the confidence interval for the difference between population means based on the difference between sample means when the population standard deviations are known.

$Lower = (\overline{x}_1-\overline{x}_2)-Z(\cfrac{\alpha}{2})\sqrt{ \cfrac{{\sigma_1}^2}{n_1} + \cfrac{{\sigma_2}^2}{n_2} }$
$Upper = (\overline{x}_1-\overline{x}_2)+Z(\cfrac{\alpha}{2})\sqrt{ \cfrac{{\sigma_1}^2}{n_1} + \cfrac{{\sigma_2}^2}{n_2} }$

Data type: Variable

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \sigma_{1} $ : population standard deviation of sample 1 ($ \sigma_{1} > 0 $)
  • $ \sigma_{2} $ : population standard deviation of sample 2 ($ \sigma_{2} > 0 $)
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ n_{1} $ : size of sample 1 (positive integer)
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ n_{2} $ : size of sample 2 (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)

Data type: List

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \sigma_{1} $ : population standard deviation of sample 1 ($ \sigma_{1} > 0 $)
  • $ \sigma_{2} $ : population standard deviation of sample 2 ($ \sigma_{2} > 0 $)
  • List(1) : list where sample 1 data is located
  • List(2) : list where sample 2 data is located
  • Freq(1) : frequency of sample 1 (1 or list name)
  • Freq(2) : frequency of sample 2 (1 or list name)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ s_{x_{1}} $ : sample standard deviation of sample 1
  • $ s_{x_{2}} $ : sample standard deviation of sample 2
  • $ n_{1} $ : size of sample 1
  • $ n_{2} $ : size of sample 2
One-Prop Z Interval (One-Proportion Z Interval)
Calculates the confidence interval for the population proportion based on a single sample proportion.

$Lower = \cfrac{x}{n}-Z(\cfrac{\alpha}{2})\sqrt{\cfrac{1}{n}(\cfrac{x}{n}(1-\cfrac{x}{n}))}$
$Upper = \cfrac{x}{n}+Z(\cfrac{\alpha}{2})\sqrt{\cfrac{1}{n}(\cfrac{x}{n}(1-\cfrac{x}{n}))}$

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ x $ : data (0 or positive integer)
  • $ n $ : sample size (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $\hat{p}$ : estimated sample proportion
Two-Prop Z Interval (Two-Proportion Z Interval)
Calculates the confidence interval for the difference between population proportions based on the difference between Two-Proportion Z Interval.

$ Lower = \cfrac{x_1}{n_1}-\cfrac{x_2}{n_2}-Z(\cfrac{\alpha}{2})\sqrt{ \cfrac{ \cfrac{x_1}{n_1}(1-\cfrac{x_1}{n_1}) }{n_1} + \cfrac{ \cfrac{x_2}{n_2}(1-\cfrac{x_2}{n_2}) }{n_2} } $
$ Upper = \cfrac{x_1}{n_1}-\cfrac{x_2}{n_2}+Z(\cfrac{\alpha}{2})\sqrt{ \cfrac{ \cfrac{x_1}{n_1}(1-\cfrac{x_1}{n_1}) }{n_1} + \cfrac{ \cfrac{x_2}{n_2}(1-\cfrac{x_2}{n_2}) }{n_2} } $

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $x_{1}$ : data value of sample 1 (integer, $x_{1}$ must be less than or equal to $n_{1}$)
  • $n_{1}$ : size of sample 1 (positive integer)
  • $x_{2}$ : data value of sample 2 (integer, $x_{2}$ must be less than or equal to $n_{2}$)
  • $n_{2}$ : size of sample 2 (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $\hat{p}_{1}$ : estimated proportion of sample 1
  • $\hat{p}_{2}$ : estimated proportion of sample 2
One-Sample t Interval
Calculates the confidence interval for the population mean based on a sample mean and a sample standard deviation when the population standard deviation is not known.

