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Anonymous

**Our organization was Stabucks and the problem was recycling**

**Create** a Microsoft®Excel® spreadsheet with the two variables from your learning team's dataset.

**Analyze** the data with MegaStat®, StatCrunch®, Microsoft® Excel®or other statistical tool(s), including:

(a) Descriptive stats for each numeric variable

(b) Histogram for each numeric variable

(c) Bar chart for each attribute (non numeric) variable

(d) Scatter plot if the data contains two numeric variables

**Determine** the appropriate descriptive statistics.

(a) For normally distributed data use the mean and standard deviation.

(b) For significantly skewed data use the median and interquartile range.

** Use**the

Use

**Format**your paper consistent with APA guidelines.

**Submit** both the spreadsheet and the completed Individual Methodology Findings Template.

**Click **the Assignment Files tab to submit your assignment.

qnt561_r7_individual_methodology_findings_template_week4.doc

qnt561_r7_descriptive_statistics_and_interpretation_example_week4.doc

Running Head: SAMPLING AND DATA COLLECTION PLAN
Sampling and Data Collection Plan
Donna Allare
QNT/561
August 4, 2014
Heidi Carty
1
SAMPLING AND DATA COLLECTION PLAN
2
Sampling and Data Collection Plan
Many critics believe DOTA will struggle to reach its desired goals for recycling cups in
the coming years (Recycling & Reducing Waste, n.d. ). While many of these shortcomings are
due to the inability to recycle hot cups, this study will focus on the presence of store front
recycling units and its relationship with the number of cups recycled at DOTA stores (Recycling
& Reducing Waste, n.d.).
The independent variable that will be sampled in this study will be the presence of frontof-store recycling units at a given DOTA coffeehouse. The dependent variable which will be
observed will be the numbers of cups recycled.
First, the approximately 23,000 stores DOTA stores will be split into two categories,
those that have store front recycling units and those that do not (Recycling & Reducing Waste,
n.d.). Of these, 1000 of each will be chosen via simple random selection (to eliminate bias). The
sample size, therefore, will be 1000 for each of the two categories. A smaller number could be
chosen to make analysis easier, but a larger sample size will increase the accuracy and credibility
of the study. The sample size used for this study is sufficient, given the analysis shown in the
appendix, as it is well above the recommended sample size obtained from a 95% confidence
interval and a 5% margin of error.
Next, the number of cups recycled by each store per year will be determined by
examination of the recycling records kept by each store. While choosing a particular month may
make the analysis easier, using the entire year will avoid inconsistencies between stores that have
peak sales during tourist months or marginal sales during months of poor weather. Utilizing, the
SAMPLING AND DATA COLLECTION PLAN
3
entire year, should help standardize for location based customer dynamics, and will similarly
increase the accuracy and credibility of the study.
The data will physically be obtained by requesting for recycling records from stores.
Stores should be able to provide accounting records of cups recycled, but in the rare instance a
store is unable to deliver the required data, another store will be chosen at random using the
aforementioned sampling method, to replace this store. After the data is collected and obtained, it
will be stored on a secure computer and will be shared amongst the researchers. Great care will
be taken to ensure DOTA’s privacy and the researchers will ensure the data is not made available
inadvertently to parties who are not involved in the research. Utmost care will be taken to ensure
the data is not lost or tampered with and proper research integrity is maintained. A fortunate
aspect of this study will be the lack of risk involved to human subjects or to customers. There
will be no adverse effect on DOTA’s consumers by the implementation of this research, so there
should be little hesitation on DOTA’s part to cooperate with the researchers.
Overall, this study will aim to determine if, in fact, there is a relationship between the
presence of recycling units in front of DOTA stores and the number of cups recycled. The null
