Tutorial Solution Week9

Tutorial Solution Week9
BES Tutorial Sample Solutions, S1/13 WEEK 9 TUTORIAL EXERCISES (To be discussed in the
week starting May 6) 1. Perform the following hypothesis tests of the population mean. In each
case, illustrate the rejection regions on both the Z and ̅ distributions, and calculate the p–value
(prob–value) of the test. (a) H0: μ = 50, H1: μ > 50, n = 100, ̅ = 55, σ = 10, α = 0.05 Rejection
region: ̅ 50 1.645 . 10⁄√100 Alternatively 10 51.645 50 1.645 ̅ ̅ . √ √100 Since 55 50 5 1.645 . 10⁄√100
Can reject H 0 and conclude that the population mean is greater than 50 . 0.05 50 51.645 reject X 1
0.05 0 1.645 reject Z 5 (b) 0.0000 H0: μ = 25, H1: μ < 25, n = 100, ̅ = 24, σ = 5, α = 0.1 ̅ 25 . ...
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distribution of √ 6 The distribution of is:  X  n  0  0  z But suppose the true  is to the
right of . Then the true distribution of is say:  0   0  z The shaded area in the above
diagram gives the probability of correctly rejecting H0 (i.e. the power, 1‐ which is greater than
) Now suppose the sample size is increased. As a result: decreases & hence Suppose the new
sample size is n1>n. √  n decreases. 7 The distribution of will now look something like:   n1
0  0  z X Note that with a fixed  the rejection region cutoff is now smaller. Again, if the
true  is actually to the right of , the probability of rejecting the same incorrect null hypothesis
is higher than before. Diagrammatically the true distribution of will be say X  0   0  z  n1
Again the shaded area in the above diagram gives the probability of correctly rejecting H0.
Conclusion: The probability of correctly rejecting a false H0. (the power
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Basic Terminology For A State Of Affairs
Basic Terminology: Unit Three
Sheila Elyse Brooks
Stanbridge College
April 27, 2015
Introduction As we continue our journey through the "trials and errors" of understanding basic
statistical terminology. Let 's focus our attention on the following: understanding what is the
difference between a hypothesis, statistical hypothesis, and an experimental hypothesis. In addition,
I want to explore how researchers determine the appropriate sample size. Now, some of you might
be asking the question "what do we do once we have our hypothesis and sample population?" Well,
now is a wonderful time to start experimenting with statistical methods like the chi–square test. I
will explain this later on.
Hypothesis
A ... Show more content on Helpwriting.net ...
However, most clinical research associates do not hold medical degree of any type. This makes it
rather to master the skills I need when I see my patients. Often, we are not allowed to open any lab
kits (because they are in sequential order) which makes nurses prone to more mistakes. When I first
came to Mount
Carmel, I notice we had simulators. So, I began to think how these simulators could reduce the
clinical errors in research. My hypothesis (state of affairs) could be: Clinical research nurses who
practice on simulators are more likely to have fewer medical errors then those who do not. My
Experimental hypothesis: Clinical research nurses are 25% more likely to make mistakes when
performing clinical skills (such as lab draws and administering study medication) then those nurses
who practice on patient simulators. My experiment: Compare the performance of nurses who use
patient simulators to practice specific study related skills versus those who do not. After you figure
out your hypothesis, you must consider what makes a "good estimate".
Making Good Estimates We obtain a sample in order to obtain a statistical measurement such as a
mean form our observation. One can say that different sample sizes would produce different values
or variations. The variation between these individual estimates is due to sampling error (Fowler et
el, 2002). It is important to note that sampling

Hcs 438 Entire Class /All Assignments , Dqs and Quizzes...
HCS 438 Entire Class /All Assignments ,DQs and Quizzes Version 3
Click Link Below To Purchase:
http://homework–aid.com/HCS438–Statistical–Applications–Complete–Class–All–Weeks–118.htm
HCS 438, Week 1, Individual Assignment, Sample of Chart or Graph
Find a chart or graph related to the health care field to complete this assignment. The chart or graph
should be related to an article or other supporting documentation.
Respond to the following questions in bullet format. Use complete sentences.
· Identify the type of chart or graph and what this type of chart or graph usually depicts. Pie charts,
for example, show portions of the whole and line charts show trends over time.
· Was the proper chart or graph used to present this ... Show more content on Helpwriting.net ...
Obtain instructor approval for your selected study.
Answer, individually, the following questions about the study. Discuss your answers with the team.
· How were measures of central tendency used in the study? Did the study use the most appropriate
measure of central tendency for the given data? Why or why not?
· How were measures of variation used in the study? What conclusions can you draw based on the
variation?
· Compare the measurements in the study with the standard normal distribution, what does this tell
you about the data?
· Based on your review of the statistics in the study, do you agree with the study's conclusions? Why
or why not?
Summarize the study; include the key points and conclusions.
Prepare a 10– to 15–slide Microsoft® PowerPoint® presentation in which you present your
findings. Online students must include speaker notes. HCS 438, Week 4, Individual Assignment –
Analysis of Research Report Paper
Prepare a 700– to 1,050–word paper in which you interpret the statistical significance of a study.
Select a study in a field of interest; this does not need to be directly related to health care.
· What statistical procedures are mentioned in the study?
· What conclusions did the study reach? Are the conclusions appropriate? Why or why not?
· Are the findings statistically significant? Why or why not? Describe the process you used to make
this determination and provide the level of significance.
Format

A Short Note On Diabetic Retinopathy ( Dr ) Is The...
Abstract– Diabetic Retinopathy (DR) is the deterioration of human eye as a result of increase in the
blood glucose level. Longer the patient has DR, higher the chance to develop purblind. The robust
detection of lesions in digital colour fundus images is an important step in the development of
automated screening system for diabetic retinopathy. In this work a novel method is introduced for
automatic detection of red lesions in the fundus image. A new set of shape features extracted from
the detected red lesion called the dynamic shape features that differentiate between the lesions and
vessel segments. The detected lesion candidates are classified using dynamic shape features based
on the medical values. The simulation analysis indicates that the proposed work is better than the
previous works in terms of accuracy, sensitivity, precision and specificity.
Keywords: Diabetic retinopathy, Fundus, Lesions, Dynamic shape features, Retina
Introduction
Diabetic Retinopathy (DR) affects the diabetic patients. Generally diabetics are of three types Type
I, II and III. The Type I diabetic is due to the genetic predisposition, Type II diabetic which usually
affects the adults. This is owing to over weight of children beyond their age limit and Type III is
seen only in pregnant women. The patients with Type I diabetics will only suffer from DR which
influence the retina. This leads the way to damage of retina and finally blindness.
DR is caused by red lesion which is composed of