$Lower = \overline{x}-t_{n-1}(\cfrac{\alpha}{2})\cfrac{s_x}{\sqrt{n}}$
$Upper = \overline{x}+t_{n-1}(\cfrac{\alpha}{2})\cfrac{s_x}{\sqrt{n}}$

Data type: Variable

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \overline{x} $ : sample mean
  • $s_{x}$ : sample standard deviation ($ s_{x} \ge 0 $)
  • $n$ : sample size (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)

Data type: List

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • List: list where sample data is located
  • Freq : frequency of sample (1 or list name)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $ \overline{x} $ : sample mean
  • $ s_{x} $ : sample standard deviation
  • $ n $ : sample size
Two-Sample t Interval
Calculates the confidence interval for the difference between population means based on the difference between sample means and sample standard deviations when the population standard deviations are not known.

When the two population standard deviations are equal (pooled)
$$Lower = (\overline{x}_1-\overline{x}_2)-t_{n_1+n_2-2}(\cfrac{\alpha}{2})\sqrt{{s_p}^2(\cfrac{1}{n_1}+\cfrac{1}{n_2})}$$ $$Upper = (\overline{x}_1-\overline{x}_2)+t_{n_1+n_2-2}(\cfrac{\alpha}{2})\sqrt{{s_p}^2(\cfrac{1}{n_1}+\cfrac{1}{n_2})}$$
When the two population standard deviations are not equal (not pooled)
$$Lower = (\overline{x}_1-\overline{x}_2)-t_{df}(\cfrac{\alpha}{2})\sqrt{(\cfrac{{S_{x_1}}^2}{n_1}+\cfrac{{S_{x_2}}^2}{n_2})}$$
$$Upper = (\overline{x}_1-\overline{x}_2)+t_{df}(\cfrac{\alpha}{2})\sqrt{(\cfrac{{S_{x_1}}^2}{n_1}+\cfrac{{S_{x_2}}^2}{n_2})}$$
$$df = \cfrac{1}{\cfrac{C^2}{n_1-1} + \cfrac{(1-C)^2}{n_2-1}}$$
$$C=\cfrac{\cfrac{{S_{x_1}}^2}{n_1}}{(\cfrac{{S_{x_1}}^2}{n_1} + \cfrac{{S_{x_2}}^2}{n_2})}$$

Data type: Variable

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $s_{x_1}$ : sample standard deviation of sample 1 ($s_{x_1} > 0$)
  • $ n_{1} $ : size of sample 1 (positive integer)
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $s_{x_2}$ : sample standard deviation of sample 2 ($s_{x_2} > 0$)
  • $ n_{2} $ : size of sample 2 (positive integer)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $df$ : degrees of freedom
  • $s_p$ : pooled sample standard deviation

Data type: List

・Input Terms

  • C-Level : confidence level (0 ≤ C-Level < 1)
  • List(1) : list where sample 1 data is located
  • List(2) : list where sample 2 data is located
  • Freq(1) : frequency of sample 1 (1 or list name)
  • Freq(2) : frequency of sample 2 (1 or list name)
  • Pooled : On (equal variances) or Off (unequal variances)

・Output Terms

  • Lower : interval lower limit (left edge)
  • Upper : interval upper limit (right edge)
  • $df$ : degrees of freedom
  • $ \overline{x}_{1} $ : sample mean of sample 1 data
  • $ \overline{x}_{2} $ : sample mean of sample 2 data
  • $ s_{x_{1}} $ : sample standard deviation of sample 1
  • $ s_{x_{2}} $ : sample standard deviation of sample 2
  • $s_p$ : pooled sample standard deviation
  • $ n_{1} $ : size of sample 1
  • $ n_{2} $ : size of sample 2
Normal PD (Normal Probability Density)
Calculates the normal probability density for a specified value.
Specifying σ = 1 and μ= 0 produces standard normal distribution.
$$f(x)=\cfrac{1}{\sqrt{2\pi}\sigma}e^{-\cfrac{(x-\mu)^2}{2\sigma^2}} \qquad (\sigma>0)$$

・Input Terms

  • $ x $ : data value
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • $ \mu $ : population mean

・Output Terms

  • Prob : normal probability density
Normal CD (Normal Cumulative Distribution)
Calculates the cumulative probability of a normal distribution between a lower bound (a) and an upper bound (b).
$$p=\frac{1}{\sqrt{2\pi}\sigma}\int_a^b e^{ \scriptscriptstyle -\frac{(x-\mu)^2}{2\sigma^2} }dx$$

・Input Terms

  • Lower : lower bound
  • Upper : upper bound
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • $\mu$ : population mean

