17.1.2.1 The Statistics on Rows Dialog Box
Contents
Supporting Information
Input
| Input Data |
Specify the data range to be performed:
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Quantities
Moments
- Let \(x_i\) be the ith sample and \(w_i\) be the ith weight.
| N Total | Total number of data points, denoted by n |
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| N Missing | Number of missing values |
| Mean | The mean (average) score
\(\bar{x}=\frac 1n\sum_{i=1}^n x_i\). |
| Standard deviation | \(s=\sqrt{\sum_{i=1}^n (x_i-\bar{x})^2/d}\)
where \(d=n-1 \,\) Note: In OriginPro, \(d\) has another option, defined in the Variance Divisor of Moment branch. |
| SE of Mean | Standard error of mean:
\[\frac S{\sqrt{n}}\] |
| Lower 95% CI of Mean | Lower limit of the 95% confidence interval of mean
\[\bar{x}-t_{(1-\alpha/2)}\frac{s}{\sqrt{n}}\] where \(t_{(1-\alpha/2)}\) is the \(1-\alpha/2\) critical value of the Student's t-statistic with n-1 degrees of freedom |
| Upper 95% CI of Mean | Upper limit of the 95% confidence interval of mean
\[\bar{x}+t_{(1-\alpha/2)}\frac{s}{\sqrt{n}}\] where \(t_{(1-\alpha/2)}\) is the \(1-\alpha/2\) critical value of the Student's t-statistic with n-1 degrees of freedom |
| Variance | \[s^2\] |
| Sum | \(\sum_{i=1}^n x_i\). |
| Skewness |
Skewness measures the degree of asymmetry of a distribution. It is defined as \[\gamma_1=\frac n{(n-1)(n-2)}\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^3 ,\mbox{for DF}\] \[\gamma_1=\frac 1n\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^3,\mbox{for N}\] \[\gamma_1=\frac 1n\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^3,\mbox{for WVR}\] |
| Kurtosis |
Kurtosis depicts the degree of peakedness of a distribution. \[\gamma_2=\frac{n(n+1)}{(n-1)(n-2)(n-3)}\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^4-\frac{3(n-1)^2}{(n-2)(n-3)},\mbox{for DF}\] \[\gamma_2=\frac 1n\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^4 -3,\mbox{for N}\] \[\gamma_2=\frac 1n\sum_{i=1}^n (\frac{x_i-\bar{x}}s)^4 -3,\mbox{for WVR}\] |
| Uncorrected Sum of Squares |
\[\sum_{i=1}^n x_i^2\] |
| Corrected Sum of Squares |
\[\sum_{i=1}^n (x_i-\bar{x})^2\] |
| Coefficient of Variance |
\[\frac s{\bar{x}}\] |
| Mean absolute Deviation |
\[\frac{\sum_{i=1}^n |x_i-\bar{x}|}n\] |
| SD times 2 |
Standard deviation times 2. \[2s \,\] |
| SD times 3 |
Standard deviation times 3. \[3s \,\] |
| Geometric Mean |
\[\bar{x}_g=\left( \prod_{i=1}^n x_i\right) ^{\frac 1n}\] |
| Geometric SD |
The geometric standard deviation \(e^{std(\log x_i)}\) Where std is the unweighted sample standard deviation. Note: Weights are ignored for the geometric standard deviation. |
| Mode |
The mode is the element that appears most often in the data range. If multiple modes are found, the smallest will be chosen. |
| Harmonic Mean |
harmonic mean (sometimes called the subcontrary mean) without weight: \(\frac n{\frac 1{x_1} + \frac 1{x_2} + ... + \frac 1{x_n}}=\left(\frac {\sum_{i=1}^n (x_i)^{-1}}n\right)^{-1}\) with weight: \(\frac {\sum_{i=1}^n w_i}{\sum_{i=1}^n \frac {w_i}{x_i}}=\left(\frac {\sum_{i=1}^n w_i x_i^{-1}}{\sum_{i=1}^n w_i}\right)^{-1}\)
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Quantiles
Quantiles are values from the data, below which is a given proportion of the data points in a given set. For example, 25% of data points in any set of data lay below the first quartile, and 50% of data points in a set lay below the second quartile, or median.
