- Six Sigma Tutorial
- Six Sigma DMAIC process
- Six Sigma Acceptance Sampling
- Sampling Plan Variation vs Lot Size Variation in Acceptance Sampling
- AQL Based Sampling Plans
- Decision Tree for Selecting Type of Variables in Sampling Plan
- FMEA – Failure Mode and Effects Analysis
- Types Of FMEA: Design FMEA (DFMEA), Process FMEA (PFMEA)
- The FMEA Quality Lever – Where To Put The Effort
- FMEA Quiz
- Six Sigma Confidence Intervals
- Confidence Limits
- Confidence Interval Formulas
- Z Confidence Interval for Means – Example
- t Confidence Interval for a Variance – Example
- Six Sigma Defect Metrics – DPO, DPMO, PPM, DPU Conversion table
- Fishbone Diagram – Fishbone Analysis
- Cost of Quality Defects and Hidden Factory in Six Sigma
- Pareto Analysis using Pareto Chart
- Six Sigma Calculators – DPMO, DPM, Sample Size
- How to select a Six Sigma project? Download selection grid template.
- How to create Six Sigma Histogram? Download Excel template
- Scatter Plots – Free Six Sigma Scatter Plot template
- How to create, use Six Sigma SIPOC tool? Download SIPOC Template
- Quality Function Deployment (QFD) – Download free templates
- What is Decision Matrix or Decision Making Matrix ?
- The nature of Process Variation
- What is RACI or RASCI Matrix/Chart/Diagram? Download free templates
Z Confidence Interval for Means – Example
Calculate a 95% C.I. on the mean for a sample (n = 35) with an x-bar of 15.6″and a known s of 2.3 ”
This interval represents the most likely distribution of population means, given the sample’s size, mean, and the population’s standard deviation. 95% of the time, the population’s mean will fall in this interval.
Use the t distribution for the confidence interval for a mean if the sample size n is relatively small (< 30), and/or s is not known. The confidence interval (C.I.) includes the shaded area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population means, given the sample’s size, mean, and standard deviation.
Use the χ2 (chi-squared) distribution for the confidence interval for the variance The confidence interval (C.I.) includes the area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population variances (sigma squared), given the sample’s size and variation.
