P00: frame around

P01: olicognography

P02: addictions

Registers of application docs

*statistics proceedings *

*procedures bias*

Similar user docs

*non parametric statistics *

*sampling errors *

*von Neumann utility*

Process in Statistics Investigation (left up to down right)

Empirical Framework for Statistical Investigation

	Preliminaries -1-	Hypothesis -2-	Variables -3-	Sampling - 4 -	Collection -5-	Control -6-	Estimation -7-	Evaluation -8-	interpretation -9-	Utilization -10-
Preliminaries -1-	Bayes Previous Estimation Experience Delphi Culture.	Domain Scale Theory	Bibliography Identities Parameters	Clusters’ (type base) Variability control Significance	Filters Adjustment structures Codes Separation.	Variability Diversity Central tendency	Graphs Remain Continuity Criteria of exclusion and inclusion *applet	Organization Response Mode of presentation	Complement Opposition Contradiction Argumentation	Exponent
Hypothesis -2-	Context Needs Environment Prejudgment Intentions	Logic Combinatory Comprehension Meta-analysis Preparation	Algebra programs Randomization Hierarchic Ascendant Classification Type variables	Randomization Strata Efficacy Asymptotic convergence Exhaustive equal probability	Structural effects Factorial development	Tools (computer, etc.) Identity and reality	Tested hypothesis Factorial analysis Discriminate analysis	Precision Clustered number	Multicriteria choice Normalization	PPCM
Variables -3-	Bibliography Means Economic Conditions Type of variable	Investigation’s organization Enquiry’s selection	Combinatory algebra Options Events Variables Perspectives Options Relations	Confidence interval Previous conditions Probability of occurrence Scales	Contradictory variables Duality Superability	Convergence Threshold levels Signs Categories Classes Indices	Metric Normalization Distances Maximum likelihood Asymptotic convergence Estimation	Correlation Regression Hypothesis’ conclusion Scales Indices	Doing Reproducible Utility	Specificity Generalization Comparability Odd ratio/relative risk
Sampling -4-	Investigated population Social and practical conditions Lists/Cluster	Coherence Efficiency Enquiry form persuasion Didactic enrolment	Trustable Precision Numbers’ rules	Cluster theory Repre-sentativity. Calculable Investigated base, Interval of confidence, Exclusion / inclusion criteria, Ethic	Limits Max and min Intervals	Autocorrelation Calculable Empirical weight	Remaining Skedasticity Proportion Structure	Relations Distances Geometry	Representativity Diversity	Domain definition Domain restriction Reproducible
Collection -5-	Investigator capacity Algebraic investigation	Recruiting, Practical experience, Effective structure	Contradiction Structure Intentions	Economical and methodological limits	Estimation theory		Variance analysis Multivariate analysis Calculus operations Comparison	Variance analysis Feed back Enquiry validation	Frequency Occurrence Weight Proportion	Variability Reproducible
Control -6-	Rules of rejection witness cases	Decisions’ criteria Excluded Responsibility	Consensus	Contamination		Intermediate tendency Truncation Conventions Aberration Exclusion	Calculus Verification Hope	Bayes Adjustment Rectification	Lost and recovered	Stability Return Validation
Estimation -7-	Universe Ensue “Others”	Functional hypothesis Concepts’ definition	Significance Phenomenology of variables	Understanding of formula	Expected Complementary information Simplification	Procedures Verification Previous judgment on variability	Laws Rules Domain validity degree of freedom Dependency Noise Tests selection Bias Deviation	Differential calculus Construction model Sensibility Specificity Positive predictive value Negative predictive value	Parametric sensibility, Quotients Relation, Odd risk	Robustness Discrimination Validation
Evaluation -8-	Explanations Hypothesis Formulas	Choice of tests Coherence of hypothesis	Variability interpretation Following Qualification	Coherence of phenomena and parameter	Games & Gains Stereotypes and paradigms	Judgment Regrouping Artifacts	Social filters Witnesses margins Threshold levels significance	Cause model Reproducible Interval Repetition Rationality	Modeling Complexity Simulation	Model significance, Soundness
Interpretation -9-	Qualification Rights Critic Feasibility	Communication Discussion Participation	Signification Representation Mixing	Method induced effects on collection Adequacy Lessons	Neutrality Perception Sensibility	Specificity Discretion Effective	Stop Closure Transformation	Model Reduction Simplicity	Explanation Interpretation Information Signification Implication	Stability Projection Intermediary conclusion
Utilization -10-	New formulation pedagogy Math & statistic culture reasons to disagree	Transmitted effective Cooperation	Effective use Statistical social expectation Parameters’ logic	Global generality Execution Sincerity	Pertinent Communicability Rigor Continuity	Reaction Critics Modifying Change	Parameters’ practice Logistic Action means	Freedom Gain hopes Programming Revisions Following	Understandability Commitment Usefulness Planning suitability	Decision Programming Flexibility Right Legitimacy

diagonal of articulation	Memory Capital	Arithmetic	Identification	Instrument Base	Analytic Procedures Algorithms	Identification Simplification	Heuristic	Synthesis Comparison	Conclusion	Practicing

Places of use docs

*entropy measuring *

*statistics misuses *

*decision steps*