・Output Terms

  • Prob : normal distribution probability p
  • z Low : standardized lower limit z value
  • z Up : standardized upper limit z value
Student’s t PD (Student’s t Probability Density)
Calculates the Student’s t probability density for a specified value.

$$f(x)=\cfrac{\Gamma(\cfrac{df+1}{2})}{\Gamma(\cfrac{df}{2})}\cfrac{(1+\cfrac{x^2}{df})^{-\cfrac{df+1}{2}}}{\sqrt{\pi \cdot df}}$$

・Input Terms

  • $ x $ : data value
  • $df$ : degrees of freedom (df > 0)

・Output Terms

  • Prob : Student’s probability density
Student’s t CD (Student’s t Cumulative Distribution)
Calculates the cumulative probability of a Student’s t distribution between a lower bound (a) and an upper bound (b).
$$ p=\cfrac{\Gamma(\cfrac{df+1}{2})}{\Gamma(\cfrac{df}{2})\sqrt{\pi \cdot df}}\int_a^b (1+\cfrac{x^2}{df})^{-\cfrac{df+1}{2}}dx $$

・Input Terms

  • Lower : lower bound
  • Upper : upper bound
  • $df$ : degrees of freedom (df > 0)

・Output Terms

  • Prob : Student’s t distribution
  • t Low : lower bound value you input
  • t Up : upper bound value you input
χ2 PD (χ2 Probability Density)
Calculates the χ2 probability density for a specified value.
$$f(x)=\cfrac{1}{\Gamma(\cfrac{df}{2})}(\cfrac{1}{2})^{\cfrac{df}{2}}x^{\cfrac{df}{2}-1}e^{-\cfrac{x}{2}}$$

・Input Terms

  • $ x $ : data value
  • $df$ : degrees of freedom (positive integer)

・Output Terms

  • Prob : χ2 probability density
χ2 CD (χ2 Cumulative Distribution)
Calculates the cumulative probability of a χ2 distribution between a lower bound and an upper bound. $$p=\cfrac{1}{\Gamma(\cfrac{df}{2})}(\cfrac{1}{2})^{\cfrac{df}{2}}\int_a^b x^{\cfrac{df}{2}-1}e^{-\cfrac{x}{2}}dx$$

・Input Terms

  • Lower : lower bound
  • Upper : upper bound
  • $df$ : degrees of freedom (positive integer)

・Output Terms

  • Prob : χ2 distribution probability
F PD (F Probability Density)
Calculates the F probability density for a specified value.

$$f(x)=\cfrac{\Gamma(\cfrac{n+d}{2})}{\Gamma(\cfrac{n}{2})\Gamma(\cfrac{d}{2})}(\cfrac{n}{d})^{\cfrac{n}{2}}x^{\cfrac{n}{2}-1}(1+\cfrac{n \cdot x}{d})^{-\cfrac{n+d}{2}}$$

・Input Terms

  • $ x $ : data value
  • $ n:df $ : degrees of freedom of numerator (positive integer)
  • $ d:df $ : degrees of freedom of denominator (positive integer)

・Output Terms

  • Prob : F probability density
F CD (F Cumulative Distribution)
Calculates the cumulative probability of an F distribution between a lower bound and an upper bound. $$p=\cfrac{\Gamma(\cfrac{n+d}{2})}{\Gamma(\cfrac{n}{2})\Gamma(\cfrac{d}{2})}(\cfrac{n}{d})^{\cfrac{n}{2}}\int_a^b x^{\cfrac{n}{2}-1}(1+\cfrac{n \cdot x}{d})^{-\cfrac{n+d}{2}}dx$$

・Input Terms

  • Lower : lower boundary
  • Upper : upper boundary
  • $ n:df $ : degrees of freedom of numerator (positive integer)
  • $ d:df $ : degrees of freedom of denominator (positive integer)

・Output Terms

  • Prob : F distribution probability
Binomial PD (Binomial Distribution Probability)
Calculates the probability in a binomial distribution that success will occur on a specified trial.
$$f(x)={}_nC_xp^x(1-p)^{n-x} \qquad (x=0,1,\cdots \cdots,n)$$
$p$ : probability of success (0 ≤ p ≤ 1)
$n$ : number of trials

・Input Terms

  • $ x $ : specified trial (integer from 0 to n)
  • Numtrial : number of trials n (integer, n ≥ 0)
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • Prob : binomial probability
Binomial CD (Binomial Cumulative Distribution)
Calculates the cumulative probability in a binomial distribution that success will occur on or before a specified trial.