Statistics And Its Impact On The Quality Of Data
Statistics is defined as a branch of mathematics used to analyze, explain summarize as well as
interpret what we observe– in order to make sense or meaning of our observations. Every day in life
we encounter information that originates from diverse forms and ways. This means that to make this
information to have sense, there is the need to use statistics. However, due to its focus and empirical
on applications, statistics is classically considered a distinctive mathematical science rather than a
branch of mathematics (Chance et al, 2005). Thus, some tasks a statistician use are less
mathematical; for instance, making sure data collection is carried out in a manner that yields valid
conclusions, reporting results, or coding data in ways understandable to the users. Statistics is
known to improve the quality of data by fashioning specific survey samples and experiment designs.
It also offers tools used to predict and forecasting the use of data as well as statistical models. It is
applicable in many academic fields that include business, government, social and natural sciences.
Descriptive statistics are entirely used to describe the sample under study. They are used basically to
describe the fundamental characteristics of a given data. They offer simple summaries concerning
the measures and the samples. When utilized together with simple graphics study, they form the
heart of practically each quantitative study of data. This means that descriptive statistics utilized
both

The Royal Theatre And Its Consortium
Executive Summary The following highlights the main issues affecting the Royal Theatre and its
consortium: since the issues is in regard to a claim exists on the basis of fraudulent
misrepresentation against the surrounding theatres and the Royal Theatre, a statistical analysis was
required to collect data from movie–goers to lead to an ethical conclusion. The analysis performed
includes inquiries in regard to the legality of Tommy's claim and whether it is justifiable under the
rule of misrepresentation; both fraud and innocent misrepresentation have been considered. In
addition, surveys were collected from groups of movie–goers, and the analysis concludes the
percentage of patronage preferences. It is recommended that Mr. Plex, and the ... Show more content
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Tommy indicates that the duration of time in which commercials were playing was 20 minutes.
Tommy was unaware that the theater would be showing commercials. Although he was unhappy
with the commercials, he stayed until he was discontent with the movie that he had paid for.
Ultimately, Tommy was denied a refund, as it is a policy of the Royal theatre; therefore, Tommy has
expressed his intent to initiate a class action lawsuit believing that his rights were violated. Analysis
Liability for Fraud: What Tommy Must Prove to Win Assuming that a contract existed at the time of
the event, between Tommy and the Theater, Tommy may use the following argument to prove his
fraudulent claim. Tommy may argue that (1) a representation was made and that was that one of the
terms of the contract provides that the movie will begin at 1 PM. Thus, he was induced by a
fraudulent claim because the movie started 20 minutes after commercials ended. Tommy may also
suggest (2) the representation was known to be false because the ticket stub indicated the movie
would start at 1 p.m. Therefore, (2) the plaintiff was expected to rely on the fact that the movie was
starting at 1 p.m.; moreover, (3) it was known to be false because the newspaper ad stated the time,
(4) and it was made with the intention that the plaintiff rely on it because the cashier at the ticket–
stub affirmed it, (5) the

The Conceptual Rationale Was Appropriate For The Study Essay
Given the statement of the problem, the conceptual rationale was appropriate for the study.
Method
Researchers use particular methodologies to guide the study, collect samples, determine a study
population, and describe interventions. The method of a research study should include protection of
participants' rights, research design, population and sample, data collection and measurement, and
procedures. Researchers should state how they protect human subjects' rights when they collect data.
Research design should also be explained in details to demonstrate the purpose of a study and
whether or not the design is the strongest alternative to use. Researchers should show the evidence
of informed consents and an approval by an institutional review board (IRB) if human subjects are
involved in a study. A target population and samples should be also included in methodologies. If
power analyses are used, researchers should explain the results of the statistics and whether or not it
is significant. Internal and external validity should be also evaluated to minimize biases. Potential
threats to internal validity include temporal ambiguity, selection, history, maturation,
mortality/attrition, and testing and instrumentation. In addition to the internal validity, the potential
threats to external validity are composed of interaction between relationship and people and
interaction between causal effects and treatment variation (Polit & Beck, 2012).
Protection of participants' rights.

Type I and Type Ii Errors and Land Law
AGUILAR, Janica Mara Y. BERBOSO, Kevin Leo C. CAYUBE, Katherine Kate Z. Prof. Ria
Sagum Advisor PHILEX: Philippine Land Law Expert Chatbot Abstract Keywords: Chatbot,
Natural Language Processing (NLP), First–Order Predicate Logic, Natural Language Generation
(NLG), Precision and Recall The researchers tend to work on an expert system with integration of
Philippine land laws. They chose to focus on land laws since one of the major problems of the
Filipinos who seek law experts' advice are due to land. The researchers deliberates the possible ways
on how can they share knowledge and guidance that works like a real lawyer advising a client but
have not taking an account to replace to seek a real land law experts ... Show more content on
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This study also intends to identify the evaluation of the experts when it comes to system's user–
friendliness, speed of response, and completeness. Five (5) land law experts chose twenty (20) test
questions used to assess the system's competency. Conclusion made by the experts based on the
results is PHILEX, being the system for giving answers to user's questions pertaining to Philipine
land laws, is acceptable as a reliable source of knowledge and guidance without intending to replace
the professional practitioners. The system got an overall percentage in terms of accuracy, a 75%,
specificity with the percentage rate of 55% and sensitivity with 80.8%. Summing up the scores in
terms of user–friendliness, speed of response, and completeness resulted to an overall weighted
mean of 4.36, 4.32, and 4.16 respectively, interprets that the system as an acceptable tool for land
law consulations. In the future, the authors would like to extend their work and knowledge by
making the system possible to access by everyone through web or what we called "web–base
application" CARLOS, Philip P. RAMOS, Joseph R. ROSARIO, Roderick M. TAMBUNGUI, Gian
Paolo C. Prof. Michael B. Dela Fuente Advisor CIVIA: College of Computer Management and
Information Technology Interactive Virtual Information Assistant Abstract Keywords: information
assistant,