Sort the input dataset in ascending order. Let \(x_{(i)}\,\!\) be the ith element of the reordered dataset
| Minimum | \[x_{(i)}\,\!\] |
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| Index of Minimum |
The index number of Minimum in the original (input) dataset. |
| 1st Quartile (Q1) | First (25%) quantile, Q1. See Interpolation of quantiles for computational methods. |
| Median | Median or second (50%) quantile, Q2. See Interpolation of quantiles for computational methods. |
| 3rd Quartile (Q3) | Third (75%) quantile, Q3. See Interpolation of quantiles for computational methods. |
| Maximum | \[x_{(n)}\,\!\] |
| Index of Maximum |
The index number of Maximum in the original (input) dataset. |
| Interquartile Range (Q3-Q1) |
\[Q_3-Q_1\,\] |
| Range (Maximum-Minimum) |
Maximum - Minimum |
| Custom Percentile(s) |
Request computation of custom percentiles. |
| Percentile list |
This option is only available when Custom Percentile(s) is checked. Percentiles are computed for all the values listed. |
| Median Absolute Deviation | For a univariate data set X1, X2, ..., Xn, the MAD is defined as the median of the absolute deviations from the data's median:
\[MAD = median(|{X_i} - median(X)|)\,\] that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values. |
| Robust Coefficient of Variation |
\[(MAD/norminv(0.75))/Median\,\] |
Computation Control
Variance Divisor of Moment
- Controls computation of variance divisor d
| DF | Degree of freedom
\[d=n-1\,\!\] |
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| N | Number of non-missing observations.
\[d=n\,\!\] |
Interpolation of quantiles
- This option decides the methods for calculating Q1, Q2, and Q3.
- Let the ith percentile be y, set \(p=i/100\), and let
\[\begin{cases} (n+1)p=j+g, & \mbox{for Weighted Average Right}\\ np=j+g, & \mbox{for other methods} \end{cases}\]
- where j is the integer part of np, and g is the fractional part of np, then different methods define the \(i^{th}\,\!\) percentile, y, as described by the following:
| Empirical Distribution with Averaging | \[y=\begin{cases} \frac{1}{2}(x_{(j)}+x_{(j+1)}), & \mbox{if }g=0\\ x_{(j+1)}, & \mbox{if }g>0 \end{cases}\] |
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| Nearest Neighbor | Observation numbered closest to np
\[y=\begin{cases} x_{(k)}, & \mbox{if }g\ne \frac{1}{2}\\ x_{(j)}, & \mbox{if }g=\frac{1}{2} \mbox{ and } j\mbox{ is even} \\ x_{(j+1)}, & \mbox{if }g=\frac{1}{2} \mbox{ and } j\mbox{ is odd} \end{cases}\] where k is the integer part of \(np+\frac{1}{2}\) |
| Empirical Distribution | \[y=\begin{cases} x_{(j)}, & \mbox{if }g=0 \\ x_{(j+1)}, & \mbox{if }g>0 \end{cases}\] |
| Weighted Average Right | weighted average aimed at \(x_{(n+1)+p)}\,\!\)
\[y=(1-g)x_{(j)}+gx_{(j+1)}\,\!\] where \(x_{(n+1)}\,\!\)is taken to be \(x_{(n)}\,\!\) |
| Weighted Average Left | weighted average aimed at\(x_{(np)}\,\!\)
\[y=(1-g)x_{(j)}+gx_{(j+1)}\,\!\] where \(x_{(0)}\) is taken to be\(x_{(1)}\) |
| Tukey Hinges | Let:
\(m=\begin{cases} \frac{n}{2},& \mbox{if }n\mbox{ is even}\\ \frac{n+1}{2},& \mbox{if }n\mbox{ is odd} \end{cases}\) \(k=\begin{cases} \frac{m}{2},& \mbox{if }m\mbox{ is even}\\ \frac{m+1}{2},& \mbox{if }m\mbox{ is odd} \end{cases}\) Then we have: \(Minimum+x_{(1)}\,\!\) \(Q_1=\begin{cases} x_{(k)},& \mbox{if }m\mbox{ is odd}\\ \frac{1}{2}(x_{(k)}+x_{(k+1)}), & \mbox{if }m\mbox{ is even} \end{cases}\) \[Q_2=\begin{cases} x_{(m)},& \mbox{if }n\mbox{ is odd}\\ \frac{1}{2}(x_{(m)}+x_{(m+1)}), & \mbox{if }m\mbox{ is even} \end{cases}\] \[Q_3=\begin{cases} x_{(n-k-1)},& \mbox{if }n\mbox{ is odd}\\ \frac{1}{2}(x_{(n-k)}+x_{(mn-k+1)}), & \mbox{if }m\mbox{ is even} \end{cases}\] \[Maximum=x_{(n)}\,\!\] Note: if this method is selected, only quartiles will be computed. Custom percentiles are disabled. |
Output
| Report Tables | Specifies the destination of report worksheet tables
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