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### 12 Cards in this Set

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 SPC Technique for applying statistical analysis to measure, monitor, and control processes Variation Classified as chance (common) cause, assignable cause Subgroup selection Makes subroup as homogenous as possible and maximizes opportunity for variation from one subgroup to another Sources of variability 1) Lot-to-lot 2) Stream-to-stream 3) Time-to-time 4) Piece-to-Piece 5) Error of measurement: equipment and human Process in statistical control Characterized by plot points that do not exceed the upper or lower control limits. X bar - R Chart UCL(Xbar)= Xbarbar + A2*Rbar LCL(Xbar)= Xbarbar - A2*Rbar UCL(R)= D4*Rbar LCL(R)=D3*Rbar Used when data is readily available Limits at 3 sigma (covers 99.73% of population) Xbar-s s=√(Σ(X-Xbar)^2/(n-1)) UCL(Xbar)=Xbarbar + A3*sbar LCL(Xbar)=Xbarbar - A3*sbar UCL(s)=B4*sbar LCL(s)=B3*sbar Used when larger sample sizes are used for increased sensistivity to variation MXbar-MR UCL(Xbar)=Xbarbar + A3*sbar LCL(Xbar)=Xbarbar - A3*sbar UCL(s)=B4*sbar LCL(s)=B3*sbar Individual Xbar's and R's are calculated based upon some number of measurements in a sample. Each sample is comprised of one new measurement and the rest old measurements. Used where data is less readily available X-MR Individual data points and moving range UCL(X)= Xbar+E2*MRbar LCL(X)= Xbar-E2*MRbar UCL(MR)= D4*MRbar ONLY chart which may have specification limits NOT as sensitive to process changes CuSum More efficient at detecting small shifts (2 sigma or less) If process remains in control, centered at μ0, the CuSum plot shows variation in a random pattern about zero. If the process drifts, the CuSum points will drift as well. Exponentially Weighted Moving Average (EWMA) EWMA(t)=λY(t)+(1-λ)EWMA(t-1) s^2(EWMA)=(λ/(2-λ))s^2 UCL=EWMA0+ks(EWMA) LCL=EWMA0-ks(EWMA) k=3 p Chart Fraction defective UCLp=pbar+3*sqrt((pbar(1-pbar))/n)