The Tail Signifying A Positive Relationship
c. It should appear in the tail signifying a positive relationship, with .05 in that tail. d. It should
appear in the tail signifying a negative relationship, with .05 in that tail. An administrator working in
a child guidance center tests the hypothesis that family income will be related to number of
treatment sessions attended. Her sample size is large, and her level of significance is .05. How
should her critical region appear in her theoretical sampling distribution? a. It should be split
between the right and left tails, with .05 appearing in each tail. b. It should be split between the right
and left tails, with .025 appearing in each tail. c. It should appear in the tail signifying a positive
relationship, with .05 in that tail. d. It should appear in the tail signifying a negative relationship,
with .05 in that tail. Slide 15: Here's the same curve demonstrating the z–score values for 2.5% at
each tail. Slide 16: This curve demonstrates a one–tail hypothesis with the critical region
representing 5% showing a positive relationship. Slide 17: This curve demonstrates a one–tail
hypothesis with the critical region representing 5% showing a negative relationship. Please answer
the following quiz questions. Which of the following statements is true about one–tailed tests of
significance? a. The critical region is at both ends of the theoretical sampling distribution. b. They
increase the chances of reaching the critical region if the results

Do Women Prefer Dark Colors Than Men?
Statistic Final Project: "Do women prefer dark colors than men?"
By: Andrea Motta
Previous Studies of this topic
According to the study done by Guilford and Smith (1959), there was found that men are generally
more attracted toward achromatic colors than women. In addition, they also proposed that women
might be more conscious of the colors and their color preference is more flexible and diverse. Also,
years later, McInnis and Shearer (1964) stated that 56% of men and 76% of women preferred cool
colors, and 51% men and 45% women chose bright colors. In a similar study, Plater (1967) stated
that men usually prefer stronger chromes than women.
Statement of the problem
Although there have been many studies about the relationship of colors ... Show more content on
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(Specifying if it is dark or light/bright color)
The response of the total 100 people sample was described as: 10% of men and 9% of women prefer
dark clothes and dark color for their car. 12% of men and 22% of women prefer light/bright clothes
and light/bright color for their car. 26% of men and 21% of women does not have a relationship in
color.
Analysis of the Data
The samples and results are not limited to a specific area because of the use of social media, but it
was simple random sample.
Descriptive Data Analysis: A pie chart was used to describe the sample data since there were only
two categories of individuals (Men and Women). The pie chart reveled that the majority of
respondents were Women (52%) compared to Men (48%). On one hand, the 52% of women were
distributed as, 42% preferred light colors, 17% preferred dark colors, and 41% did not have
preference of colors (figure 1). On the other side, the 48% of men were distributed as, 25% of light
colors, 21% of dark colors, and 54% did not have color preference (figure 2). Test Analysis: Chi–
square analysis was done using TI–84plus to identify the association of gender and preference of
color. The claim that the color chosen is dependent of the gender is tested at ∝=0.05.
Hypothesis

Ho: The color chosen is independent of the gender.
Ha: The color chosen is dependent of the gender. (Claim) Test Statistic (TI–84plus): 2ND + X–1
Edit (Enter data from figure 3, excluding labels) STAT

The Psychological Impacts Of Incarceration
The problem to be addressed is the recognition of incarceration impacting adversely on some
prisoners, while others cope relatively well with the stresses of confinement to a maximum security
prison subculture. This topic will be analyzed using a quantitative approach. Since the publication of
The Prison Community (Clemmer, 1940) virtually thousands of books and articles have been
published on prison life and the adjustment of prisoners to their confinement. One article highlights
this exact problem titled The Psychological Impact of Incarceration: Implications for Post Prison
Adjustment (Haney, 2001). Mental illness and developmental disability represents the largest
number of disabilities among prisoners. Purpose Statement The purpose of this experiment will be
to test the theory of the psychological impacts of incarceration that relates to inmate behavior while
they reside in a restricted subculture paying close attention to two specific variable age, and marital
status. The independent variable of having the advantage which can be defined as misclassification
of offender. The dependent variable would be misconduct of inmate behavior based on age ranges.
The purpose will be achieved determining the maladaptive responses such as the increased mortality
rates, self–mutilation, and prison misbehavior during the early phases of incarceration (first five
years of a long term sentence). The sample will consist of inmates who are convicted of murder in
comparison to the rest

Inferential Statistics in Business
In today's world, we are faced with situations everyday where Statistics can be applied. In general,
Statistics is the science of collecting, organizing, and analyzing numerical data. The techniques
involved in Statistics are important for the work of many professions, thus the proper preparation
and theoretical background of Statistics is valuable for many successful career paths. Marketing
campaigns, the realm of gambling, professional sports, the world of business and economics, the
political domain, education, and forecasting future occurrences are all areas which fundamentally
rely on the use of Statistics. Statistics is a broad subject that branches off into several categories. In
particular, Inferential Statistics contains two ... Show more content on Helpwriting.net ...
The distribution of the test statistic under the null–hypothesis is derived from the assumptions
identified previously. Common test statistics may follow the following distributions: Normal,
Student T, and Chi–Square. This distribution separates the possible values of the estimator into two
categories: values for which the null–hypothesis is accepted or rejected. The region for which we
accept the null–hypothesis is called the critical region and the area underneath the curve that
corresponds to the critical region is known as the level of confidence. Hence, we can develop a
confidence interval for which we can see the lowest and highest point of the critical region. Any
observed sample mean that lies outside of this confidence interval (outside the critical region) would
cause us to reject the null–hypothesis in favor of the alternative hypothesis. The area of the rejection
region is known as the level of significance and represents type I error (alpha) corresponding to the
probability that a true null–hypothesis is rejected (as opposed to type II error– beta; the probability
of accepting a false null–hypothesis). Essentially, hypothesis testing calls for comparing a test
statistic to the critical value of the test statistic. If this test statistic is greater than the critical value of
the test statistic, we will reject the null hypothesis in favor of the alternative hypothesis. If

Critical Assessment And Communication Of Policy
Conceptual Framework
The theoretical framework guiding this study conceptualizes policy analysis as a multidisciplinary
inquiry aimed at the creation, critical assessment and communication of policy–relevant
information. Policy analysis is a problem–solving discipline. To solve practical problems, the
analysis draws on social science methods and theories (Dunn, 2012).
The methodology of policy analysis is a process of "reasoned inquiry aimed at finding solutions to
practical problems" (Dunn, 2012 pp. 3). Policy analysis is methodologically heterogeneous; it is not
confined to a specialized social science field or constrained by the doctrines and principles of
philosophies of science. Rather, as long as the research yields reliable knowledge, policy analyst
may draw from a wide range of scientific methods, both qualitative and quantitative. The separation
between science and non–science is a pseudo–problem. Nonetheless, the focus should be placed on
the distinction between reliable and unreliable knowledge (Grünbaum, Cohen, & Laudan, 1983).
Policy analysis is partly descriptive and partly normative. Traditional social science disciplines are
descriptive, which they describe and explain the cause and consequence of the policy. Whereas,
normative refers to value judgment about what ought to be, in contrast to descriptive statements
about what is (Dunn, 2012, Friedman, 1953). To investigate problems of efficiency, policy analysis
draws on normative orientation. This

