Linganisha mbinu

	Chati za Kudhibiti Sifa (p, np, c, u)	Kipimo cha idadi-mbili cha z
Nyanja	Takwimu	Takwimu
Familia	Process / pipeline	Hypothesis test
Mwaka wa asili	1931	1900
Mwanzilishi	Walter A. Shewhart	Karl Pearson / classical large-sample z approximation
Aina	Statistical process control charts for count/proportion data	Parametric proportion comparison
Chanzo asilia	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company. ISBN: 978-0-87389-076-2	Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley.
Majina mbadala	p-chart, np-chart, c-chart, u-chart	z-test for proportions, two-sample proportion test, one-proportion z-test, Oran Testi — z Testi (Oranlar)
Zinazohusiana	4	4
Muhtasari	Attributes control charts extend Shewhart's framework to count and proportion data — quality characteristics that are classified rather than measured. The p- and np-charts monitor the proportion or number of defective items using the binomial distribution, while the c- and u-charts monitor the number of defects per unit using the Poisson distribution. They are the standard statistical-process-control tools when inspection yields pass/fail or defect counts rather than continuous measurements.	The proportion test (z-test for proportions) is a parametric hypothesis test that compares one or two sample proportions against a reference value or each other. Grounded in the large-sample normal approximation formalized by Fleiss, Levin, and Paik (2003), it is the standard tool for binary outcome comparisons when samples are large enough for the central limit theorem to apply.