・Input Terms

  • Lower : lower boundary
  • Upper : upper boundary
  • Numtrial : number of trials n (integer, n ≥ 1)
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • Prob : binomial cumulative probability
Poisson PD (Poisson Distribution Probability)
Calculates the probability in a Poisson distribution that success will occur on a specified trial.
$$f(x)=\cfrac{e^{-\lambda} \lambda^x}{x!} \qquad (x=0,1,2,\cdots)$$

・Input Terms

  • $ x $ : specified trial (integer, x ≥ 0)
  • $ \lambda $ : mean ( λ > 0 )

・Output Terms

  • Prob : Poisson probability
Poisson CD (Poisson Cumulative Distribution)
Calculates the cumulative probability in a Poisson distribution that success will occur on or before a specified trial.

・Input Terms

  • Lower : lower boundary
  • Upper : upper boundary
  • $ \lambda $ : mean ( λ > 0 )

・Output Terms

  • Prob : Poisson cumulative probability
Geometric PD (Geometric Distribution Probability)
Calculates the probability in a geometric distribution that the success will occur on a specified trial. $$ f(x)=p(1-p)^{x-1} \qquad (x=1,2,3,\cdots) $$

・Input Terms

  • $ x $ : specified trial (positive integer)
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • Prob : geometric probability
Geometric CD (Geometric Cumulative Distribution)
Calculates the cumulative probability in a geometric distribution that the success will occur on or before a specified trial.

・Input Terms

  • Lower : lower boundary
  • Upper : upper boundary
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • Prob : geometric cumulative probability
Hypergeometric PD (Hypergeometric Distribution Probability)
Calculates the probability in a hypergeometric distribution that the success will occur on a specified trial.
$$ prob = \cfrac{ {}_MC_x \times {}_{N-M}C_{n-x} }{ {}_NC_n } $$

・Input Terms

  • $x$ : specified trial (integer)
  • $n$ : number of trials from population (0 ≤ n integer)
  • $M$ : number of successes in population (0 ≤ M integer)
  • $N$ : population size (nN, MN integer)

・Output Terms

  • Prob : hypergeometric probability
Hypergeometric CD (Hypergeometric Cumulative Distribution)
Calculates the cumulative probability in a hypergeometric distribution that the success will occur on or before a specified trial.
$$ prob = \sum_{i=Lower}^{Upper}\frac{ {}_MC_i \times {}_{N-M}C_{n-i} }{ {}_NC_n } $$

・Input Terms

  • Lower : lower boundary
  • Upper : upper boundary
  • $n$ : number of trials from population (0 ≤ n integer)
  • $M$ : number of successes in population (0 ≤ M integer)
  • $N$ : population size (nN, MN integer)

・Output Terms

  • Prob : hypergeometric cumulative probability
Inverse Normal CD (Inverse Normal Cumulative Distribution)
Calculates the boundary value(s) of a normal cumulative probability distribution for specified values.

Tail: Left
$$ \int_{-\infty}^{\alpha}f(x)dx=p $$
Upper bound α is returned.

Tail: Right
$$ \int_{\alpha}^{+\infty}f(x)dx=p $$
Lower bound α is returned.

Tail: Center
$$ \int_{\alpha}^{\beta}f(x)dx=p \qquad (\mu=\cfrac{\alpha+\beta}{2}) $$
Lower bound α and upper bound β are returned.

・Input Terms

  • Tail setting: probability value tail specification (Center, Left, Right)
  • Prob : probability value (0 ≤ Area ≤ 1)
  • $ \sigma $ : population standard deviation ($ \sigma > 0 $)
  • $ \mu $: population mean

・Output Terms

  • x1InvN : Upper bound when Tail:Left. Lower bound when Tail:Right or Tail:Center
  • x2InvN: Upper bound when Tail:Center
Inverse t CD (Inverse Student’s t Cumulative Distribution)
Calculates the lower bound value of a Student’s t cumulative probability distribution for specified values.
$$ \int_{\alpha}^{+\infty}f(x)=p $$

