We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byMarissa Bennett
Modified over 3 years ago
Strategic Success Factors in Counter- Insurgency Campaigns: With Discussion of the Modelling Approach Dstl/CP23836/1.2 (ORS10) Presentation to ORS Defence Special Interest Group, 24 Feb 2010 Dr Andrew Hossack Dstl Policy & Capability Studies Dept © Crown Copyright 2010
© Dstl 2010 Dstl is part of the Ministry of Defence 2 17 January 2014 Before I Start… This is a recycled presentation: –Most of it has been previously presented at: Cornwallis XII, Nova Scotia, Apr 07 (CP23836) (Best Paper) UCSD IGCC CT Conference, San Diego, May 07 (CP24670) MORS IW Workshop, Monterey, Dec (CP26591) Etc… –The central research is (mostly) published in the Cornwallis XII Proceedings –The background terms, definitions, scope etc are published in the Cornwallis X Proceedings similarly My apologies to anyone who has heard this all before… … Please feel free to doze quietly for the next minutes! Recycled Presentation!
Background to this Presentation
© Dstl 2010 Dstl is part of the Ministry of Defence 4 17 January 2014 The Overall COIN Research Study FYs05-07: An ongoing multi-year research programme into Counter-Terrorist/Counter-insurgency (CT/COIN) campaigns: –Building upon previous HA studies of CT, stabilisation etc –Undertaken to inform development of PSO/OOTW models by Dstl etc –Phase I (Aug 04 – Jul 05): Identify generic BLUE success factors –Phase II (Aug 05 – Sep 06): Identify generic RED success factors –Phase IIIA (Oct 06 – Mar 07): Initial Study of Campaign Evolution A possible Phase IIIB (analysis of the strategic dynamics within campaigns) has not yet been agreed
© Dstl 2010 Dstl is part of the Ministry of Defence 5 17 January 2014 Previous Historical Analysis of OOTW This work builds upon a number of prior HA OOTW studies: –Operations Other Than War (OOTW) Study ( ) tactical analyses of patrols, ambushes etc in 8 COIN campaigns –Counter-Terrorism (CT) Strategies Fastball (2001) –Counter-Terrorism Overseas (CTO) Study ( ) * Focused on structure, attributes of International Terrorist groups –Iraq Campaign Assessment I (Nov 2003) * –Stabilisation Operations Study (2004) Identified success factors for external stabilisation of states *Key results included in Cornwallis IX presentation
Review of Analysis Methodology
© Dstl 2010 Dstl is part of the Ministry of Defence 7 17 January 2014 Study Approach Undertaken as a historical analysis (HA) study, i.e. Historical Analysis: –Is operational analysis of quantified data describing the actual behaviour of systems across a wide range of historical cases –Is empirical, statistical and holistic –Involves the testing of hypotheses using established statistical techniques –Focuses on understanding the enduring, underlying mechanisms of conflict –Is about the analysis of real operations …..ongoing, recent and historical!
© Dstl 2010 Dstl is part of the Ministry of Defence 8 17 January 2014 The Spectrum of Conflict Conflict Intensity (Level of Violence) (Counter)- Insurgency Civil War Limited War Theatre War Global War Humanitarian Relief Ops Strikes & Raids (Counter)- Terrorism Peace Enforcement Disaster Relief Peacekeeping/ Enforcement (Counter)- Terrorism (Counter)- Insurgency Civil War Show of Force General War Regional Conflict Low Intensity Conflict Mid Intensity Conflict High Intensity Conflict Peacetime & Crisis Symmetric (conventional) conflict Non-Conflict Asymmetric (unconventional) conflict Scope of CT/COIN Study US: UK:
© Dstl 2010 Dstl is part of the Ministry of Defence 9 17 January 2014 Study Scope Counter-Terrorism (CT) & Counter-Insurgency (COIN) assumed to represent adjacent & overlapping regions of some spectrum of asymmetric conflict No a priori distinction made between major terrorist & minor insurgent campaigns Boundary Issues: –Micro/Urban Terrorist Campaigns excluded (E.g.: November 17) –Genuine Civil Wars excluded (E.g.: Chinese Communists)
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Terrorism & Insurgency (1) Terrorist, Terrorism are emotive words, open to multiple (mis-) interpretations and abuse Terrorist typically used to refer to groups that predominantly use terrorism tactics……e.g.: –bombings –shootings –assassinations …but identical tactics also often present in insurgency campaigns as well to lesser degree
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Terrorism & Insurgency (2) UK / NATO doctrine defines terrorism to be: …..the unlawful use or threatened use of violence against individuals in an attempt to coerce or intimidate governments or societies to achieve political, religious or ideological objectives UK / NATO doctrine defines insurgency to be: ….an organised movement aimed at the overthrow of a constituted government through the use of subversion and armed conflict Only terrorism by Non-State actors against the State is considered in this study
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Study Definition CT/COIN Campaign Any extended, essentially two-sided, asymmetric conflict in which some non-state player largely within a (possibly de facto) state attempts to force some change in either the nature and/or leadership of said state predominantly through some mixture of terrorist and/or insurgent tactics. As defined from UK / NATO terminology!
