2.69.2.2 Algorithm for Capability Analysis

Normal Capability Analysis

Standard Deviation Estimation

Within-subgroup and overall standard deviations are estimated for normal capability analysis.

  • Within-subgroup Standard Deviation (\sigma_{within})
    According to the subgroup size (bigger than 1, or equal to 1), estimating method is different.
    • Subgroup Size > 1
      • Average of Subgroup Ranges (Rbar)
        \sigma_{within}=S_r=\frac{\sum_{i=1}^N\frac{f_ir_i}{d_2(n_i)}}{\sum_{i=1}^Nf_i}, where f_i = \frac{(d_2(n_i))^2}{(d_3(n_i))^2}
        N: Number of subgroups
        r_i: The range of the ith subgroup, r_i=\max(ith\_subgroup\_observations)-\min(ith\_subgroup\_observations)
        n_i: Number of observations in the ith subgroup
        d_2(n_i), d_3(n_i): Unbiasing constant, d2(), d3()
      • Average of Subgroup Standard Deviations (Sbar)
        Unbiased \sigma_{within}=\bar{S}=\frac{\sum_{i=1}^N\frac{h_iS_i}{c_4(n_i)}}{\sum_{i=1}^Nh_i}, where h_i=\frac{(c_4(n_i))^2}{1-(c_4(n_i))^2}
        Not use unbiasing constant, \sigma_{within}=\frac{\sum_{i=1}^NS_i}{N}
        N: Number of subgroups
        S_i: The standard deviation of the ith subgroup
        n_i: Number of observations in the ith subgroup
        c_4(n_i): Unbiasing constant, c4()
      • Pooled Standard Deviation
        Unbiased \sigma_{within}=\frac{S_p}{c_4(d+1)}, where S_p=\sqrt{\frac{\sum_{i=1}^N\sum_{j=1}^{n_i}(X_{ij}-\bar{X}_i)^2}{\sum_{i=1}^N(n_i-1)}}, c_4(d+1)=\frac{\Gamma{(\frac{d+1}{2})}}{\Gamma{(\frac{d}{2})}}\sqrt{\frac{2}{d}}
        Not use unbiasing constant, \sigma_{within}=S_p
        N: Number of subgroups
        n_i: Number of observations in the ith subgroup
        X_{ij}: The jth observation in the ith subgroup
        \bar{X_i}: The Mean of the ith subgroup
        d: Degrees of freedom for S_p, d=\sum_{i=1}^N(n_i-1)
        c_4(d+1): Unbiasing constant, c4()
        \Gamma(): Gamma function
    • Subgroup Size = 1
      • Average of Moving Range
        \sigma_{within}=\frac{R_w+\cdots+R_N}{(N-w+1)d_2(w)}
        N: The number of all observations
        w: The number of observations used in the moving range
        R_i: The ith moving range. R_i=\max{(X_i,\cdots,X_{i-w+1})}-\min{(X_i,\cdots,X_{i-w+1})}, i=w,\cdots,N, and X_i is the ith observation
        d_2(w): Unbiasing constant, d2()
      • Median of Moving Range
        \sigma_{within}=\frac{\overline{MR}}{d_4(w)}
        N: The number of all observations
        w: The number of observations used in the moving range
        MR_i: The ith moving range. MR_i=\max{(X_i,\cdots,X_{i-w+1})}-\min{(X_i,\cdots,X_{i-w+1})}, i=w,\cdots,N, and X_i is the ith observation
        \overline{MR}: The median of the MR_i, i=w,\cdots,N
        d_4(w): Unbiasing constant, d4()
      • Square Root of Mean Squared Successive Differences (MSSD)
        Unbiased \sigma_{within}=MSSD=\frac{\sqrt{\frac{\sum_{i=1}^{N-1}d_i^2}{2(N-1)}}}{c_4'(N)}
        Not use unbiasing constant, \sigma_{within}=\sqrt{\frac{\sum_{i=1}^{N-1}d_i^2}{2(N-1)}}
        N: Number of observations
        d_i: Succesive differences of observations
        c_4'(N): Unbiasing constant, c4'()
  • Ovarall Standard Deviation (\sigma_{overall})
    Unbiased \sigma_{overall}=\frac{S}{c_4(N)}, where S=\sqrt{\frac{\sum_{i=1}^N(X_{i}-\bar{X})^2}{N-1}}
    Not use unbiasing constant, \sigma_{within}=S
    N: Number of all observations
    X_{i}: The ith observation
    \bar{X}: The Mean of the all observations
    c_4(N): Unbiasing constant, c4()