RES 341 Final Exam with Instructor Notes
University of Phoenix OnlineCourse: RES / 341Final Exam(Chapters 9+17 from Applied Statistics
in Business and Economics )SOLUTIONPlease mark one answer for all multiple choice questions
with RED!Chapter 91. In hypothesis testing, Type I error isA) always equal to 5 percent.
B) always smaller or equal to 5 percent.
C) the probability of rejecting H0 when H0 is true.
D) the probability of rejecting H0 when H1 is true.
Answer: C2. In hypothesis testing, Type II error isA) equal to 1 – probability of committing Type I
error.
B) equal to 5 percent or more.
C) the probability to accept H0 when H0 is true.
D) the probability to accept H0 when H1 is true.
Answer: D3. The probability of Type I error, , and the probability of Type II error, ... Show more
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D) has all of the above characteristics.
Answer: D15. The critical valueA) is calculated from the sample.
B) is usually 0.05 or 0.01.
C) divides the acceptance region from the rejection region.
D) is determined by the test statistic.
Answer: CUse the following to answer 16–18Dullco Manufacturing claims that its alkaline batteries
last forty hours on average in a certain type of portable CD player. Tests on a random sample of 18
batteries from a day's production run of 2,850 batteries showed a mean battery life of 37.8 hours
with a standard deviation of 5.4 hours.

16. The test statistic isA) 1.980B) 1.728C) 2.101D) 1.960Answer: B17. In determining the p–value
for reporting the study's findings, which of the following is true?A) The p–value is less than .05.
B) The p–value is equal to .
C) The p–value is greater than .05.
D) The p–value cannot be determined without specifyingAnswer: A18. In a left–tailed test at α = .05
we wouldA) comfortably reject the claim.
B) comfortably accept the claim.
C) feel uncomfortable with either decision (too close).
D) switch to alpha of 0.01 for a more powerful test.
Answer: A19. Which of the following statements are correct?A) For a given level of significance,
the critical value of Student's t increases as n increases.
B) A test statistic of t = 1.813 with d.f. = 15 leads to a clear–cut decision.
C) It is harder to reject the null hypothesis when conducting a two–tailed test rather than a

The Three Levels Of Analytics, Descriptive, Predictive,...
1. Describe each of the three levels of Analytics (Descriptive, Predictive, and Prescriptive). Give a
brief example of how they might be used to solve business decisions. Descriptive: Descriptive
Analytics, probably the most common type of analytics, is the process of describing and evaluating
the historical data and recognizing patterns from samples. It serves as a foundation for more
advanced analytics. An example of Descriptive Analytics would be discovering and reporting trends.
Predictive: Predictive analytics uses data mining and text analytics, and well as predictive modeling
to anticipate what will happen forthcoming based on insights discovered through descriptive and
diagnostic analytics. Some of these trends and events discovered may not have been so obvious
descriptive analysis. An Example of predicative analytics is the ability to identify risks and
opportunities for future events in terms of sales, production and more. Prescriptive: Prescriptive
analytics is the process of evaluating and creating new methods of operation to create and achieve
business objectives whilst balancing the constrictions. Prescriptive analytics uses predictive analytic
models, guidelines, scoring, and optimization methods to advocate a course of action, and the
resulting expecting outcomes. Prescriptive analytics provides evaluations of different possible ways
to continue, and comprehend the significances of each of those actions in a timelier manor. Informed
decisions can

The Effects Of Supervision And The Types Of Changes That...
Understanding the effects of supervision, and the types of changes that may take place under that
supervision, can contribute to developing programs to assist in a wide variety of areas. In the article,
"Changes in the effects of process–oriented group supervision as reported by female and male
nursing students: a prospective longitudinal study", authors Arvidsson, Baigi, and Skärsäter (2008)
research the reports of male and female nursing students on changes in the effects of process–
oriented group supervision (PGS). The study took place over the course of a 3–year study period
(2002–2005) at a university in south Sweden, and included a study group of nursing students (n =
183) followed over course of their studies. A questionnaire was used to assess changes brought on
by PGS, and contained the three subscales of supportive, educational, and developmental
(Arvidsson et al., 2008). In addition to these subscales, there were also items for age, gender, and
previous experience in healthcare; which is used to give a breakdown of the overall effects of PGS
(Arvidsson et al., 2008). A t–test was used to make a comparison of the first and third year of studies
across the educational, supportive, and developmental subscales (Arvidsson et al., 2008). Although
specific research hypotheses are not given in the article, based on the information presented it is
possible to construct the research hypotheses and corresponding null hypotheses with a fairly high
degree of accuracy. The

What Is Security Screening?
Screening is part of the airports' security processes and its objectives are to deter, prevent, and
respond to criminal acts that may impact the safety and security of the flying public (Wells &
Young, 2003). The purpose of screening is in line with the objective of aviation security and it is
part of the multiple layers of aviation security. Albeit there is major criticism against security
screening mainly because it has failed to catch any terrorist while screening has become more
frustrating to passengers because of the wait in the queue and the ridicule of security screening
process itself. However, screening serves as a basic need and it works, because it has been
identifying weapons such as guns. This essay serves to provide a view on both sides of the argument
on security screening.
Security at commercial service airports includes passenger screening and checked–baggage
screening amongst other security protocols. Passengers are screened based on a passenger–screening
system (PSS). The system incorporates screening and profiling by labeling the passenger as either
low or high risk, if the passenger is considered low risk and is also not chosen at random to receive
additional scrutiny, the passenger passes through the metal detector and sends their carry–on bags
through the x–ray machine, and this process is called primary screening; if the passenger is labeled
as high risk, they have to pass through primary screening followed by a more thorough search of
their

Current Population Survey Paper
Introduction (State and Plan) As a group of young adults, we are interested in the correlation of a
person's age and their total family income. We chose these variables because we want to learn if the
general assumption of total family income increases as a person ages is true. Especially since most
people tend to get promotions in their respective career fields over time leading to an increase in
wages or get married leading to a general increase in total family income. In order to study these
variables, a statistical analysis of age and total family income is needed. For this we will compare
the two quantitative discrete variables, total family income and age, from the Current Population
Survey (CPS) data set using histograms, a ... Show more content on Helpwriting.net ...
This means the data was statistically significant and we can reject the null hypothesis. The
alternative hypothesis states that as an individual ages after the age of 18, their total family income
will increase. A two way table test for homogeneity for a characteristic distribution was performed
to analyze the data which included two populations, 18–45 and 45–96 age groups. Individuals
between the ages of 45–96 have a higher total family income in the income brackets of $0–$60,000
and $60,000–$250,000. However, a Type I Error may have occurred where the null hypothesis was
rejected, but in fact was true. This means that individuals between the ages of 18–45 do have a total
family income greater than or equal to the individuals between the ages of 45–96. A consequence of
this this error could be that individuals between the ages of 18–45 going to jail for tax fraud. A Type
II Error on the other hand, would mean that we failed to reject the null hypothesis when the null
hypothesis is in fact false. This means that individual's total family income between the ages of 18–
45 is not greater than or equal to the total family income of individuals between the ages of 45–96. A
consequence of this could be the government believing that individuals in the 18–45 age range make
more money than what they actually do thus failing to allot the necessary financial and government