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Conceptual Model of Campaign State in Conflict (SiC) Area of Conflict Location (ACL) Neighbouring State Terrorist / Insurgent Group (TIG) State Security Forces (SyF) External Intervening State (EIS)
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Examples of Campaign Structure
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Outline of Analysis Method C. 100 possible success factors identified and tested for statistically significant association with campaign outcome: –FY05: c. 40 possible SyF success factors tested –FY06: c. 60 possible Insurgent success factors tested Assessment criteria for ordered categories defined for each factor: –Category -1 : Poor, incompetent or no usage/presence –Category 0 : Moderate, mixed or occasional usage/presence –Category +1: Good, competent or extensive usage/presence Assessment criteria, categories for outcome measures defined similarly
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Coding of Success Factors Factors judged on 3 point ordinal scale:
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Outcome Measures Used Two types of campaign success considered: –Military Success (judged on achievement of monopoly of violence) –Political Success (judged on achievement of initial objectives) Military Success assumed zero-sum a priori: –(State) Success State monopoly of violence at end campaign –(State) Partial Success Both State & Insurgents retain capacity for violence –(State) Failure Insurgent monopoly of violence at end campaign Political Success coded independently for all Actors (Internal State, External State, Insurgents): –SuccessMost initial objectives achieved at end campaign –Partial SuccessSome initial objectives achieved at end campaign –FailureFew initial objectives achieved at end campaign
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Data Collection & Coding Data for 44 COIN campaigns collected & categorised by external researchers –FY05: Data collected on overall campaign, context, SyF factors etc –FY06: Additional data on Insurgent & social factors only Case selection was pseudo-random: –Stratified sampling across geographic regions –Spread over post WWI-period Each data point currently represents an entire campaign: –Static analysis only; takes no account of development of campaign over time
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Statistical Analysis Candidate success factors tested for association with success using Somers D statistic at 90% confidence Essentially assessing distributions of data in 3 x 3 Contingency Tables: Is there a tendency for better (or worse) values of variable X to be consistently paired with better (or worse) campaign outcomes?
Analysis Results (1): (Attritional) Efficiency of SyF, Insurgents
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Efficiency of SyF & TIG 0.1 "Force Ratio (Ratio of Median Annual Forces) Efficiency Security Forces Terrorists/Insurgents Plot shows efficiency of each combatant vs Force Ratio: –Efficiency: No. Opponents killed per 1,000 Man-Years force deployed
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Efficiency of SyF & TIG 0.1 "Force Ratio (Ratio of Median Annual Forces) Efficiency y = 56.5x -0.9 R 2 = Security Forces Terrorists/Insurgents SyF efficiency decreases significantly with increasing FR –Each additional soldier/policeman adds less than the one before
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Efficiency of SyF & TIG 0.1 "Force Ratio (Ratio of Median Annual Forces) Efficiency y = 19.0x 0.3 R 2 = Security Forces Terrorists/Insurgents No evidence that Insurgent efficiency changes with FR: –Each Insurgent is as efficient as the one before
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Efficiency of SyF & TIG 0.1 "Force Ratio (Ratio of Median Annual Forces) Efficiency y = 56.5x -0.9 R 2 = 0.4 y = 19.0x 0.3 R 2 = Security Forces Terrorists/Insurgents Possibly this is because Insurgents are typically too small to experience effects of diminishing returns on scale?