Potential Capability

  • Cp
    Cp=\frac{USL-LSL}{Toler*\sigma_{within}}
    USL, LSL: Upper and lower specification limits respectively
    Toler: Multiplier of the sigma tolerance
    \sigma_{within}: Within-subgroup standard deviation
  • (1-\alpha)100\% Confidence Interval Bounds for Cp
    LowerBound = Cp\sqrt{\frac{\chi_{\alpha/2,\nu}^2}{\nu}}
    LowerBound = Cp\sqrt{\frac{\chi_{1-\alpha/2,\nu}^2}{\nu}}
    \alpha: Alpha for the confidence level
    \nu: Degrees of freedom
    \chi_{\alpha,\nu}: \alpha percentile of chi-square distribution with \nu degrees of freedom
    \nu is calculated differently based on the method used for standard deviation.
    • Average of Subgroup Ranges (Rbar): \nu=0.9k(n-1)
    • Average of Subgroup Standard Deviations (Sbar): \nu=f_nk(n-1)
    • Pooled standard deviation: \nu=\sum(n_i-1)
    • Average of Moving Range or Median of Moving Range: \nu\approx k-R_{span}+1
    • Square Root of MSSD: \nu=k-1
      where n_i is the ith subgroup size, k is number of subgroups, R_{span} is the length of the moving range, n is the mean of subgroup size, n=\frac{\sum n_i}{k}, and f_n is calculated according to n as follows:
n 2 3 4 5 6,7 8,9 10-17 18-64 > 64
f_n 0.88 0.92 0.94 0.95 0.96 0.97 0.98 0.99 1
  • CPL
    CPL=\frac{\bar{X}-LSL}{\frac{Toler}{2}*\sigma_{within}}
    \bar{X}: Process mean estimated from observations or historical value
    LSL: Lower specification limit
    Toler: Multiplier of the sigma tolerance
    \sigma_{within}: Within-subgroup standard deviation
  • CPU
    CPU=\frac{USL-\bar{X}}{\frac{Toler}{2}*\sigma_{within}}
    \bar{X}: Process mean estimated from observations or historical value
    USL: Upper specification limit
    Toler: Multiplier of the sigma tolerance
    \sigma_{within}: Within-subgroup standard deviation
  • Cpk
    Cpk=\min(CPU, CPL)
  • (1-\alpha)100\% Confidence Interval Bounds for Cpk
    LowerBound = Cpk - Z_{1-\alpha/2}\sqrt{\frac{1}{N(\frac{Toler}{2})^2}+\frac{Cpk^2}{2\nu}}
    UpperBound = Cpk + Z_{1-\alpha/2}\sqrt{\frac{1}{N(\frac{Toler}{2})^2}+\frac{Cpk^2}{2\nu}}
    N: Total number of observations
    \alpha: Alpha for the confidence level
    \nu: Degrees of freedom, for details, please refer to (1-\alpha)100\% Confidence Interval Bounds for Cp above
    Toler: Multiplier of the sigma tolerance
    Z_{1-\alpha/2}: 1-\alpha/2 percentile from the standard normal distribution


  • CCpk
    CCpk=\left\{\begin{array}{ll}\frac{USL-\hat{\mu}}{\frac{Toler}{2}*\sigma_{within}} &Only\;USL\;Valid\cr\frac{\hat{\mu}-LSL}{\frac{Toler}{2}*\sigma_{within}} &Only\;LSL\;Valid\cr\frac{\min{(USL-\hat{\mu}, \hat{\mu}-LSL)}}{\frac{Toler}{2}*\sigma_{within}} &Both\;USL\;and\;LSL\;Valid\end{array}\right.
    \hat{\mu}: Estimated mean, \hat{\mu} = \left\{\begin{array}{ll}Target & Target\;is\;specified\cr\frac{USL+LSL}{2}&USL\;and\;LSL\;Valid,Target\;is\;not\;specified\cr \bar{X}&Otherwise\end{array}\right.
    USL, LSL: Upper and lower specification limits respectively
    Toler: Multiplier of the sigma tolerance
    \sigma_{within}: Within-subgroup standard deviation
    \bar{X}: Mean of observations