The Effect Of Gmms On Study Medication Adherence Has...
Our systematic review demonstrates that the use of GMMs to study medication adherence has
increased considerably in the past few years, with no studies having been conducted prior to 2010.
This is likely a reflection of the fact that GMMs are a relatively newer statistical technique. For
instance, Nagin first introduced GBTMs in 1999, and Muthén introduced GMMs in 2001.23, 24,
According to a literature search conducted by Nagin and Odgers, a small, but rapidly developing
clinical literature base has emerged since these models were first proposed – from 2000 to 2008,
applications of these methods to clinical studies increased from 8 to 80 publications per year.25
Thus, it is perhaps unsurprising, that increased familiarity through application in other clinical areas,
led to eventual adoption and popularization of the technique in studying medication adherence.
GMMs (and their GBTM counterparts) provide a sophisticated statistical method by which to
capture the heterogeneity within a given population, and often map onto how many researchers
conceptualize growth, in that, different types of individuals likely exhibit different trajectories of
behavior. Given the rapid adoption and application of such models within the clinical literature, and
the fact that this modeling technique is relatively novel, it stands to reason that there may be certain
methodological nuances which have been overlooked or ignored by researchers. Our review reveals
several problematic issues related to

Why The Airline Southwest Meets Or Exceeds The National...
The question addressed is if the airline Southwest meets or exceeds the national rate for on–time
performance at the Oklahoma City airport. The data collected from the BTS is categorized into
either on–time or not on–time for Oklahoma City and nationwide. The BTS data was a complete
collection from participating airlines, which consist of airlines with 1 percent or more of scheduled
domestic passenger revenues. A Chi–Squared Goodness–of–Fit test was selected for use.
The Chi–Squared Goodness–of–Fit test offered a method to compare categorical data such as on–
time performance or not on–time performance. The two tailed hypothesis test is Ho: Southwest at
Oklahoma City airport has the same on–time performance as the national rate. Ha: Southwest at
Oklahoma City airport does not have the same on–time performance as the national rate. As the data
was for 2015 this allowed for 12 months of data enabling a df =11 with a desired significance level
of 95% α = .05, power of β = .80, and medium effect size of 0.30 resulting in a desired sample size
of 187 which was exceeded as the data includes 17,786 operations.
Results
The BTS reported a total of 5,819,079 operations nationally with an on–time rate of 79.92% and
Oklahoma had 17,786 with an on–time rate of 77.29%. The national on–time rate was applied to the
total operations by southwest as shown in Table 1 and resulted in the expected delays in the
expected column. Actual delay rates from BTS were displayed in the observed

Clothing Impact on Social Interactions Essay
Does Clothing Have an Impact on Social Interactions:
An Observational Study in the Classroom
There are many reasons why we choose to wear a particular article or style of clothing. Many of us
consider our choice in clothing as an extension of our identity. While many others pick items from
their wardrobe that reflect their current mood. There are also many times when we choose to dress a
certain way in anticipation of being in a particular social setting. Even people who don't seem to
bother with matching clothes or wearing a designer label or walk around wearing clothes that are
torn and dirty, are making a statement. What remains to be examined is whether or not there is a
clear relationship between the ... Show more content on Helpwriting.net ...
As dress is important in the job setting, social setting, and even consumer setting, the participation
of students in class as well as their relationship with their professors has a strong impact on their
success in college. It for this reason that the following study is worth examining and has much
relevance to the human world. Its possible implications may also give helpful strategies for current
and prospective classroom students to better succeed in their studies.
Method
Participants and Study Design
Undergraduate students in the general education requirement class for Literature participated in this
study. The class was selected as it was currently being taken by each of the researchers and was felt
to be a class that heavily relied on class participation. The study was conducted during a regular
class meeting and consisted of 92 students assigned to one of two groups (n = 1) for those dressed
up and those dressed down (n = 2). Dressed up was operationally defined as a student wearing dress
pants, khakis, skirts, suits, dress shirts, sweaters and no sneakers. Dressed down was operationally
defined as jeans, sweats, t–shirts, etc... For this study there were 35 students grouped as dressed up
and 57 as dressed down.
Each participant was also categorized as male (n = 1) or female (n = 2). This study had 35 males and
57 females. Of the

Determining The Function Of The Ordinal Scale
Before conducting a statistical analysis, every researcher needs to measure the depended variable.
The measurement depends on the type of variable. Different types are measured differently. So, the
researcher has to decide on the appropriate scale of measurement. There are typically four levels of
measurement: nominal, ordinal, interval, and ratio.
1. The purpose of the nominal scale is for identification and represents the lowest level of
measurement. With a nominal scale, the researcher only names or categorizes responses (i.e.
male/female, high/low, unemployed, employed, retired, student). Thus, nominal scale measures
variables that are non–numeric or where the numbers have no value.
2. The purpose of the ordinal scale is to allow comparisons of the degree to which two subjects
possess the dependent variable. The ordinal scale provides a rank order (i.e. very dissatisfied,
somewhat dissatisfied, somewhat satisfied, or very satisfied). However, it is the order of the values
that is important, not the differences between them. Ordinal scales usually measures non–numeric
variables like satisfaction, happiness, discomfort, etc
3. The Interval scale provides information about both the rank order and the exact differences
between the values (i.e. pain scale). However, interval scales do not have a true zero point.
4. The Ratio scale (i.e. height and weight) is the most informative scale because it tells the
researcher about order, the exact value between units, and has a