hypothesis of this study will be that the sample mean of cups recycled in stores that do not have
store-front recycling units, (𝜇1 ) , will be the same as the sample mean of cups recycled in stores
that have store-front recycling units, (𝜇2 ). On the other hand, the alternate hypothesis will be
that the sample mean of cups recycled in stores that do not have store-front recycling units,
(𝜇1 ) , will be different than the sample mean of cups recycled in stores that have store-front
recycling units, (𝜇2 ).
𝐻𝑦𝑝𝑡𝑜ℎ𝑒𝑠𝑒𝑠 𝑡𝑜 𝑏𝑒 𝑡𝑒𝑠𝑡𝑒𝑑: 𝐻𝑜: 𝜇1 = 𝜇2 , 𝐻𝑎: 𝜇1 ≠ 𝜇2
SAMPLING AND DATA COLLECTION PLAN
As mentioned before, reliability of this study will be obtained by using a sample size
larger than the recommended sample size given a 95% confidence interval and 5% margin of
error. Additionally, the sample mean will represent cups recycled during the entire year to
standardize for location specific customer dynamics. Validity of the study will be achieved by
ensuring that a SRS is utilized to collect the data, and the integrity of the data does not become
compromised. Special care will be taken to ensure the data is secured and all statistical work be
done accurately. Special care will be taken to ensure that all statistical estimates are valid. For
instance, the recommended sample mean, is shown to be a valid estimate and the confidence
interval a valid indicator of the sample mean, given the fulfillment of the criteria discussed in
Appendix 1. Overall, this experimental design will be made reliable and valid not only through
the collection of proper data, but the high effective work of the researchers performing this
study.
4
SAMPLING AND DATA COLLECTION PLAN
5
Appendix 1:
Recommended Sample Size for 95% CI and 5% ME:
Note: In order to calculate a sample size, we would need to have an idea about the standard
deviation in cups recycled, which is data we do not have, prior to carrying out this study.
However, an alternative approach we can use is to use the proportion of stores having in front of
store recycling units to calculate our recommended sample size.
𝑀𝐸 = 𝑧√
(𝑝̂)(1−𝑝̂)
𝑛
𝑧
, solving for n yields: 𝑛 = (𝑀𝐸)2 (𝑝̂ )(1 − 𝑝̂ )
(See References 1, 3 and 4 for more info).
Now, we are given ME =.05, the z-score corresponding to a 95 Confidence Interval is
1.96, and the proportion of stores having store front recycling units will need to be determined.
The proportion of stores having recycling units in front of their stores was 5% in 2011,
since then DOTA has claimed at most a 67% per year increase in 2013. It can be assumed the
percentage was less than that on average per year over the last 3 years (Recycling & Reducing
Waste, n.d.). In order to achieve the most accurate sample size, we should assume that the
increase per year was 67%. This will maximize the denominator in the equation shown below,
thus producing a sample size that overcompensates for or lack of an accurate statistic, and will
still therefore be reliable (See references 1, 3, and 4 for more info).
𝑝̂ = (5%)(1.67)(2014-2011)=(23.29%), therefore the sample size will be:
𝑧 2
1.96 2
) (𝑝̂ )(1 − 𝑝̂ ) = (
) (. 23)(1 − .23) = 272.1
𝑀𝐸
. 05
𝑛=(
SAMPLING AND DATA COLLECTION PLAN
(See References 1, 3, and 4 for more info).
This estimate can be considered reasonably accurate because:
(i)
The sample is a SRS and was obtained from a binomial population (either have
store front recycling or do not have store front recycling).
(ii)
Both n𝑝̂ ≥10 and n𝑝̂ (1 − 𝑝̂ ) ≥10 (See references 1, 3, and 4).
(iii)
The size of the population (23,000+) is at least 10 times the size of the sample.
Therefore, we can assume that if our sample size is greater than 272.14, our study will produce
reliable results (See References 1, 3, and 4 for more info).
6
SAMPLING AND DATA COLLECTION PLAN
7
References
1
Hypothesis Testing of the Difference Between Two Populations Means. (n.d.).
Retrieved July 31, 2014, from http://www.kean.edu/~fosborne/bstat/07b2means.html
2
Recycling & Reducing Waste| Starbucks Coffee Company | Starbucks Coffee Company. (n.d.).
Retrieved July 31, 2014, from
http://www.starbucks.com/responsibility/environment/recycling
3
Statistics Notes Class 23: Retrieved July 31, 2014, from http://www.unc.edu/~rls/s1512010/class23.pdf
4
Statistics Slides Class 10: Retrieved July 31, 2014, from
http://www.csun.edu/~an73773/SlidesClass10F09.pdf