・Input Terms

  • Prob : t cumulative probability (0 ≤ Area ≤ 1)
  • $df$ : degrees of freedom (df > 0)

・Output Terms

  • xInv: inverse t cumulative distribution
Inverse χ2 CD (Inverse χ2 Cumulative Distribution)
Calculates the lower bound value of a χ2 cumulative probability distribution for specified values.
$$ \int_{\alpha}^{+\infty}f(x)=p $$

・Input Terms

  • Prob : χ2 cumulative probability (0 ≤ Area ≤ 1)
  • $df$ : degrees of freedom (positive integer))

・Output Terms

  • xInv: inverse χ2 cumulative distribution
Inverse F CD (Inverse F Cumulative Distribution)
Calculates the lower bound value of an F cumulative probability distribution for specified values.
$$ \int_{\alpha}^{+\infty}f(x)=p $$

・Input Terms

  • Prob : F cumulative probability (0 ≤ Area ≤ 1)
  • $n:df$ : degrees of freedom of numerator (positive integer)
  • $d:df$ : degrees of freedom of denominator (positive integer)

・Output Terms

  • xInv: inverse F cumulative distribution
Inverse Binomial CD (Inverse Binomial Cumulative Distribution)
Calculates the minimum number of trials of a binomial cumulative probability distribution for specified values.
$$ \sum_{x=0}^{m}f(x)\ge prob $$

・Input Terms

  • Prob : binomial cumulative probability (0 ≤ Area ≤ 1)
  • Numtrial : number of trials n (integer, n ≥ 0)
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • xInv : inverse binomial cumulative distribution
Inverse Poisson CD (Inverse Poisson Cumulative Distribution)
Calculates the minimum number of trials of a Poisson cumulative probability distribution for specified values.
$$ \sum_{x=0}^{m}f(x)\ge prob $$

・Input Terms

  • Prob : Poisson cumulative probability (0 ≤ Area ≤ 1)
  • $ \lambda $ : mean ( λ > 0 )

・Output Terms

  • xInv : inverse Poisson cumulative distribution
Inverse Geo CD (Inverse Geometric Cumulative Distribution)
Calculates the minimum number of trials of a geometric cumulative probability distribution for specified values.
$$ \sum_{x=0}^{m}f(x)\ge prob $$

・Input Terms

  • Prob : geometric cumulative probability (0 ≤ Area ≤ 1)
  • pos : probability of success p (0 ≤ p ≤ 1)

・Output Terms

  • xInv : inverse geometric cumulative distribution
Inverse Hypergeometric (Inverse Hypergeometric Cumulative Distribution)
Calculates the minimum number of trials of a hypergeometric cumulative probability distribution for specified values.
$$ prob \le \sum_{i=0}^{X} \cfrac{ {}_MC_i \times {}_{N-M}C_{n-i} }{ {}_NC_n } $$

・Input Terms

  • Prob : hypergeometric cumulative probability (0 ≤ Area ≤ 1)
  • $n$ : number of trials from population (0 ≤ n integer)
  • $M$ : number of successes in population (0 ≤ M integer)
  • $N$ : population size (nN, MN integer)

・Output Terms

  • xInv: inverse hypergeometric cumulative distribution
Scatter Plot
This plot compares the data accumulated ratio with a normal distribution accumulated ratio. If the scatter plot is close to a straight line, then the data is approximately normal. A departure from the straight line indicates a departure from normality.
Box & Whisker Plot
This type of graph lets you see how a large number of data items are grouped within specific ranges. A box encloses all the data in an area from the first quartile ($Q1$) to the third quartile ($Q3$), with a line drawn at the median ($Med$). Lines (called whiskers) extend from either end of the box up to the minimum ($minX$) and maximum ($maxX$) of the data.
Histogram
A histogram shows the frequency (frequency distribution) of each data class as a rectangular bar. Classes are on the horizontal axis, while frequency is on the vertical axis. You can change the start value ($HStart$) and step value ($HStep$) of the histogram, if you want.
Pie Chart
You can draw a circle graph based on the data in a specific list.
Dot Plot
The values in Column A (horizontal axis) represents bin numbers, while the values in Column B (vertical axis) represent the data point count in each bin. A dot is plotted for each data point in a bin.
Normal CD
Calculates the cumulative probability of a normal distribution between a lower bound and an upper bound.