Overall Capability

  • Pp
    Pp = \frac{USL-LSL}{Toler*\sigma_{overall}}
    USL, LSL: Upper and Lower specification limits respectively
    Toler: Multiplier of the sigma tolerance
    \sigma_{overall}: Overall standard deviation
  • (1-\alpha)100\% Confidence Interval Bounds for Pp
    LowerBound = Pp\sqrt{\frac{\chi_{\alpha/2,\nu}^2}{\nu}}
    UpperBound = Pp\sqrt{\frac{\chi_{1-\alpha/2,\nu}^2}{\nu}}
    \alpha: Alpha for the confidence level
    \nu: Degrees of freedom, \nu=N-1
    N: Number of observations
    \chi_{\alpha, \nu}^2: \alpha percentile of the chi-square distribution with \nu degrees of freedom
  • PPL
    PPL = \frac{\bar{X}-LSL}{(Toler/2)*\sigma_{overall}}
    \bar{X}: Process mean, can be historical value, or calculated from observations
    LSL: Lower specification limit
    Toler: Multiplier of the sigma tolerance
    \sigma_{overall}: Overall standard deviation
  • PPU
    PPU = \frac{USL-\bar{X}}{(Toler/2)*\sigma_{overall}}
    \bar{X}: Process mean, can be historical value, or calculated from observations
    USL: Upper specification limit
    Toler: Multiplier of the sigma tolerance
    \sigma_{overall}: Overall standard deviation
  • Ppk
    Ppk = \min(PPU, PPL)
  • (1-\alpha)100\% Confidence Interval Bounds for Ppk
    LowerBound = Ppk - Z_{1-\alpha/2}\sqrt{\frac{1}{N(\frac{Toler}{2})^2} + \frac{Ppk^2}{2\nu}}
    UpperBound = Ppk + Z_{1-\alpha/2}\sqrt{\frac{1}{N(\frac{Toler}{2})^2} + \frac{Ppk^2}{2\nu}}
    N: Number of observations
    \alpha: Alpha for the confidence level
    \nu: Degrees of freedom, \nu=N-1
    Toler: Multiplier of the sigma tolerance
    Z_{1-\alpha/2}: The 1-\alpha/2 percentile from the standard normal distribution
  • Cpm
    When Target is specified, it is able to calculate Cpm using USL, LSL and Target.
    Cpm = \left\{\begin{array}{ll}\frac{USL-LSL}{Toler*\sqrt{\frac{\sum_{i=1}^K\sum_{j=1}^{n_i}(X_{ij}-Target)^2}{\sum_{i=1}^Kn_i}}}&USL,LSL\;Valid\;and\;Target=m\cr\frac{\min(Target-LSL, USL-Target)}{\frac{Toler}{2}*\sqrt{\frac{\sum_{i=1}^K\sum_{j=1}^{n_i}(X_{ij}-Target)^2}{\sum_{i=1}^Kn_i}}}&USL,LSL\;Valid\;and\;Target\neq m\cr\frac{USL-Target}{\frac{Toler}{2}*\sqrt{\frac{\sum_{i=1}^K\sum_{j=1}^{n_i}(X_{ij}-Target)^2}{\sum_{i=1}^Kn_i}}}&Only\;USL\;and\;Target\;Valid\cr\frac{Target-LSL}{\frac{Toler}{2}*\sqrt{\frac{\sum_{i=1}^K\sum_{j=1}^{n_i}(X_{ij}-Target)^2}{\sum_{i=1}^Kn_i}}}&Only\;LSL\;and\;Target\;Valid\cr NANUM &Otherwise\end{array}\right.
    USL, LSL: Upper and Lower specification limits respectively
    Target: Target value
    Toler: Multiplier of the sigma tolerance
    m: Midpoint between USL and LSL
    n_i: Number of observations in ith subgroup
    X_{ij}: The jth observation in the ith subgroup
    K: Number of subgroups
    NANUM: Missing value
  • (1-\alpha)100\% Confidence Interval Bounds for Cpm
    • Two-Sided
      LowerBound = Cpm\sqrt{\frac{\chi_{\alpha/2, \nu}^2}{\nu}}
      UpperBound = Cpm\sqrt{\frac{\chi_{1-\alpha/2, \nu}^2}{\nu}}
    • One-Sided
      LowerBound = Cpm\sqrt{\frac{\chi_{\alpha, \nu}^2}{\nu}}
      \nu: Degrees of freedom, \nu = \frac{N((1+a^2)^2}{1+2a^2}, where a = (Mean-Target)/\sigma_{overall}, and N is the number of observations
      \alpha: Alpha for the confidence level
      \chi_{\alpha,\nu}^2: \alpha quantile of the chi-square distribution with \nu degrees of freedom

