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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