Analysis Results (2): The relationship between Force Ratio & Campaign Outcome
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Force Ratio & Campaign Outcome (1) There is a weak relationship between odds of military campaign success and whole-campaign Force Ratio: –Statistically significant at 93% confidence –A conservative and reasonable result given expected noise in data –Military success used because a zero-sum outcome –Strictly, tested against the Ratio of Median Annual Forces (used as a surrogate approximation to Force Ratio) Can use ordinal logistic regression to estimate the rate of change of odds with change in Force Ratio
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Force Ratio & Campaign Outcome (2) Ordinal Logistic regression estimates that: –Odds(Win), Odds(Lose) change by x3 with each x10 change in FR –90% Confidence that true rate of change is between x1 + - x8 x10 increase in FR x3 increase in Equivalent Odds
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Force Ratio & Campaign Outcome (4) The Force Ratio-Outcome Model may provide: –A basis for future development of a model incorporating key success factor values as well as Force Ratio –A method of quantifying the value of changing outcome odds in terms of the change in Force Ratio required to give an equivalent effect That is, each x2 change in campaign odds is: –Estimated to be equivalent to a x4 change in Force Ratio –At least equivalent to a x2 + change in Force Ratio (90% confidence)
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Force Ratio & Campaign Outcome (2) Each x2 (x4) change in campaign odds is: –Estimated to be equivalent to a x4 (x16) change in Force Ratio –At least equivalent to a x2 + (x4+) change in Force Ratio (90% confidence) x16 Change in FR x4 Change in Odds
Analysis Results (3): Generic Security Forces Success Factors
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 SyF Success Factors (1) Magnitude of Estimated Equivalent Force Multiplier Military Campaign Success for State & SyF Political Campaign Success for State & SyF x50 < FM - Pop Support for Conflict - Pop Support for SyF x20 < FM x50 SyF Doctrine - Pop Support for SyF - x10 < FM x20 Pop Support for ConflictSyF Doctrine SyF Training - Flexibility of SyF C2 Targeting TIG Leadership (-) x5 < FM x10 Resource AvailabilitySyF Counter-Intelligence SiC LegitimacyWinning Hearts & Minds SyF Counter-IntelligenceFlexibility of SyF C2 x1 < FM x5 Winning Hearts & MindsOverall SyF Intelligence State Legitimacy SyF Strategic PostureIntegration of SyF C2 Legal Status of Campaign They only tell us the What? not the How? (Context specific?) These are the same factors that were reported to Cornwallis X!
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 SyF Success Factors (2) Generically, the factors most important to State military and political success in COIN at the campaign level are those concerned with: –Wresting the campaign initiative away from the Insurgents: good Counter-Int; proactive Strategic Posture; Hearts & Minds –The directed & precise application of Security Forces capability: flexible & integrated C2; good Int; good Training and Doctrine –Creating/maintaining the necessary political context for success: Popular Support for Security Forces; Hearts & Minds; good Training and Doctrine
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 SyF Success Factors (3) These generic, campaign level success factors include no factors relating to: –Security Forces capability itself: Firepower, mobility, use of Special Forces etc –the specific methods by which Security Forces capability is applied against Insurgents degradation of infrastructure, direct attrition, control of population These factors may still be significant in specific contexts There is some requirement for boots-on-the-ground: –force ratio advantage & resource availability –Needed to avoid losing, but not in itself sufficient for winning?
Analysis Results (4): Generic Insurgents Success Factors
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Insurgent Success Factors (1) Magnitude of Estimated Equivalent Force Multiplier Military Campaign Success for Insurgents Political Campaign Success for Insurgents x50 < FMPop Support for Insurgents x20 < FM x50(Insurgent Doctrine) - Pop Support for Insurgents - x10 < FM x20Destruction of SyF(Insurgent Doctrine) - Attempted Destruction of State Infrastructure x5 < FM x10 Insurgent Firepower - Insurgent Counter-Intelligence - Overextension of SyF - Overthrow State Leaders - Insurgent TrainingInsurgent Experience (-) Legitimise Claim to PowerInsurgent Internal Structure (-) x1 < FM x5 Attract External InterventionAttempted Destruction of State Institutions (-) Insurgent Concealment in Terrain - Subversion of PopulationLegitimise Claim to Power Overall Insurgent IntelligenceSubversion of Population CAVEAT: The Insurgent Doctrine Factor is unsafe – use with caution!
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Insurgent Success Factors (2) Proportionately fewer factors are identified as generically associated with success for Insurgents compared to SyF Is Insurgent success more context-specific than for SyF? –Fewer generic rules of thumb for guidance? Emphasises need for SyF Int to get inside Insurgents heads? Or a reflection of reduced robustness of Insurgent data compared to SyF data?