Benchmark Zs for Potential Capability

  • Z.LSL, Z.USL, and Z.Bench
    Z.LSL = \frac{\bar{X}-LSL}{\sigma_{within}}
    Z.USL = \frac{USL-\bar{X}}{\sigma_{within}}
    Z.Bench = \Phi^{-1}(1-P_1-P_2)
    \bar{X}: Process mean, estiimated from data, or historical mean
    LSL, USL: Lower and upper specification limits
    P_1=Prob(X<LSL)=1-\Phi(Z.LSL)
    P_2=Prob(X>USL)=1-\Phi(Z.USL)
    \Phi(X): Cumulative distribution function of standard normal distribution
    \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
    \sigma_{within}: Within subgroups standard deviation
  • Confidence Intervals for Z.Bench With Two Specification Limits
    • Two-Sided
      LowerBound = -\Phi^{-1}(U)
      UpperBound = -\Phi^{-1}(L)
      where
      L=\frac{\exp(L(p)-Z_{1-\alpha/2}\sqrt{V})}{1+\exp(L(p)-Z_{1-\alpha/2}\sqrt{V})}
      U=\frac{\exp(L(p)+Z_{1-\alpha/2}\sqrt{V})}{1+\exp(L(p)+Z_{1-\alpha/2}\sqrt{V})}
      L(p)=\ln(p/(1-p))
      p=\left(1-\Phi(\frac{\bar{X}-LSL}{s})\right)+\left(1-\Phi(\frac{USL-\bar{X}}{s})\right)
      V=V(p)=\left(\frac{\partial{L(p)}}{\partial{\mu}}\right)^2\frac{\sigma^2}{N}+\left(\frac{\partial{L(p)}}{\partial{\sigma^2}}\right)^2\frac{2\sigma^4}{\nu}
      \frac{\partial{L(p)}}{\partial{\mu}}=\frac{1}{p(1-p)}\frac{\partial{p}}{\partial{\mu}}=\frac{1}{p(1-p)}\frac{1}{\sigma}\left(\varphi(\frac{USL-\mu}{\sigma})-\varphi(\frac{LSL-\mu}{\sigma})\right)
      \frac{\partial{L(p)}}{\partial{\sigma^2}}=\frac{1}{p(1-p)}\frac{\partial{p}}{\partial{\sigma^2}}=\frac{1}{2p(1-p)}\left(\frac{USL-\mu}{\sigma^3}\varphi(\frac{USL-\mu}{\sigma})-\frac{LSL-\mu}{\sigma^3}\varphi(\frac{LSL-\mu}{\sigma})\right)
      \sigma=s, \mu=\bar{X}
      N: Total number of obsevations
      p: Tail probabilities outside of the specification limits
      Z_{1-\alpha/2}: (1-\alpha/2)^{th} percential of standard normal distribution
      \alpha: Alpha for the confidence level
      \bar{X}: Process mean, estimated from data, or historical mean
      LSL, USL: Lower and upper specification liimits
      s: Within subgroups standard deviation
      \nu: Degrees of freedom for s
      \Phi(X): Cumulative distribution function of standard normal distribution
      \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
      \varphi(X): Probability density function of standard normal distribution
    • One-Sided
      Refer to the Two-Sided above, and change 1-\alpha/2 to 1-\alpha in the definition of L for UpperBound.
  • Confidence Intervals for Z.Bench With One Specification Limit
    • Lower Specification Limit and Two-Sided
      LowerBound=Z.LSL-Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.LSL^2}{2\nu}}
      UpperBound=Z.LSL+Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.LSL^2}{2\nu}}
      N: Total number of obsevations
      Z_{1-\alpha/2}: (1-\alpha/2)^{th} percential of standard normal distribution
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
    • Lower Specification Limit and One-Sided
      LowerBound = -\Phi^{-1}(p_1)
      p_1: Root of the equation: Pr\left(T_{\nu}(-\sqrt{N}\Phi^{-1}(p_1))\le\sqrt{N}Z.LSL\right)=1-\alpha
      N: Total number of obsevations
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
      \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
      T_{\nu}(\delta): Random variable that is distributed as non-central t distribution with \nu degrees of freedom and non-centrality parameter \delta
      Pr(\cdot): Cumulative distribution function of non-central t distribution
    • Upper Specification Limit and Two-Sided
      LowerBound=Z.USL-Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.USL^2}{2\nu}}
      UpperBound=Z.USL+Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.USL^2}{2\nu}}
      N: Total number of obsevations
      Z_{1-\alpha/2}: (1-\alpha/2)^{th} percential of standard normal distribution
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
    • Upper Specification Limit and One-Sided
      LowerBound = -\Phi^{-1}(p_2)
      p_2: Root of the equation: Pr\left(T_{\nu}(-\sqrt{N}\Phi^{-1}(p_2))\le\sqrt{N}Z.USL\right)=1-\alpha
      N: Total number of obsevations
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
      \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
      T_{\nu}(\delta): Random variable that is distributed as non-central t distribution with \nu degrees of freedom and non-centrality parameter \delta
      Pr(\cdot): Cumulative distribution function of non-central t distribution

Benchmark Zs for Overall Capability

The calculation of benchmark Zs for overall capability is similar to potential capability, by replacing the \sigma_{within} by \sigma_{overall}. Please refer to Benchmark Zs for Potential Capability for more details.

