For this assignment, you must analyze a dataset and provide the results of your analysis. You should not interpret the output at this stage. Please refer to the data file in the week 1 resources.
In the video game dataset provided, you can explore two categorical or grouping variables (independent variables), which include the type of player (police officer or thief) and advertising period (advertising period or no advertising period). You can explore the data to determine if the number of video game visits and/or the amount of visit time (dependent variables) are different for the levels of the two independent variables. If the data are normally distributed, you could use independent samples t-tests as your inferential model to compare the two levels of each independent variable (you would run two separate t-tests). If you analyze both independent variables simultaneously with their interaction term, you will use a two-way analysis of variance.
Your paper should consist of the following components:
Describe the problem and state the hypotheses to be tested.
Include the appropriate descriptive statistics and visuals in order to describe the characteristics of the data and include a written summary of the data.
Address all relevant statistical assumptions and provide a written summary of the findings.
Describe the results of the inferential analyses implemented to address each hypothesis.
Length: 6 pages, not including title and reference pages
References: Include a minimum of 6 scholarly resources.
The completed assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. The content should reflect scholarly writing and current APA standards and provide a plagiarism report
Master Scoring Summary
ID Initiative Name Score
Economic Fit/ Attractiveness (70) Ability To Execute / Business Fit (30) Confidence Rating
1 Initiative 1 38 22 90
2 Initiative 2 44 14 55
3 Initiative 3 52 28 80
4 Initiative 4 44 10 75
5 Initiative 5 60 18 80
6 Initiative 6 38 28 75
7 Initiative 7 50 12 65
8 Initiative 8 50 12 65
9 Initiative 9 52 28 80
10 Initiative 10 48 26 65
11 Initiative 11 48 22 60
12 Initiative 12 48 22 60
13 Initiative 13 50 28 75
14 Initiative 14 52 28 70
15 Initiative 15 58 26 85
16 Initiative 16 42 24 90
17 Initiative 17 58 28 90
18 Initiative 18 54 28 95
19 Initiative 19 54 28 95
20 Initiative 20 54 28 100
21 Initiative 21 50 26 100
22 Initiative 22 46 26 80
23 Initiative 23 58 28 100
24
25
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38 44 52 44 60 38 50 50 52 48 48 48 50 52 58 42 58 54 54 54 50 46 58 22 14 28 10 18 28 12 12 28 26 22 22 28 28 26 24 28 28 28 28 26 26 28 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Economic Fit/Attractiveness
Ability to Execute/Business Fit
TIM-7101_Video_Game_Data
Date Visits VisitTime TotalTime Game Advertising
Friday 0 0 0 Police Yes
Saturday 1 0.76 0.76 Police Yes
Sunday 0 0 0 Police Yes
Monday 0 0 0 Police No
Tuesday 0 0 0 Police No
Wednesday 0 0 0 Police No
Thursday 0 0 0 Police No
Friday 0 0 0 Police No
Saturday 0 0 0 Police No
Sunday 0 0 0 Police No
Monday 6 1.33 7.95 Police Yes
Tuesday 5 2.98 14.9 Police Yes
Wednesday 0 0 0 Police Yes
Thursday 7 2.4 16.83 Police Yes
Friday 0 0 0 Police Yes
Saturday 0 0 0 Police Yes
Sunday 1 0.82 0.82 Police Yes
Monday 8 1.93 15.45 Police Yes
Tuesday 3 1.33 3.99 Police No
Wednesday 0 0 0 Police No
Thursday 0 0 0 Police No
Friday 0 0 0 Police No
Friday 1 1.68 1.68 Theif Yes
Saturday 1 0.67 0.67 Theif Yes
Sunday 0 0 0 Theif Yes
Monday 1 1.16 1.16 Theif No
Tuesday 0 0 0 Theif No
Wednesday 1 2.88 2.88 Theif No
Thursday 0 0 0 Theif No
Friday 0 0 0 Theif No
Saturday 0 0 0 Theif No
Sunday 0 0 0 Theif No
Monday 8 1 7.97 Theif Yes
Tuesday 3 1.41 4.22 Theif Yes
Wednesday 0 0 0 Theif Yes
Thursday 10 2.85 28.45 Theif Yes
Friday 0 0 0 Theif Yes
Saturday 1 4.44 4.44 Theif Yes
Sunday 1 1.23 1.23 Theif Yes
Monday 6 2.15 12.89 Theif Yes
Tuesday 0 0 0 Theif No
Wednesday 0 0 0 Theif No
Thursday 0 0 0 Theif No
Friday 0 0 0 Theif No
Scoring Definitions
Growth Opportunity Scoring Definitions
Evaluation Criteria Higher Attractiveness / Fit
(5 Points) Medium Attractiveness / Fit
(3 Points) Lower Attractiveness / Fit
(1 Point)
Attractiveness Revenue Potential 3 Year revenue potential of $1,000,000 or more 3 Year revenue potential of $999,999 – $400,000 3 Year revenue potential of $399,999 or less
Pretax Potential More than 40% Between 30% – 40% Less than 30%
Strategic Alignment Fits a key strategic growth initiative / lever and it fits our culture / business model Fits a strategic growth initiative / lever Unclear fit with current business strategies
Client Need Unmet need validated by potential customers; unmet need with customer request for service Unmet need identified and confirmed (not with customer); met need with customer openess to service Unmet need may exist but has not been confirmed; met need with customer not intersted in service
