Running head: DATA PROCESSING PLAN 1
Data Processing Plan
Michael Dyer
Southern New Hampshire University
DATA PROCESSING PLAN 2
Data Processing Plan
Running for Glory is faced with a couple of questions of whether we should try to expand
the business and
Running head: MILESTONE 5
1
Milestone 5
Michael Dyer
Southern New Hampshire University
MILESTONE 5
2
Milestone 5
The best way to analyze the data is by using a combination of tools, methods, and
strategies. The process of collecting the data generated a l
Running head: RFG FINAL 1
Running for Glory Final Report
Michael Dyer
Southern New Hampshire University
RFG FINAL 2
Running for Glory Final Report
Situation Analysis
Running for Glory focused on keeping their customers loyal by providing products for
the
1.
How many products in which two categories have a profit margin % greater than
60%?
a.
Select the Product tab
b.
Click and drag over the data points near 60% on the graph
c.
Repeat step b until only the points above 60% are selected
Answer: There was 16
probability
The new probability for T1 test is as below;
Now, I have to guess 1 correct Answer among the two question, So the parameter will be:
N=2
K=1
P = 0.25
1- P = 0.75
= P(1 correct question in T1 Test) = (2!/(2-1)!*1!)*.25^1*(1-.25)^1
= 0.375
T2 te
Problem Set 2
1
Problem Set 2
Southern New Hampshire University: Decision Methods & Modeling
Problem Set 2
2
1.
Think for a minute and describe your optimal strategy for solving this problem in 50
words or less.
I would grab 11 socks and fins a matching p
Data Science and Big Data Analytics
Lab 05 Guide
Copyright
Copyright 1996, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012 EMC Corporation. All Rights Reserved. EMC believes the information
in this publication is accurate as o
Query Languages
In order to obtain needed information, data managers should be able to interact with a
database and extract information or even perform some specific tasks against data. Query
languages allow the user to interact directly with the database
Kimball Versus Inmon: Enterprise Data Warehouse Versus Many Data Marts
Decision support systems (DSS) are defined by the Data Management Body of
Knowledge (DAMA-DMBOK) as one or a series of computer applications that analyze data
based on rules and presen
Enterprise Systems and Data Management
According to the Data Management Body of Knowledge (DAMA-DMBOK), an enterprise
system (also known as an enterprise application) is a large-scale package that supports
business processes, the information flows that su
Data Management Case Analysis Final Project DAT500
1
DATA MANAGEMENT CASE ANALYSIS FINAL PROJECT
Angela Mann
Southern New Hampshire University
Data Management Case Analysis Final Project DAT500
2
Introduction
The consulting firm of Dinep, Newhart and Mann
Tools Analysis One Database Management Systems DAT500
1
TOOLS ANALYSIS ONE DATABASE MANAGEMENT SYSTEMS Module 4
Angela Mann
Southern New Hampshire University
Tools Analysis One Database Management Systems DAT500
2
For this tool analysis, I chose to review
Independent, Dependent, and Mutually Exclusive Events
Independent Events
Events are independent when the occurrence of one event has no effect on the occurrence of other events. The probability of both events occurring is
P(A and B) = P(A) x P(B).
The fiv
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Courses
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Tutorials
GLMs (/wws509)
Multilevel (/pop510)
Survival (/pop509)
Germn Rodrguez
Demography (/eco572)
Stata (/stata)
Introducing R
2 Getting Started
Obviously the first thing you need to do is download a copy of R. The current v
Probability Distributions
In any random experiment, a chance, or probability, always exists that a particular event will or will not occur. The probability that an event will occur is the
ratio of the number of outcomes favorable to that event to the tota
Binomial Distributions
The binomial distribution is a discrete probability distribution, and one of the more important and frequently used statistical distributions in Six Sigma. The
word binomial means "consisting of two names." It is an apt description
Calculating Probability
Probability analysis is the basis for many Six Sigma tools and is used during most stages of the DMAIC process. For instance, control charts can be used
during the Control stage, confidence intervals can be used during the Measure
Poisson Distributions
To understand probability distributions, it is necessary to grasp the difference between defects and defectives. In Six Sigma, a defect is a nonconformity a characteristic that does not conform to its quality standard. A defective is
sprintf cfw_base
R Documentation
Use C-style String Formatting Commands
Description
A wrapper for the C function sprintf, that returns a character vector containing a formatted combination
of text and variable values.
Usage
sprintf(fmt, .)
gettextf(fmt, .
Lab Exercise 6: Association Rules
Purpose:
This lab is designed to investigate and practice Association Rules.
After completing the tasks in this lab you should able to:
Tasks:
Tasks you will complete in this lab include:
References:
Use R functions for A
DAT 520 Problem Set 2
Simple Proportions and Binomials
Binomial Probability Review
The binomial probability formula:
Deal with factorials efficiently: You can cut down the amount of calculating by reducing factorials:
Remember that
And that
Solving Binomi
Cookbook for R
Strings Creating strings from variables
Creating strings from variables
Problem
Solution
Using paste()
Using sprintf()
Notes
Problem
You want to do create a string from variables.
Solution
The two common ways of creating strings from varia
How do you justify which visualization tool is best suited for the targeted user audience and
their interaction needs across multifunctional teams?
You first must know what you want to see, what problem you are trying to solve, what
questions have to be a
Graduate Course Syllabus
DAT 520: Decision Methods and Modeling
Center: Online
Course Prerequisites
DAT 510
Course Description
The role of many analysts is as much about interpreting the results of data analysis as it is about gathering the data
and "crun
DAT 520 Final Project Notes
This document is intended to assist you with the final project. This course introduces a decision analysis
using a decision tree. There are guidelines that can assist in completing a project of this kind.
Milestone One: Choose
DAT 520 Milestone Three Guidelines and Rubric
In this milestone, you will perform an evaluation of your decision model and revise your decision model as needed. Evaluation examples are if you are
performing a bottom-up style recursive partitioning analysi
DAT 520 Advanced Problem Set Rubric
Problem Sets 4, 5, 6, and 7
Instructor Feedback: This activity uses an integrated rubric in Blackboard. Students can view instructor feedback in the Grade Center. For more information,
review these instructions.
Critica
DAT 520 Peer Review Activity Checklist and Rubric
Evaluate two of the exemplar final projects. For the peer reviews, all the items in the final project rubric and the course outcomes are fair game for your
commentary; however, follow this peer review chec
DAT 520 Milestone One Guidelines and Rubric
For your project, choose a data set from the curated list of sources (Final Project Topics and Sources.xls), or you may submit your proposal for a different data
source than these listed. Refer to the Final Proj