Expected Within Performance

  • PPM < LSL and % < LSL
    The parts per million (PPM) less than the lower specification limit (PPM < LSL) and percentage less than the lower specification limit (% < LSL) are computed from the probability which is as follows:
    P(X < LSL)=1-\Phi(\frac{\bar{X}-LSL}{s})
    LSL: Lower specification limit
    \bar{X}: Process mean, estimated from data, or historical mean
    s: Within subgroups standard deviation
    \Phi(X): Cumulative distribution function of standard normal distribution
    Then PPM\;<\;LSL and \%\;<\;LSL are multiples of the above probability:
    [PPM\;<\;LSL] = 1000000\cdot P(X<LSL)
    [\%\;<\;LSL] = 100\cdot P(X<LSL)
  • Confidence Intervals for PPM < LSL and % < LSL
    • Two-Sided
      Confidence intervals for P(X < LSL) are given by the following formulas
      LowerBound=1-\Phi(U)
      UpperBound = 1-\Phi(L)
      U=Z.LSL+Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.LSL^2}{2\nu}}
      L=Z.LSL-Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.LSL^2}{2\nu}}
      \Phi(X): Cumulative distribution function of standard normal distribution
      N: Number of observations
      \alpha: Alaph for confidence level
      \nu: Degrees of freedom for standard deviation
      Z_{1-\alpha/2}: (1 - \alpha/2)_{th} percentile of standard normal distribution
      Then get
      LowerBound(PPM\;<\;LSL)=1000000\cdot LowerBound
      UpperBound(PPM\;<\;LSL)=1000000\cdot UpperBound
      LowerBound(\%\;<\;LSL)=100\cdot LowerBound
      UpperBound(\%\;<\;LSL)=100\cdot UpperBound
    • One-Sided
      UpperBound(PPM\;<\;LSL)=1000000\cdot p_1
      UpperBound(\%\;<\;LSL)=100\cdot p_1
      where p_1 is the root of Pr\left(T_{\nu}(-\sqrt{N}\Phi^{-1}(p_1))\le\sqrt{N}Z.LSL\right)=1-\alpha
      N: Total number of obsevations
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
      \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
      T_{\nu}(\delta): Random variable that is distributed as non-central t distribution with \nu degrees of freedom and non-centrality parameter \delta
      Pr(\cdot): Cumulative distribution function of non-central t distribution
  • PPM > USL and % > USL
    The parts per million (PPM) greater than the upper specification limit (PPM > USL) and percentage greater than the upper specification limit (% > USL) are computed from the probability which is as follows:
    P(X > USL)=1-\Phi(\frac{USL-\bar{X}}{s})
    USL: Upper specification limit
    \bar{X}: Process mean, estimated from data, or historical mean
    s: Within subgroups standard deviation
    \Phi(X): Cumulative distribution function of standard normal distribution
    Then PPM\;>\;USL and \%\;>\;USL are multiples of the above probability:
    [PPM\;>\;USL] = 1000000\cdot P(X>USL)
    [\%\;>\;USL] = 100\cdot P(X>USL)
  • Confidence Intervals for PPM > USL and % > USL
    • Two-Sided
      Confidence intervals for P(X > USL) are given by the following formulas
      LowerBound=1-\Phi(U)
      UpperBound = 1-\Phi(L)
      U=Z.USL+Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.USL^2}{2\nu}}
      L=Z.USL-Z_{1-\alpha/2}\sqrt{\frac{1}{N}+\frac{Z.USL^2}{2\nu}}
      \Phi(X): Cumulative distribution function of standard normal distribution
      N: Number of observations
      \alpha: Alaph for confidence level
      \nu: Degrees of freedom for standard deviation
      Z_{1-\alpha/2}: (1 - \alpha/2)^{th} percentile of standard normal distribution
      Then get
      LowerBound(PPM\;>\;USL)=1000000\cdot LowerBound
      UpperBound(PPM\;>\;USL)=1000000\cdot UpperBound
      LowerBound(\%\;>\;USL)=100\cdot LowerBound
      UpperBound(\%\;>\;USL)=100\cdot UpperBound
    • One-Sided
      UpperBound(PPM\;>\;USL)=1000000\cdot p_2
      UpperBound(\%\;>\;USL)=100\cdot p_2
      where p_2 is the root of Pr\left(T_{\nu}(-\sqrt{N}\Phi^{-1}(p_2))\le\sqrt{N}Z.USL\right)=1-\alpha
      N: Total number of obsevations
      \alpha: Alpha for the confidence level
      \nu: Degrees of freedom for standard deviation
      \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution
      T_{\nu}(\delta): Random variable that is distributed as non-central t distribution with \nu degrees of freedom and non-centrality parameter \delta
      Pr(\cdot): Cumulative distribution function of non-central t distribution
  • PPM Total and % Total
    The parts per million that are outside the specification limits is calculated by:
    [PPM\;<\;LSL]+[PPM\;>\;USL] or [\%\;<\;LSL]+[\%\;>\;USL]
  • Confidence Intervals for PPM Total and % Total with Both Lower and Upper Specification Limits
    • Two-Sided
      UpperBound(PPM\;Total)=1000000\cdot U or UpperBound(\%\;Total)=100\cdot U
      LowerBound(PPM\;Total)=1000000\cdot L or LowerBound(\%\;Total)=100\cdot L
      The calculation of U and L can be referred to Benchmark Zs for Potential Capability for more details.
    • One-Sided
      UpperBound(PPM\;Total)=1000000\cdot U or UpperBound(\%\;Total)=100\cdot U
      Here U is calculated using the same method as two-sided, but replacing \alpha/2 by \alpha
  • Confidence Intervals for PPM Total and % Total with Only One Specification Limit (Lower Only or Upper Only)
    • Lower Specification Limit Only
      Use the same calculation as the confidence interval for the PPM < LSL or % < LSL
    • Upper Specification Limit Only
      Use the same calculation as the confidence interval for the PPM > USL or % > USL