Relationship Measures Of Attachment Security And Parental...
The current study investigated the relationship measures of attachment security, parental sensitivity,
emotional availability, and child attachment classifications in high–risk parent–child dyads. The first
hypothesis was only partially supported, all home– and lab–based measures were positively related
but were not all significant. Consistent with past research, home–based measures of attachment
security and parental sensitivity were strongly positively related, which adds to the literature
supporting the validity of the AQS and MBQS–mini (Tarabulsy, et al., 2009). Moreover, these
findings were anticipated as the two measures are complementary and can be assessed using the
same parent–child observations. Parental sensitivity was related to all EA dimensions. In contrast,
attachment security was not related to any dimensions of EA, except the dimension of adult
structuring. The latter findings failed to replicate results in past research which linked measures of
attachment security to dimensions of EA (Biringen et al., 2014). Childhood attachment
classifications obtained through the Strange Situation Procedure (SSP) were also examined in this
study. Disorganized attachment has shown to be the most detrimental on children. Given the high
risk sample, the current study had an even split of secure and disorganized children. These results
have been replicated in the past by Easterbrooks, Biesecker, and Lyons–Ruth (2000) with low–
income high psychosocial risk families. The

Statistics for Bi
Statistics for Business Intelligence – Hypothesis Testing
Index:
1. What is Hypothesis testing in Business Intelligence terms? 2. Define – "Statistical Hypothesis
Testing" – "Inferences in Business" – and "Predictive Analysis" 3. Importance of Hypothesis Testing
in Business with Examples 4. Statistical Methods to perform Hypothesis Testing in Business
Intelligence 5. Identify Statistical variables required to compute Hypothesis testing. a. Correlate
computing those variables from the data available in normalized tables arranged in row x columns.
6. Computing Statistical Hypothesis Testing for Business Decisions using Algorithms 7. User
Interface Development for Presentation of Hypothesis feature ... Show more content on
Helpwriting.net ...
Predictive analysis considers previous data and predicts future data points, but predictive analysis
fails to consider new variables to practice and judge results. In this case, hypothesis helps to analyze
old data to set benchmark and set goal to achieve the new variable modified.
Hypothesis helps in making decision by comparing benchmark values and present results with the
variables applied. If Hypothesis is used to predict the future results this will be more about decision
to conclude whether to implement/accept or reject. Again, Hypothesis also use all statistical modals
of data, outliers removal, mean of sample, mean of population and proportions.
4. Statistical methods to perform Hypothesis Testing in Business Intelligence –
Steps in Hypothesis Testing –
Step 1: Take a decision item of proposed or implemented
Step 2: Formulate testable hypothesis (In case of implemented decision hypothesis will be to judge
whether the modified variable has shown positive results or not)
Step 3: Design the experiment
Step 4: Set up a Decision Rule
Step 5: Collect Data
Step 6: Make a Decision
Step 7: Revise your decisions towards success or profits.
Hypothesis Formulation:

We formulate a pair of mutually exclusive, collectively exhaustive statements about the business
scenario.
One statement or other must be true, but they cannot both be true.
Ho = Null Hypothesis
H1 = Alternative Hypothesis
The two statements are hypothesis

Cohen’S Paper The Earth Is Round (P>0.05) Is A Critique
Cohen's paper The Earth is Round (p>0.05) is a critique of null–hypothesis significance testing
(NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests
ways in which researchers can improve their research, as well as the way they report their research.
Cohen's main point is that researchers who use NHST often misinterpret the meaning of p–values
and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to
worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A
Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is
where the researcher incorrectly accepts the false null ... Show more content on Helpwriting.net ...
Those focused on effect sizes will have positive bias in their estimations. Therefore, Cohen suggests
that we call it the nil hypothesis. Nil means zero, and the hypothesis should be called nil instead of
null because we are always testing against zero.
Cohen states that the p–value is only able to tell us whether there is a difference between A and B. If
the null hypothesis is rejected, then A is more than B, or vice versa. Unfortunately, Cohen believes
that the only thing researchers are learning is whether one is larger than the other, which is not
enough information. To conclude Cohen's discussion on the statistics used in psychological research,
he discusses how correlation is not causation. In terms of how to avoid continuing to make this
error, Cohen does not believe that there is a way to replace NHST. First, we are not supposed to look
for an easy alternative to NHST because it does not exist. Two possible solutions are to understand
and improve the data that we collect and to report the effect sizes in the form of confidence intervals
(Cohen, 1994).
Cohen's article is a reminder to be careful when reviewing research. This article shows how not all
research is true and people put their faith into what they want the research to mean. It is damaging to
the progression of psychology as a science if researchers do not claim something as significant for
being true. We do this by claiming something is significant because that is what NHST says, and

Essay about July at the Multiplex
DATE: June 12, 2012 TO: Mr. Plex, Owner, Royal 16 Theater FROM: Team 8 RE: Analysis of
Liability for Fraud Based off of your request, we have completed an analysis concerning Royal 16
Theater's liability for fraud assumed by the customer, Tommy. Please contact us if any additional
information is needed. July at the Multiplex Executive Summary What are the standards of selling a
service or product so the customer will not get furious? If we tried out best but they are still
unsatisfied, what kind of response should we give? In this case "July at the Multiplex", the plaintiff,
Tommy, was not satisfied with the service that was provided with Royal 16 Theater. He demanded
the money that he paid be returned. The theater owner, Mr. Plex ... Show more content on
Helpwriting.net ...
The duty to mitigate damages is the responsibility of the plaintiff because the plaintiff injured by the
breach of the contract cannot recover for losses that could have been easily avoided. In this case
Tommy fulfilled his duty to mitigate damages and was not trying to recover for any damages that he
could have easily avoided. Instead Tommy wants to file a lawsuit based on misrepresentation. A
misrepresentation is a statement made that is not unified with the truth. There are two categories that
misrepresentation can fall under: either innocent or fraudulent. When a misrepresentation is innocent
it was made not intentionally to deceive the other party. A fraudulent misrepresentation is made with
the intent to deceive with knowledge that it is false. Tommy wants to pursue legal action against
Royal 16 Theater on the basis that they conducted fraudulent misrepresentation. Prima Facie Case
The prima facie case that relates to this case between Tommy and Royal 16 Theater is Cao and Cao
v. Nguyen and Pham. In Cao and Cao v. Nguyen and Pham the plaintiffs file a lawsuit against the
defendant for fraudulent misrepresentation. They said that when they were attempting to buy a
property the defendants claimed that the property was in fact a duplex and that multiple families
could live there. The city building and safety department revealed that the property was not a duplex
and could