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Insurgent Success Factors (3) Possibly 3 broad themes discernible among Insurgent Success Factors: –Possession of favourable political context for success: Having popular support; establishing legitimacy of cause; subverting population into detaching itself from the State –Maintaining an Intelligence Superiority over SyF –Possession of kinetic military strength: Firepower; Trained insurgents; Relevant Doctrine?
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Insurgent Success Factors (4) Firepower / kinetic military strength may be more important to Insurgents than to SyF –Possession of heavy weapons firepower is a military success factor for Insurgents but not for SyF! –Insurgents do not suffer diminishing returns on increasing forces Presumably because Insurgents are much smaller than SyF so gain greater benefit from improvement in military capability As much a hypothesis as an inference! Suggests the necessity of modelling both the military as well as the political component of CT/COIN!
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Insurgent Success Factors (5) Importance of military capability may explain some of the less immediately understandable Insurgent success factors: –Insurgent Internal Structures (with hierarchical better than cellular) –Destruction of SyF –Overextension of SyF –Overthrow of State Leadership –Concealment within Terrain (but not within populations) These all require (relatively) large Insurgency size to be worth pursuing Or, they may be spurious results (False Positives)
Caveats on Results
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Caveats on these Results Static, whole-campaign analysis: –Effectively considers the average state of factors across geography and time Robustness of Insurgent factor design: –Wide range of possible Insurgent strategies for different contexts / campaigns –Hard to write generic definitions that cover 44 campaigns over 85 years! –Problem of Intent vs Action (Threatening vs Actual Destruction etc…) Spurious or False Positive Results: –An inherent limitation of inferential statistics! –At 90% confidence, it is statistically likely that up to: 4 of the SyF success factors found for each outcome type 6 of the Insurgent success factors found for each outcome type are false
Summary & Conclusions
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Conclusions on Success Factors The Political Component of CT/COIN: Popular support factors for Insurgents and SyF potentially offer some of the largest modifiers to campaign odds of success: –Affecting both military and political campaign outcomes –Up to twice as important to political as to military success The Military Component of CT/COIN: Although Force Ratio has a weaker effect upon campaign outcome than key SyF and Insurgent Success Factors: –There is still some requirement for boots-on-the-ground –SyF suffer diminishing returns on manpower at typical FRs –Insurgents gain at least some benefit from numbers, training, firepower
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Conclusions on Research Method provides technique for quantifying the payoff from adopting given strategies / capabilities….if done appropriately Results demonstrate the necessity for modelling the military and the political components of CT/COIN together –Endorses a Rational Expectations perspective of PSO/COIN There is potential for further exploitation of existing, static campaign data Further major research will require more detailed, campaign- phase resolution
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Any Questions?
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Published Research A HOSSACK Historical Analysis of Terrorist Campaigns, with observations on Current Operations in Iraq (Dstl/CP10135). Published In: A WOODCOCK, G ROSE, eds., The Cornwallis Group IX: Analysis for Stabilization and Counter- Terrorist Operations (2004) pp 393 – 417. C IRWIN, A S MORLEY, Drawing lessons from the past. A historical analysis of stabilization operations. Royal United Services Institute (RUSI) Journal, Vol. 150 No. 1, February 2005, pp49 – 53 A HOSSACK, K SIVASANKARAN. Success Factors in CT/COIN Campaigns: Preliminary Results arising from Current Research (Dstl/CP14230). Published In: A WOODCOCK and G ROSE, eds., The Cornwallis Group X: Analysis for New and Emerging Societal Conflicts (2005) pp A HOSSACK. Security Force & Insurgent Success Factors in Counter-Insurgency Campaigns (Dstl/CP23836). Published In: A WOODCOCK and G ROSE, eds., The Cornwallis Group XII: Analysis for Multi-Agency Support (2008).
© Dstl 2010 Dstl is part of the Ministry of Defence January 2014 Contact Details Dr Andrew Hossack FORS CMath FIMA Principal Analyst & Associate Fellow Historical and Operational Data Analysis (HODA) Team Analysis, Experimentation & Simulation (AES) Group Policy & Capability Studies (PCS) Department Defence Science & Technology Laboratory (Dstl) iSAT K, Rm C036, Grenville Bldg West Court, Portsdown Hill Road, Fareham, HANTS PO17 6AD Tel: +44(0) Fax: +44(0) adhossack dstl.gov.uk [dstl] is part of the UK Ministry of Defence
Chap 7-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 7 Sampling and Sampling Distributions Statistics for Business.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
Time for a BREAK! You have 45 Minutes. Time Left 44.
C Copyright © 2005, Oracle. All rights reserved. Practice Solutions.
