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Data Stream Momentum
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The Experiment

## 2. Data Stream Momentum

This Notebook examines the idea of Data Stream Momentum. We begin with an exploration into the Complexities of Data Accumulation, then move into the life of a Data Stream. We meet some strange characters. Data Density, Improbable Possibilities, and Time Momenta are just a few of the weirdoes that we encounter on the way to Data Stream Momentum. We casually link the idea of physical Force with Change in a Data Stream. We define the concepts of Quake, Data Flow, Flow Density, and Data Stream Current. Finally we look at interactive Data Streams through correlation principles. Below are the contents of this Notebook.

1. The Complexities of Data accumulation

A. Generating Data

B. Beginning Fuzzy Data

C. Equivalent Data Densities

D. Back to Data Density

2. Data Streams & Central Tendencies

A. Data Streams

B. Measures of Central Tendencies and Variation

C. Derivation of a Context-Based Equation for the Mean and Change in Means

D. Factors determining the stability of the Mean Average:

Pie Graphs: A Visual Representation of Real Impact

E. Real & Potential Impact: Beginning Definitions

3. Data Stream Density

A. General Concepts

B. Time Momenta Diagrams & Spiral Time Theory

C. Standard Deviations & the Potential for Change

D. Examples: Random, Dead vs Real Time Momenta

4. Data Stream Momentum & Improbable Possibility

A. Defining Equations for Data Stream Density

B. Visualizations: Something in terms of Nothing

C. Definition: Data Stream Momentum

D. Distinguishing Stream Types

5. Data Stream Characteristics

A. An Exploration of Live & Dead Data

B. Data Stream Predictability

C. Emergence or Different Durations

D. Killing a Data Stream vs. a Data Stream that Dies Naturally

E. Predictive Probability and Change

F. Beginning Data Stream Quakes

G. Underlying Assumptions

6. Flow Density

A. Dimensional Data & Data Flow

B. Flow Density

C. Flow Analysis

7. Data Stream Force & Proportional Impact

A. Newton's Laws & Data Stream Equivalencies

B. Proportional Impact: not another Impact?!

C. Z-Scores & Impact, some Data Stream definitions

8. Interactive Data Streams

A. Collective Measures

B. The Current of a Data Stream

C. Types of Internal Force

D. Correlation in Interactive Data Streams

E. A Change: Opposite & equal

F. Quantum Logic: You Can't Look Inside

Summary & Conclusions