Posttraumatic Stress Traumatic Stress Disorder
Posttraumatic Stress Diagnostic Sacle: A Review Within the human experience, there has always
been a risk of exposure to both physical and psychological trauma. The threat of violence, personal
tragedies, and economic and social catastrophes are all possible triggers for an onset of Post–
Traumatic Stress Disorder (PTSD). What was once reserved as a diagnosis for veterans has been
found to affect both young and old. The development of various assessment tools have been used in
diagnosing PTSD and one of the most commonly used tests is the Post–Traumatic Stress Diagnostic
Scale. It is an assessment that was developed and validated by Edna Foa to provide a brief but
reliable self–report measure of PTSD for use in both clinical and research settings (Zalta, Gillihan,
Fisher, Mintz, McLean, Yehuda, & Foa, 2014). The Post–Traumatic Diagnostic Scale is an
assessment instrument deigned to be taken by adults with at least an eighth grade reading level. This
review of will evaluate the hits, misses, false positive errors, and false negative errors of this
instrument and how to each area is applied in interpreting the construct measurement of this
assessment tool. Although the Post Traumatic diagnostic Scale has consistently ranked high with
clinicians and researchers, in self–reporting the definition of trauma can potentially become
subjective and compromise the reliability of the assessment tool. The Assessment Instrument: Post
traumatic Diagnostic Scale The Post Traumatic

Hcs/438 Dq's Essay
HCS/438 DQ's
Week 1:
DQ1: What are the differences between descriptive and inferential statistics?
According to Bennett (2009), the biggest difference between descriptive and inferential statistics is
that descriptive statistics "deals with describing raw data in the form of graphics and sample of
statistics" and inferential statistics "deals with estimating population parameters from sample data."
This means that inferential statistics would be an estimate because the data would be estimated from
sample data rather than using specific data whereas descriptive statistics would be more accurate.
An example of descriptive statistics would be trying to find an average of something such as a
G.P.A. or your overall grade in a class. ... Show more content on Helpwriting.net ...
We come up with the number 7. 7 would be the mean in this case.
"The median is the middle value of the data set. To find a median we arrange the values in ascending
(or descending) order, repeating data values that appear more than once. If the number of values is
odd, there is exactly one value in the middle of the list, and this value is the median. If the number
of values is even, there are two values in the middle of the list, and the median is the number that
lies halfway between them. For an example the list 3, 4, 6, 6, 10. The median number is 6 because 6
is the middle number in the list." (Bennett, Briggs, & Triola, 2009, p. 146).
"The mode is the most common value or group of values in a data set. For an example the mode in
the number set 3, 4, 6, 6, 10 is 6 because this value occurs twice in the data set."(Bennett, Briggs,
& Triola, 2009, p. 146).
We would use mean, median and mode in healthcare to find the average of many things such as how
effective a new medication would be on the average population. Or how far into a new treatment
patients start seeing results or improvements. Bennett, J.O., Briggs, W.L., & Triola, M.F.
(2009). Statistical Reasoning for Everyday Life (3rd ed.). Boston, MA: Pearson Education Inc
DQ2: Measures of variability are range, Interquartile range, variance, and standard deviation. Range
is the difference between the highest and the lowest

Exercise 40 Chi Squared Essay
Researchers routinely choose an ◊–level of 0.05 for testing their hypotheses. What are some
experiments for which you might want a lower ◊–level (e.g., 0.01)? What are some situations in
which you might accept a higher level (e.g., 0.1)?
An alpha level of 0.05 is arbitrary and was set as a standard by scientists. One of the key concepts in
hypothesis testing is that of significance level or, the alpha level, which specifies the probability
level for the evidence to be an unreasonable estimate. Unreasonable means that the estimate should
not have taken its particular value unless some non–chance factor(s) had operated to alter the nature
of the sample such that it was no longer representative of the population of interest. (Price, 2000) ...
Show more content on Helpwriting.net ...
(2000). What Alpha Level? In I. Price, Inferential Statistics (p. Chapter 5). New England: University
of New England.
University of Texas–Houston Health Science Center . (2013). Hypothesis Testing . Retrieved March
21, 2013, from Biostatistics for the Clinician :
http://www.uth.tmc.edu/uth_orgs/educ_dev/oser/L2_2.HTM
4DQ1
How would you explain the analysis of variance, assuming that your audience has not had a
statistics class before?
When examining the differences between two or more groups, you can use the analysis of variance
which is known as ANOVA. This is a statistical technique that is used to compare the means or
averages of more than two groups. There are three uses of ANOVA which are the one–way, the two–
way and N–way Multivariate ANOVA. (Solutions, 2013) The determining factor when to use one of
the "ways" is dependent upon how many "treatments" are used in the study. We use the term
treatment because ANOVA originated in the 1920's to test different treatments of fertilizers' crop
yields. ("Analysis of Variance," 2012, p. 2) Here, we will cover the one–way and the two–way
ANOVA.
The one–way between groups, ANOVA is used when you want to test the difference between two or
more groups. This is the simplest version of ANOVA. (Crossman, 2013) This could be used for
example in a study on the ages of patients on different cardiac medications. Here we are only
looking at

Qmet 252
A manufacturer wants to increase the shelf life of a line of cake mixes. Past records indicate that the
average shelf life of the mix is 216 days. After a revised mix has been developed, a sample of nine
boxes of cake mix gave these shelf lives (in days): 215, 217, 218, 219, 216, 217, 217, 218 and 218.
At the 0.025 level, has the shelf life of the cake mix increased?
Choose one answer.
|[pic]|a. No, because computed t lies in the region of acceptance. [pic] | |
|[pic]|b. Yes, because computed t is less than the critical value. [pic] | |
|[pic]|c. Yes, because computed t is greater than the critical value. [pic] | |
|[pic]|d. No, ... Show more content on Helpwriting.net ...
Question 5
Marks: 1
A machine is set to fill the small size packages of M&M candies with 56 candies per bag. A sample
revealed: 3 bags of 56, 2 bags of 57, 1 bag of 55, and 2 bags of 58. How many degrees of freedom
are there?
Choose one answer.
|[pic]|a. 7 [pic]| |
|[pic]|b. 9 [pic]| |
|[pic]|c. 6 [pic]| |
|[pic]|d. 8 [pic]| |
|[pic]|e. 1 [pic]| |
Total number of bags = 3+2+1+2=8
df = 8–1=7
Incorrect
Marks for this submission: 0/1.
Question 6
Marks: 1
What value does the null hypothesis make a claim about?
Choose one answer.
|[pic]|a. Population parameter [pic] | |
|[pic]|b. Sample mean [pic] | |
|[pic]|c. Sample statistic [pic] | |
|[pic]|d. Type II error [pic] | |