Running head: DESCRIPTIVE STATISTICS
1
Descriptive Statistics
Name
QNT/561
Date
Instructor’s Name
DESCRIPTIVE STATISTICS
2
Descriptive Statistics
Determine the appropriate descriptive statistics.
Note: If the data was normally distributed, use the mean and standard deviation. If the data was
skewed significantly, use the median and interquartile range.
Numeric Variable Name1
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Numeric Variable Name2 (if applicable)
Distribution: State if not normally distributed
Central Tendency:
Dispersion:
Number:
Min/Max:
Confidence Interval: (if distribution is normal)
Attribute Variable Name (if applicable)
Create a bar chart. Describe the proportions.
DESCRIPTIVE STATISTICS
Descriptive Statistics Interpretation
Numeric Variable Name1
Describe the variable in laymen terms.
Numeric Variable Name2 (if applicable)
Describe the variable in laymen terms.
3
DESCRIPTIVE STATISTICS
4
Appendix A
Raw data used in the analysis
Fit data to one page.
DESCRIPTIVE STATISTICS
5
Appendix B
Charts and Tables
This part of the paper will include items that are then cited in the body of the paper. Usually,
large items are placed here not to distract from reading the paper.
DESCRIPTIVE STATISTICS
6
Appendix C
Descriptive Statistics
This part of the paper will include descriptive statistics.

Descriptive Statistics and Interpretation Example
QNT/561 Version 7
1
University of Phoenix Material
Descriptive Statistics and Interpretation Example
Interpretation Phrases
Central Tendency:
Mean = average of a set of data
Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint
in an ordered (sorted) set of data, a physical location
Mode = most frequent value in a set of data
Dispersion:
Standard deviation = variation
Interquartile range (IQR) = the middle 50% of the data
Range = the difference between the largest and smallest value of the data
Confidence Interval: (data must be normal)
There is 95% confidence that the population average is between _____ and ____ units.
Normal or significantly skewed data:
MegaStat: Descriptive statistics Normal curve goodness of fit p-value
•
•
Normal, p-value > .05
Significantly Skewed, p-value < .05
Histogram: Eyeball the histogram.
•
•
Normal data will have a symmetrical or slightly skewed shape.
Significantly Skewed shape will have extreme skewness
Use phrase combinations: Normally distributed: Mean and Standard Deviation, Not normally
distributed: Median and IQR
Copyright © 2014 by University of Phoenix. All rights reserved.
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
2
Descriptive Statistics
Body Weight (Lbs.)
Central Tendency:
Mean = 149 Lbs.
Dispersion:
Standard deviation = 30 Lbs.
Count:
100
Min/Max:
99 pounds and 234 Lbs.
Confidence Interval:
144 to 155 Lbs.
See the histogram in Appendix A, and descriptive statistics in Appendix B.
Age
Distribution is not normally distributed
Central Tendency:
Median = 36 years
Dispersion:
Interquartile Range = 20.5 years / 2 = ± 10 years
Count:
100
Min/Max:
18 years and 74 years
Confidence Interval:
Not applicable (data is not normally distributed)
See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C.
Education Level
Thirteen percent of the subjects have no high school degree while 44% have high school degree.
Forty three percent have a college or college graduate degree. See the bar chart in Appendix D.
Copyright © 2014 by University of Phoenix. All rights reserved.
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
3
Descriptive Statistics Interpretation
Interpretation
Body Weight
One hundred subjects were randomly selected. Their body weight was observed between 99 and
234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half
or more were above 149 pounds. There is 95% confidence that the population body weight average is
between 144 and 155 pounds.
Age
The data was significantly skewed. One hundred subjects were randomly selected. Their ages
were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects
were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The
most frequent age was 36 years.
Copyright © 2014 by University of Phoenix. All rights reserved.
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
APPENDIX A
Body Weight and Age Histograms
Copyright © 2014 by University of Phoenix. All rights reserved.
4
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
APPENDIX B
Descriptive Statistics Body Weight and Age
Copyright © 2014 by University of Phoenix. All rights reserved.
5
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
APPENDIX C
Scatterplot Body Age versus Weight
Copyright © 2014 by University of Phoenix. All rights reserved.
6
Descriptive Statistics and Interpretation Example
QNT/561 Version 7
APPENDIX D
Bar Chart Education Level
Copyright © 2014 by University of Phoenix. All rights reserved.
7

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