Joe Greene, a new manager at Pilgrim Bank wants to better understand profitability data for bank’s customers. Joe is able to obtain a random sample of 31,634 customers on the following variables – Profitability (in $, for the most recent completed year, i.e. 2006), whether or not the customer uses the online banking channel, customer tenure, age and income where available, as well as the customer’s residential area. Descriptive statistics for Profits indicates that the average profit per customer is $111.50 with a standard deviation of $272.84.

a. Is Joe justified in assuming that this is a “large” sample? (see slide 7-14) YES, BECAUSE 31,634 IS A LOT MORE THAN 30 OBVERSATIONS. GENERALLY, THE LARGER THE SAMPLE, THE MORE RELIABLE ARE THE ESTIMATES. THE KEY IS TO HAVE A RANDOMLY SELECTED SAMPLE TO REDUCE THE RISK OF BIASED ESTIMATES

b. Joe has been informed that the bank serves approximately 5 million customers nationwide – should he worry about using finite population correction factor (slide 7-9). FINITE POPULATION CORRECTION (FPC) IS NOT NECESSARY, SINCE THE POPULATION OF APPROXIMATELY 5 MILLION IS QUITE LARGE. FPC IS ONLY RECOMMENDED FOR SMALL POPULATIONS.

c. Joe wants to estimate average profit for the entire population based on his sample. He knows that the “point estimate” for average profit would be $111.50, but, he will need to calculate the margin of error. The first step for this is to calculate the standard error (see slide 7-6); provide the value below. 272.84 / SQRT(31,634) = $1.53

d. The second step in calculating the margin or error (often simply called error) is to multiply the standard error by 1.96; provide the value below. 1.96*$1.53=$3. THUS THE MARGIN OF ERROR FOR OUR ESTIMATE OF AVERAGE PROFIT PER CUSTOMER, FOR THE ENTIRE CUSTOMER BASE WILL BE $3

e. Joe can combine responses from c and d to report the estimated average profit customer for all the bank’s customers as...

...Simple Random Sampling
3.1 INTRODUCTION
Everyone mentions simple random sampling, but few use this method for population-based surveys. Rapid surveys are no exception, since they too use a more complex sampling scheme. So why should we be concerned with simple random sampling? The main reason is to learn the theory of sampling. Simple random sampling is the basic selection process ofsampling and is easiest to understand. If everyone in a population could be included in a survey, the analysis featured in this book would be very simple. The average value for equal interval and binomial variables, respectively, could easily be derived using Formulas 2.1 and 2.3 in Chapter 2. Instead of estimating the two forms of average values in the population, they would be measuring directly. Of course, when measuring everyone in a population, the true value is known; thus there is no need for confidence intervals. After all the purpose of the confidence interval is to tell how certain the author is that a presented interval brackets the true value in the population. With everyone measured, the true value would be known, unless of course there were measurement or calculation errors. When the true value in a population is estimated with a sample of persons, things get more complicated. Rather then just the mean or proportion, we need to derive the standard error for the variable of...

...12
SAMPLING MECHANICS
Sampling is an activity that involves the selection of individual people, data or things, from a target population/universe.
A population, or universe, is the entire set people data or things that is the subject of exploration.
A census involves obtaining information, not from a sample, but rather from the entire population or universe.
A sample (as opposed sampling) is a subset of the population/universe.
For Marketing Research purposes, sampling usually involves people, not data or things.
Sampling Plans are strategies and mechanics for selecting members of the sample from the population:
1. Define the population. It is usually limited based on some set of characteristics, e.g., males, aged 21-39, who have consumed alcoholic beverages within the past 3 months for a beer study.
2. Choose data collection methodology. What kind of information do you require from the sample, how will they be identified, where are they available, etc.
3. Set sampling frame. This is as exhaustive a list as operationally and economically possible that represents the population and is also accessible utilizing the selected methodology.
4. Choose sampling method.
• Probability samples are those that allow all members of the sampling frame an equal opportunity of selection. Probability samples include Simple Random,...

...
Sampling and Data collecting Plan
October 9, 2014
University of Phoenix
QNT/561
Team D has chosen to look into whether or not should Pear Inc. should start putting their resources into either Laptops or tablet electronic devices. Putting resources into a venture that may or not pan out could be detrimental to that same company. As an example; Kodak and their choice not to expand into digital, something they created, and stay the course with their polyurethane film (Mui, 2012).In the end the choice the public made was to go to digital and have the image saved for ever instead of the inevitable fading of pictures and the breakdown of the film making Kodak play catchup.
Population, Size, and Target
When considering of finding the population we have chosen social media for all sexes, ages, financial, and country demographics. The reasoning behind the choice is Pear’s product utilizes this source of media in particular. In addition social media is heavily used by the same people who frequent these sites. The sampling would reflect on Pear’s target audiences assisting in the choice of which direction to go with Pear’s resources. It determining size team D would like to obtain a min of 10% of the population of random sampling. Due to the time limit restraint of the weeks leading up to week 4 team D would like to see a 300 individual response to the choices of our chosen sampling element.
Sample...