Expected Overall Performance

The calculation for expected overall performance is similar as the procedure for expected within performance, but by using overall standard deviation instead. For more details, please refer to Expected Within Performance.

Observed Performance

  • PPM < LSL for Observed Performance
    [PPM\;<\;LSL(Observed)]=\frac{1000000\cdot(NumberOfObservations\;<\;LSL)}{N}, where LSL is lower specification limit, and N is the total number of observations
  • PPM > USL for Observed Performance
    [PPM\;>\;USL(Observed)]=\frac{1000000\cdot(NumberOfObservations\;>\;USL)}{N}, where USL is upper specification limit, and N is the total number of observations
  • PPM Total for Observed Performance
    [PPM\;Total]=[PPM\;<\;LSL(Observed)]+[PPM\;>\;USL(Observed)] = \frac{1000000\cdot(NumberOfObservations\;<\;LSL)}{N}+\frac{1000000\cdot(NumberOfObservations\;>\;USL)}{N}, where LSL is lower specification limit, USL is upper specification limit, and N is the total number of observations

Between/Within Capability Analysis

Standard Deviation Estimation

  • Within Subgroup Standard Deviation (\sigma_{within})
    Please refer to Standard Deivation Estimation for more details about pooled standard deviation, average of subgroup ranges, and average of subgroup standard deviation.
  • Between Subgroup Standard Deviation (\sigma_{between})
    \sigma_{between} = \max\left(0, \sqrt{\sigma_{xbar}^2-\frac{\sigma_{within}^2}{SubgroupSize}}\right)
    \sigma_{xbar} is calculated by average of moving range, median of moving range or square root of mean squared successive differences. For more details, please refer to Standard Deivation Estimation.
  • Between/Within Standard Deviation (\sigma_{b/w})
    \sigma_{b/w}=\sqrt{\sigma_{within}^2+\sigma_{between}^2}
  • Ovarall Standard Deviation (\sigma_{overall})
    Please refer to Overall Standard Deviation subsection in Standard Deivation Estimation.