Customers Targets customer inside domain of interest, and decision maker is in a function we are very familiar with Targets customer inside our domain of interest and the decision maker is unfamiliar with us Targets customer outside our domain of interest
Time to Revenue Less than 6 months to initial revenue 7- 18 months to initial revenue Greater than 18 months to initial revenue
Investment Required
(non employee) Minor (0 – 10% of revenue potential) Moderate (10-20% of revenue potential) Significant (>20% revenue potential)
Progressive Cutting Edge – Viewed as progressive by the target customer Leading Edge – Viewed as “second” to the market but considered progressive Standard – Effective and proven but not progressive
Ability to Execute / Business Fit Capabilities – Process Does not require any significant additions to, or enhancement of, our existing processes Requires enhancement of existing processes, but does not require new processes Depends on process that do not exist in the business today
Capabilities – Technology Tools Does not require any significant additions or upgrades to current tools Requires substantial upgrades to existing tools, but no new tools Requires new technology tools
Capabilities – Skillsets Only requires existing leadership, management, and operational skillsets Requires new skillsets / talent from a leadership/management or an operational perspective (not both) Requires the addition or new skillsets / talent from both a leadership/management and an operational perspective
Competitors Competitive set is limited or does not exist (less than 2) Competitive set is moderate (2-6) Competitive set is is very robust for our currents offering(s) (7+)
Pricing Model Pricing terms and mechanics are consistent with current offerings and familiar to the target customer set Pricing terms and mechanics are different from current offerings or unfamiliar to the target customer set (not both) Pricing terms and mechanics are different from current offerings and will
Evidence Based Library and Information Practice 2007, 2:1
32
Evidence Based Library and Information Practice
Feature Article
A Statistical Primer: Understanding Descriptive and Inferential Statistics
Gillian Byrne
Information Services Librarian
Queen Elizabeth II Library
Memorial University of Newfoundland
St. John’s, NL , Canada
Email: gbyrne@mun.ca
Received: 13 December 2006 Accepted: 08 February 2007
© 2007 Byrne. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
As libraries and librarians move more towards evidence‐based decision making, the data
being generated in libraries is growing. Understanding the basics of statistical analysis is
crucial for evidence‐based practice (EBP), in order to correctly design and analyze research
as well as to evaluate the research of others. This article covers the fundamentals of
descriptive and inferential statistics, from hypothesis construction to sampling to common
statistical techniques including chi‐square, correlation, and analysis of variance (ANOVA).
Introduction
Much of the research done by librarians,
from bibliometrics to surveys to usability
testing, requires the measurement of certain
factors. This measurement results in
numbers, or data, being collected, which
must then be analyzed using quantitative
research methods. A basic understanding of
statistical techniques is essential to properly
designing research, as well as accurately
evaluating the research of others.
This paper will introduce basic statistical
principles, such as hypothesis construction
and sampling, as well as descriptive and
inferential statistical techniques. Descriptive
statistics describe, or summarize, data, while
inferential statistics use methods to infer
conclusions about a population from a
sample.
In order to illustrate the techniques being
http://creativecommons.org/licenses/by/2.0
Evidence Based Library and Information Practice 2007, 2:1
33
Great Job Lousy Job
If you accept the job Have a great experience Waste time & effort
If you decline the job Waste an opportunity Avoid wasting time & effort
Figure 1. Illustration of Type I & II errors.
described here, an example of a fictional
article will be used. Entitled Perceptions of
Evidence‐Based Practice: A Survey of Canadian
Librarians, this article uses various
quantitative methods to determine how
Canadian librarians feel about Evidence‐
based Practice (EBP). It is important to note
that this article, and the statistics derived
from it, is entirely fictional.
Hypothesis
Hypotheses can be defined as “untested
statements that specify a relationship
be
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