...
Sampling and Data Collection Plan
Name
QNT/561
October 27, 2014
Instructor
Introduction
Royal Blue Airlines is a medium sized airline company offering flights throughout the United States, Mexico, Caribbean Islands, and Latin America. The company operates a fleet of Boeing 737 aircrafts and is in the process of replacing older ones with newer, state-of-the-art planes. These new planes are very expensive, so management wants to maximize passenger count. A study has been authorized to determine: Is there is a difference in the number of passengers taking flights (DV) that are based upon certain days of the week (IV)?
Population and Size
The population for this study consists of airline passengers that numbers in the millions. This population is a vast number of subjects and unrealistic to gather data on each one. The target population will be chosen, and inferences will be made about how the results also apply to the entire population.
Target Population
By narrowing the research to just passengers on Royal Blue Airlines, the study becomes more manageable. Royal Blue Airlines stores data on each flight so they have already had a head start on determining customer trends. By using their company’s data, the cost of the project is drastically reduced and will offer greater insight to answer the research question.
Sampling Element
A survey consisting of six questions (Appendix A) will be handed out to all passengers during...

...CHAPTER 7—SAMPLING AND SAMPLING DISTRIBUTIONS
MULTIPLE CHOICE
1. From a group of 12 students, we want to select a random sample of 4 students to serve on a university committee. How many different random samples of 4 students can be selected?
a.|48|
b.|20,736|
c.|16|
d.|495|
ANS: D
2. Parameters are
a.|numerical characteristics of a sample|
b.|numerical characteristics of a population|
c.|the averages taken from a sample|
d.|numerical characteristics of either a sample or a population|
ANS: B
3. How many simple random samples of size 3 can be selected from a population of size 7?
a.|7|
b.|21|
c.|35|
d.|343|
ANS: C
4. Sampling distribution of is the
a.|probability distribution of the sample mean|
b.|probability distribution of the sample proportion|
c.|mean of the sample|
d.|mean of the population|
ANS: A
5. A simple random sample of 100 observations was taken from a large population. The sample mean and the standard deviation were determined to be 80 and 12 respectively. The standard error of the mean is
a.|1.20|
b.|0.12|
c.|8.00|
d.|0.80|
ANS: A
6. A population has a standard deviation of 16. If a sample of size 64 is selected from this population, what is the probability that the sample mean will be within 2 of the population mean?
a.|0.6826|
b.|0.3413|
c.|-0.6826|
d.|Since the mean is not given, there is no answer to this question.|
ANS: A
7....

...SAMPLINGSAMPLING
SAMPLING DISTRIBUTION
(THEORETICAL)
SAMPLING TECHNIQUES (APPLIED)
8/13/2014
QM_Session 14 15
SAMPLING TERMS
An unit/element is the entity on which data are collected.
A population is a collection of all the units/elements of
interest.
A sample is a subset of the population.
The sampled population is the population from
which the sample is drawn.
A frame is a list of the elements/units that the sample will
be selected from.
8/13/2014
QM_Session 14 15
Parameter and Statistic
Parameter is a
population
characteristic
Eg. µ , P, σ
Statistic is a sample
characteristic
Eg. x , s, p
Using Sample
• Statistical Inference:
On basis of sample statistics
derived from limited and
incomplete sample
information
Predict and forecast values of
population parameters...
Estimate and test hypotheses
about values of population
parameters...
Make decisions...
Make generalizations
about the
characteristics of a
population...
8/13/2014
On the basis of
observations of a
sample, a part of a
population
QM_Session 14 15
Selecting a Sample
Sampling from a Finite Population
Sampling from an Infinite Population
8/13/2014
QM_Session 14 15
Sampling from a Finite Population
Finite populations are often defined by lists such as:
Organization membership roster
Credit card account numbers
Inventory product numbers
A simple...

...Population and Sampling
MTH/231
August 29, 2012
Importance of Population and Sampling
History from Political Arithmetic to Statistics
The history timeline show evidence of statistical data as early as Ancient Greece time but records show statistics in late 16th century, when it was introduced by, John Graunt, William Petty, and Pascal and later in 17th century by Gottfried Achenwall. It was an exciting time when success and discoveries raised the confidence of scientists, physicist and astronomers to think that laws of nature are not of divine intervention. As the time evolved and new discoveries were attained from political arithmetic like, mortality demographics, census data, economy, and International Statistical Congresses, they all led to changing its name to ‘statistics’.
Population
Every 10 years the country conducts a census of population to provide data that can be of use for research, business marketing, planning, surveys, and different sampling. The first U.S. census took place in 1790. What is ‘population’? The common term “population” describes people that live in a town which is located in a certain region within a certain county or state and their respective characteristic such age, sex, ethnicity, marital status, or other. The statistic term
“population” consist of all members, elements of the defined group. It includes all subjects to be studied or collecting information on for data driven...

...mocked by the townsfolk.
Help- Fellow pilgrim who helps pull Christian from the Slough of Despond.
Worldly Wiseman- A reasonable and practical man whom Christian encounters early in his journey Worldly Wiseman tries unsuccessfully to urge Christian to give up his religious foolishness and live a content secular life.
Formalist- A traveler whom Christian meets along the wall of Salvation. With his hypocrisy, Formalist sneaks over the wall, instead of following the straight and narrow as Christian did.
Hypocrisy- Formalist’s travel companion
Discretion- One of the four mistresses of the Palace Beautiful. Discretion takes Christian and feeds him.
Piety- one of the four mistresses of the palace Beautiful. Piety asks Christian about his journey so far.
Prudence- One of the four mistresses of the Palace Beautiful. Prudence tries to understand Christian’s purpose in traveling to Mount Zion
Charity- One of the four mistresses of the palace Beautiful. Charity asks Christian why he did not bring his family, which causes him to weep.
The Interpreter- Spiritual guide who shelters Christian. The Interpreter instructs Christian in the art of reading religious meanings hidden in everyday objects and events, which he houses in his Significant Rooms.
Shining Ones- Three celestial creatures who clothe Christian with the new garments and give him the certificate. The Shining Ones act as guardians throughout Christians journey.
Faithful-Fellow pilgrim...