STATISTICS HYPOTHESES TEST (I) Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
Chapter 12 Membrane Transport Essential Cell Biology Third Edition Copyright © Garland Science 2010.
January Structure of the book Section 1 (Ch 1 – 10) Basic concepts and techniques Section 2 (Ch 11 – 15): Inference for quantitative outcomes Section.
© The McGraw-Hill Companies, Inc., Chapter 12 Chi-Square and Analysis of Variance (ANOVA)
Break Time Remaining 10:00. Break Time Remaining 9:59.
13:00 Clock will move after 1 minute PPT – VCIC Timer 15.ppt.
PP Test Review Sections 6-1 to 6-6 Mrs. Rivas 1. 2.
1 Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION.
Estimation Chapter 8 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.
1 Phase III: Planning Action Developing Improvement Plans.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Statistical Inferences Based on Two Samples Chapter 10.
The Frequency Table or Frequency Distribution Table What is it? Not a graph itself, but a prelude to an important statistical graph.
4/4/2015Slide 1 SOLVING THE PROBLEM A one-sample t-test of a population mean requires that the variable be quantitative. A one-sample test of a population.
Biostatistics Unit 5 Samples Needs to be completed. 12/24/13 1.
DLMSO Classroom Timer Select a time to count down from the clock above 60 min 45 min 30 min 20 min 15 min 10 min 5 min or less.
1 Data, Now What? Skills for Analyzing and Interpreting Data Abby Winer Christina Kasprzak Kathleen Hebbeler Division for Early Childhood Annual Conference.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression and Model Building Chapter 14.
Copyright © 2012, Elsevier Inc. All rights Reserved. 1 Chapter 7 Modeling Structure with Blocks.
Copyright © 2013 Pearson Education. All rights reserved. Chapter 4 Sampling Distributions and Data Descriptions.
OPTN Modifications to Heart Allocation Policy Implemented July 12, 2006 Changed the allocation order for medically urgent (Status 1A and 1B) patients Policy.
Copyright © 2011 Pearson Education, Inc. Putting Statistics to Work.
Chap 4-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 4 Probability Statistics for Business and Economics 6 th Edition.
Oil & Gas Final Sample Analysis April 27, Background Information TXU ED provided a list of ESI IDs with SIC codes indicating Oil & Gas (8,583)
Artificial Intelligence Genetic Algorithms Source: 1.
Hypothesis Tests: Two Independent Samples Cal State Northridge 320 Andrew Ainsworth PhD.
Chapter 11 Membrane Structure Essential Cell Biology Third Edition Copyright © Garland Science 2010.
Chapter 14 Energy Generation in Mitochondria and Chlorplasts Essential Cell Biology Third Edition Copyright © Garland Science 2010.
Copyright © Action Works 2008 All Rights Reserved - Photos by David D. Kempster 1.
Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.
Numbers Treasure Hunt
Evaluating Provider Reliability in Risk-aware Grid Brokering Iain Gourlay.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Experimental Design and Analysis of Variance Chapter 11.
BMU - E I 1 Development of renewable energy sources in Germany in
Chapter 1 Organization Theory and Health Services Management.
1 RA III - Regional Training Seminar on CLIMAT&CLIMAT TEMP Reporting Buenos Aires, Argentina, 25 – 27 October 2006 Status of observing programmes in RA.
×1= 9 4 1×1= 1 5 8×1= 8 6 7×1= 7 7 8×3= 24.
Basic Statistics Measures of Central Tendency STRUCTURE OF STATISTICS STATISTICS DESCRIPTIVE INFERENTIAL TABULAR GRAPHICAL NUMERICAL CONFIDENCE INTERVALS.
Simulations The basics for simulations. Simulation is a way to model random events, such that simulated outcomes closely match real-world outcomes. By.
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt Synthetic.
SADC Course in Statistics Introduction to Non- Parametric Methods (Session 19)
STATISTICS INTERVAL ESTIMATION Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
Regional Policy Changes in Common Indicators Definitions and Discussion Brussels, 14 th March
Survey Internal Communication trends in the Italian banking sector Mario Spatafora Simone DellOrto Milan, September 2002.
C82MST Statistical Methods 2 - Lecture 3 1 Overview of Lecture Partitioning Evaluating the Null Hypothesis ANOVA Basic Ratios Sums of Squares Mean Squares.
Subtraction: Adding UP. Category 1 The whole is a multiple of ten.
© 2017 SlidePlayer.com Inc. All rights reserved.