Between/Within Capability

Please refer to Potential Capability section for the calculations of Cp, CPL, CPU, Cpk, and CCpk. The difference is that the \sigma_{within} is replaced by \sigma_{b/w}. And, the calculation of \nu in the formula of Cp confidence interval is also different. Here, \nu is computed by:
\nu=\frac{\left(\frac{MS_B+(m-1)MS_E}{m}\right)^2}{\frac{(m-1)^2}{m^2}\frac{MS_E^2}{df_E}+\frac{1}{m^2}\frac{MS_B^2}{df_B}}
MS_B=\frac{\sum_{i=1}^Ln_i(\bar{X_i}-\bar{X})^2}{df_B}
MS_E=\frac{\sum_{i=1}^L\sum_{j=1}^{n_i}(X_{ij}-\bar{X_i})^2}{df_E}
m = \frac{N^2-\sum_{i=1}^Ln_i^2}{N(L-1)}
df_E=\sum_{i=1}^L(n_i-1)
df_B=L-1
N: Total number of observations
L: Number of subgroups
n_i: The i^{th} subgroup size
\bar{X}: Mean across all subgroups
\bar{X_i}: Mean of the i^{th} subgroup
X_{ij}: The j^{th} observation in the i^{th} subgroup

Overall Capability

Please refer to Overall Capability section for more details.

Benchmark Zs for Between/Within Capability

The calculation of benchmark Zs for between/within capability is similar to potential capability, by replacing the \sigma_{within} by \sigma_{b/w}. Please refer to Benchmark Zs for Potential Capability for more details.

Benchmark Zs for Overall Capability

The calculation of benchmark Zs for overall capability is similar to potential capability, by replacing the \sigma_{within} by \sigma_{overall}. Please refer to Benchmark Zs for Potential Capability for more details.

Expected Between/Within Performance

The calculation of expected between/within performance is similar to expected within performance, by replacing the \sigma_{within} by \sigma_{b/w}. Please refer to Expected Within Performance for more details. And the following confidence intervals have different calculations:
  • Confidence Intervals for PPM < LSL and % < LSL
    • One-Sided
      LowerBound(PPM\;<\;LSL)=1000000\cdot p_1
      LowerBound(\%\;<\;LSL)=100\cdot p_1
  • Confidence Intervals for PPM > USL and % > USL
    • One-Sided
      LowerBound(PPM\;>\;USL)=1000000\cdot p_2
      LowerBound(\%\;>\;USL)=100\cdot p_2

Expected Overall Performance

The calculation for expected overall performance is similar as the procedure for expected within performance, but by using overall standard deviation instead. For more details, please refer to Expected Within Performance.

Observed Performance

For more details, please refer to Observed Performance.

Non-normal Capability Analysis

Overall Capability

  • Pp: Pp is computed by the parameters of the distribution used. Two methods are used for the Pp calculation, Z-Score method and ISO method.
    • Z-Score Method
      Pp = \frac{Z_{usl}-Z_{lsl}}{6}
      Z_{usl}=\Phi^{-1}(p_2), Z_{lsl}=\Phi^{-1}(p_1)
      \Phi^{-1}(p): Inverse cumulative distribution function of standard normal distribution, p*100^{th} percentile of standard normal distribution
      p_1=Prob(X\le LSL), p_2=Prob(X\le USL): Cumulative distribution function of the used distribution
      USL, LSL: Upper and Lower specification limits respectively
    • ISO Method
      Pp = \frac{USL-LSL}{X_{0.99865}-X_{0.00135}}
      USL, LSL: Upper and Lower specification limits respectively
      X_{0.99865}, X_{0.00135}: The 99.865^{th},0.135^{th} percentile of the used distribution
  • PPL
    • Z-Score Method
      PPL= \frac{-\Phi^{-1}(p_1)}{3}
      \Phi^{-1}(p): Inverse cumulative distribution function of standard normal distribution, p*100^{th} percentile of standard normal distribution
      p_1=Prob(X\le LSL): Cumulative distribution function of the used distribution
      LSL: Lower specification limit
    • ISO Method
      PPL = \frac{X_{0.5}-LSL}{X_{0.5}-X_{0.00135}}
      LSL: Lower specification limit
      X_{0.5}, X_{0.00135}: The 50^{th},0.135^{th} percentile of the used distribution
  • PPU
    • Z-Score Method
      PPU= \frac{\Phi^{-1}(p_2)}{3}
      \Phi^{-1}(p): Inverse cumulative distribution function of standard normal distribution, p*100^{th} percentile of standard normal distribution
      p_2=Prob(X\le USL): Cumulative distribution function of the used distribution
      USL: Upper specification limit
    • ISO Method
      PPU = \frac{USL-X_{0.5}}{X_{0.99865}-X_{0.5}}
      LSL: Lower specification limit
      X_{0.99865}, X_{0.5}: The 99.865^{th},50^{th} percentile of the used distribution
  • Ppk
    Ppk = \min(PPU, PPL)

