P-Chart Farrokh Alemi, Ph.D..

Slides:



Advertisements
Similar presentations
Understanding Your Habit: Flow Charts & Lists of Routines Farrokh Alemi Ph.D.
Advertisements

CORE 1 UNIT 8 Patterns of Chance
Introduction to Control Charts By Farrokh Alemi Ph.D. Sandy Amin Based in part on Amin S. Control charts 101: a guide to health care applications. Qual.
Tutorial on Tukey Charts Farrokh Alemi, Ph.D. Sunday, 11/25/2007.
1 © The McGraw-Hill Companies, Inc., 2006 McGraw-Hill/Irwin Technical Note 9 Process Capability and Statistical Quality Control.
1 Statistics -Quality Control Alan D. Smith Statistics -Quality Control Alan D. Smith.
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter Seventeen Statistical Quality Control GOALS When.
Time Between Charts Farrokh Alemi, Ph.D.. Steps in construction of time in between charts 1. Verify the chart assumptions 2. Select to draw time to success.
Nursing Home Falls Control Chart Problem Statement: Assume that following data were obtained about number of falls in a Nursing Home facility. Produce.
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall8-1 Chapter 8: Statistical Quality Control.
Tutorial on Risk Adjusted P-chart Farrokh Alemi, Ph.D.
Benchmarking Clinicians Farrokh Alemi, Ph.D.. Why should it be done? Hiring, promotion, and management decisions Help clinicians improve.
Control Charts  Control Charts allow a company’s performance over time to be analyzed by combining performance data, average, range and standard deviation.
T T20-01 Mean Chart (Known Variation) CL Calculations Purpose Allows the analyst calculate the "Mean Chart" for known variation 3-sigma control.
T T20-03 P Chart Control Limit Calculations Purpose Allows the analyst to calculate the proportion "P-Chart" 3-sigma control limits. Inputs Sample.
T T07-01 Sample Size Effect – Normal Distribution Purpose Allows the analyst to analyze the effect that sample size has on a sampling distribution.
Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved. Essentials of Business Statistics: Communicating with Numbers By Sanjiv Jaggia and.
T T20-00 Range Chart Control Limit Calculations Purpose Allows the analyst to calculate the "Range Chart" 3- sigma control limits based on table.
Control Charts are tools for tracking variation based on the principles of probability and statistics SPC: Statistical Process Control.
Control Charts for Attributes
Defects Defectives.
Farrokh Alemi Ph.D. Defining Probability. Element Event Definition.
The Bell Shaped Curve By the definition of the bell shaped curve, we expect to find certain percentages of the population between the standard deviations.
XmR Chart Farrokh Alemi, Ph.D.. Purpose of Control Chart  Real or random.  Tell a story of changes in outcomes of the process.
Discrete Probability Distributions
Introduction to Linear Regression and Correlation Analysis
Lesson 7.1 Quality Control Today we will learn to… > use quality control charts to determine if a manufacturing process is out of control.
Go to Index Analysis of Means Farrokh Alemi, Ph.D. Kashif Haqqi M.D.
Introduction to Control Charts: XmR Chart
Statistical Process Control
Go to index Two Sample Inference for Means Farrokh Alemi Ph.D Kashif Haqqi M.D.
Statistical Process Control (SPC)
1 Probability Distributions for Discrete Variables Farrokh Alemi Ph.D. Professor of Health Administration and Policy College of Health and Human Services,
T T20-04 C Chart Control Limit Calculations Purpose Allows the analyst to calculate the defectives per unit "C-Chart" 3-sigma control limits.
Chapter 7. Control Charts for Attributes
By: Samah Tout and Bing Liu Team Potassium Sigma Nov. 20,2007.
MORE THAN MEETS THE EYE Wayne Gaul, Ph.D., CHP, CHMM Tidewater Environmental Columbia, SC SRHPS Technical Seminar, April 15, 2011.
1 Six Sigma Green Belt Introduction to Control Charts Sigma Quality Management.
1 Slides used in class may be different from slides in student pack Technical Note 8 Process Capability and Statistical Quality Control  Process Variation.
Quality Control  Statistical Process Control (SPC)
1 CHAPTER (7) Attributes Control Charts. 2 Introduction Data that can be classified into one of several categories or classifications is known as attribute.
INFERENCE Farrokh Alemi Ph.D.. Point Estimates Point Estimates Vary.
Time To Missed Exercise Farrokh Alemi, Ph.D.. Why do it? You need to distinguish between random days of missed exercise from real changes in underlying.
Risk Adjusted X-bar Chart Farrokh Alemi, Ph.D. Based on Work of Eric Eisenstein and Charles Bethea, The use of patient mix-adjusted control charts to compare.
Two Sample Problems  Compare the responses of two treatments or compare the characteristics of 2 populations  Separate samples from each population.
STANDARD ERROR OF SAMPLE
Agenda Review homework Lecture/discussion Week 10 assignment
Probability Calculus Farrokh Alemi Ph.D.
Statistics PSY302 Review Quiz One Fall 2018
Process Capability.
Comparing two Rates Farrokh Alemi Ph.D.
One way ANALYSIS OF VARIANCE (ANOVA)
Has Rate of Changed for Southeast Alabama Medical Center?
Causal Control Chart Farrokh Alemi, Ph.D..
Xbar Chart Farrokh Alemi, Ph.D..
Analysis of Observed & Expected Infections
Graphical Representation of Independence among 3 Variables
P-Chart Farrokh Alemi, Ph.D. This lecture was organized by Dr. Alemi.
Statistics PSY302 Review Quiz One Spring 2017
T20-02 Mean Chart (Unknown Variation) CL Calculations
Xbar Chart By Farrokh Alemi Ph.D
Testing Hypotheses I Lesson 9.
Introduction to Control Charts
Time between Control Chart: When Is Harm Reduction Drug Use?
Which Chart Is Right? By Farrokh Alemi Ph.D.
Risk Adjusted P-chart Farrokh Alemi, Ph.D.
Time-between Control Chart for Exercise By Farrokh Alemi Ph.D
Exponential Distribution Ends Up Normal
Tukey Control Chart Farrokh Alemi, Ph.D.
Univariate Description
Presentation transcript:

P-Chart Farrokh Alemi, Ph.D.

Purpose To analyze adverse outcomes Mortality Infection rate Medication error To examine percent population with a characteristic Percent satisfied with care Percent diabetes under control

Assumptions Dichotomous, mutually exclusive and exhaustive events The observations over time are independent Case mix does not change over time Sample represent population

Steps in Construction of Limits p = total adverse events / total cases si2 = p * (1-p) / ni UCL = p + 3 * s LCL = p – 3 * s Plot, interpret & distribute Number of cases in time period i

Example data

Calculate Grand Average p =C10/B10 =SUM(C2:C9) =SUM(B2:B9)

Calculate Standard Deviation =(($B$12*(1-B$12))/B2)^0.5

Calculate Control Limits = $B$12 + 3*E2 Reset negative numbers to zero

Plot chart

Interpret chart & distribute Note control limits change because number of cases per time period changes Any point outside the limits cannot be due to chance No points fall outside the limits