Overall Benchmark Zs for Non-normal Capability

  • Z.LSL, Z.USL, and Z.Bench
    Z.LSL = 3*PPL
    Z.USL = 3*PPU
    Z.Bench = \Phi^{-1}(1-P_1-P_2)
    P_1=Prob(X<LSL): Cumulative distribution function of the used distribution, probability (X < LSL) based on the used nonnormal distribution
    P_2=Prob(X>USL): Cumulative distribution function of the used distribution, probability (X > USL) based on the used nonnormal distribution
    \Phi(X): Cumulative distribution function of standard normal distribution
    \Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution

Expected Overall Performance

  • PPM < LSL
    The parts per million (PPM) less than the lower specification limit (PPM < LSL) is computed from the probability which is as follows:
    [PPM\;<\;LSL]=1000000*F(LSL)
    PPM: Parts per million
    LSL: Lower specification limit
    F(X): Cumulative distribution function of the used nonnormal distribution
  • PPM > USL
    The parts per million (PPM) greater than the upper specification limit (PPM > USL) is computed from the probability which is as follows:
    [PPM\;>\;USL]=1000000*(1-F(USL))
    PPM: Parts per million
    USL: Upper specification limit
    F(X): Cumulative distribution function of the used nonnormal distribution
  • PPM Total
    [PPM\;Total] = [PPM\;<\;LSL] + [PPM\;>\;USL]

Observed Performance

For more details, please refer to Observed Performance.

Distribution

For more details, please refer to Distributions.

Binomial Capability Analysis

Average P

AverageP=\frac{D_{total}}{N_{total}}
D_{total}: Sum of all defectives
N_{total}: Sum of all sample sizes

Average P 95% Confidence Interval

LowerBound = \frac{\nu_1*F_{0.025, \nu_1, \nu_2}}{\nu_2+\nu_1*F_{0.025, \nu_1, \nu_2}}
UpperBound = \frac{\nu_3*F_{0.975, \nu_3, \nu_4}}{\nu_4+\nu_3*F_{0.975, \nu_3, \nu_4}}
\nu_1 = 2*D_{total}
\nu_2=2*(N_{total}-D_{total}+1)
\nu_3=2*(D_{total}+1)
\nu_4=2*(N_{total}-D_{total})
D_{total}: Sum of all defectives
N_{total}: Sum of all sample sizes
F: Inverse F cumulative distribution function

%Defective

\%$D$efective=100*AverageP

%Defective 95% Confidence Interval

LowerBound=100*LowerBoundForAverageP
UpperBound=100*UpperBoundForAverageP

PPM Defective

PPM\;$D$efective = 1000000*AverageP

PPM Defective 95% Confidence Interval

LowerBound=1000000*LowerBoundForAverageP
UpperBound=1000000*UpperBoundForAverageP

Process Z

Process \; Z = \Phi^{-1}(AverageP)
\Phi^{-1}(X): Inverse comulative distribution function of standard normal distribution

Process Z 95% Confidence Interval

LowerBound=-\Phi^{-1}(UpperBoundForAverageP)
UpperBound=-\Phi^{-1}(LowerBoundForAverageP)
\Phi^{-1}(X): Inverse cumulative distribution function of standard normal distribution

Poisson Capability Analysis

Mean Defective

Mean\;$D$efective=\frac{D_{total}}{N}
D_{total}: Sum of all defectives
N: Number of samples

Mean Defective 95% Confidence Interval

LowerBound = \frac{\chi_{0.025,\nu_1}^2}{2N}
UpperBound = \frac{\chi_{0.975,\nu_2}^2}{2N}
\nu_1 = 2*D_{total}
\nu_2=2*(D_{total}+1)
D_{total}: Sum of all defectives
N: Number of samples
\chi^2: Inverse Chi Square cumulative distribution function

Mean DPU

Mean\;$D$efects\;Per\;Unit=\frac{D_{total}}{N_{total}}
D_{total}: Sum of all defectives
N_{total}: Sum of all sample sizes

Mean DPU 95% Confidence Interval

LowerBound = \frac{\chi_{0.025,\nu_1}^2}{2N_{total}}
UpperBound = \frac{\chi_{0.975,\nu_2}^2}{2N_{total}}
\nu_1 = 2*D_{total}
\nu_2=2*(D_{total}+1)
D_{total}: Sum of all defectives
N_{total}: Sum of all sample sizes
\chi^2: Inverse Chi Square cumulative distribution function

Minimum DPU

The minimum defects per unit among all samples.

Maximum DPU

The maximum defects per unit among all samples.