Correct
Question 7
Marks: 1
If α = 0.05, what is the probability of making a Type I error?
Choose one answer.
|[pic]|a. 20/20 [pic] | |
|[pic]|b. 19/20 [pic] | |
|[pic]|c. 1/20 [pic] | |
|[pic]|d. 0 [pic] | |
Correct
Question 8
Marks: 1
What is the level of significance?
Choose one answer.
|[pic]|a. Beta error [pic] | |
|[pic]|b. Probability of a Type II error [pic] | |
|[pic]|c. Probability of a Type I error [pic] | |
|[pic]|d. z–value of 1.96 [pic] | |
Correct
Marks for this

Why Statistics Are Part Of Everyday Life
Statistics are part of everyday life. Whether we know it or not we use them in some way, shape or
form during the course of our day. Business and medical professionals, sociologists and scientists
alike all utilize statistics in order to determine probabilities of outcomes. The purpose of this paper is
to discuss what I have learned about statistics in during this class covered during the last five weeks.
The topics I will cover inform the class are descriptive statistics, inferential statistics, hypothesis
development and testing, selection of appropriate statistical tests and evaluating statistical results.
The intent is to discuss the application of the elements in analyzing and making decisions about
data. Our book states that there are many kinds of descriptive statistics. Calculating measures of
central tendency and measures of variability are two we focused on. As their names suggest,
measures of central tendency indicate what is most typical in a data set. Measures of variability
gauge how much difference there is in a set of measures (Tanner &Youseef–Morgan, 2013).
Descriptive statistics aid in sorting through large amounts of data and making them understandable.
In order to measure central tendency we utilize the mean, median, and mode. These are used to find
the most frequent value (mode), the middle most value (median), and the average (mean). These
would be useful if you wanted to use age as a data point in a marketing campaign to target the age

How Statistics Is Important For The Quality Of Data
Statistics is well–defined as a division of mathematics and is most often used to explain, analyze,
and summarize as well as interpret what we perceive– in order to make sense or meaning of our
interpretations. Every day we encounter statistics that originate from various forms and behaviors.
Which means that to in order for all of the information gathered to make sense, there would
realistically be no need for the use of statistics. Conversely, due to its focus and pragmatic on
applications, statistics is characteristically considered a distinctive mathematical science rather than
a division of mathematics (Chance et al, 2005). Therefore, some measures a statistician utilizes are
less mathematical; for example, ensuring data collection is conceded in a manner that yields valid
conclusions, recording results, or coding data in behaviors understandable to the users. Statistics has
been recognized to improve the quality of data by forming specific survey samples and experiment
strategies. It also offers tools used to forecast the use of data as well as statistical models. It is
appropriate in many academic arenas that consist of business, social and natural sciences, and
government, to list a few.
Descriptive statistics are wholly used to define the sample under study; they are used essentially to
describe the fundamental characteristics of a prearranged data. Descriptive statistics offer modest
summaries concerning the measures as well as the samples. When applied in

Compulsory Assessments
The four accepted articles were analyzed and summarized. Article validity, reliability, and bias are
included in the discussion. An overview of the articles are included in Table 2, indicating evidence
level, design, population characteristics, intervention specifics, outcome measures, and PICO
question relevance.
Article One: Jung–Hyun, C. & Nyeon–Jun, K. (2015). The Effects of Balance Training and Ankle
Training on the Gait of Elderly People who have Fallen. Journal of Physical Therapy Science, 27(1),
139–142. Selection of the randomized controlled trial (RCT) by Jung–Hyun and Nyeon–Jun (2015)
was based on inclusion criteria, objective of the study, and outcome measurements. Jung–Hyun and
Nyeon–Jun compared the effects of balance exercises ... Show more content on Helpwriting.net ...
(2009) recruited 28 women from three nursing homes who met inclusion criteria, one woman
dropped out prior to randomization. The 27 women were randomly assigned to two groups. In the
first session, 14 women received a 10–minute friction plantar massage and 10 minutes of
mobilization to feet and ankle joints, while 13 women received a 20–minute placebo intervention
(Vaillant et al., 2009). In the second session, the treatments were reversed, with at least 1 week
between sessions (Vaillant et al., 2010). Statistical significance was established by p ≤ 0.05 and CI
of 95%, which decreased the risk of a Type I error and improved precision, respectively (Jewell,
2015). Between–group comparison analyses indicated a single session of massage and mobilization
resulted in significant improvements in OLB and TUG tests, but not in the LR test (Vaillant et al.,
2009). Vaillant et al. concluded that balance responses in elderly women could improve after a
single session of foot and ankle manual

Type 2 Error In Nursing
This discrepancy may increase the risk of type I or type II errors that may have an impact on public
health as well as may cause economic loss due to the estimated 24 hour delay in reporting results. A
type I error is the incorrect rejection of a true null hypothesis or in other words, it leads one to
believe that a supposed effect or relationship exists when, in fact, it doesn't (Banerjee et al., 2009).
An example of this in terms of water quality monitoring is prohibiting swimming unnecessarily or
reporting a false positive result which may lead to economic loss at high bather density beaches. A
type II error is the failure to reject a false null hypothesis or in other words, it leads one to believe
that a supposed effect or relationship does not exist when, in fact, it does (Banerjee et al., 2009). An
example of this in terms of water quality monitoring is allowing recreational swimming during
elevated risk, or reporting a false positive result which may lead to an increased risk of recreational
water–associated illnesses amongst bathers.
Amendments to the Federal Water Pollution Control Act (Clean Water Act)
The USEPA's water quality criteria of 1986 have been increasingly ... Show more content on
Helpwriting.net ...
The USEPA was re–authorized by the Safe Water Improvement and Modernization Act (H. R. 909;
2007) to direct BEACH Act appropriations to States and local governments for coastal recreational
water quality monitoring and notification programs (Safe Water Improvement and Modernization
Act, 2007). This Act also recommended that researchers study the benefits of using a molecular
diagnostic technique for monitoring and assessing the quality of coastal recreational water.
Specifically, to study the full range of available rapid indicator technologies and to compare the use
of molecular techniques to culture–based methods. Grant awards or cooperative agreements for
organizations were also authorized to assist in carrying out these

Statistics Can Reduce Internet Fraud Essay
Many online auction sites help consumers bid on items they might want to possess. These sites
include eBay, WebStore, ebid, OnlineAuction, OZtion, Overstock, and a whole slew of others.
Currently there is no system to protect consumers from fraudulent sales. When someone bids for an
item, consumers trust both the seller and the online auctioneer to be both honest and ethical when
presenting items for bid. Many of the auction houses, including Sotheby's and Christie's have their
experts examine each piece to prove authenticity. However, it is not all bad news. To help protect
consumers from fraud, Joseph Gastwirth and Wesley Johnson have found that the use of statistics
answers the problem of authenticity. Joseph ... Show more content on Helpwriting.net ...
They gathered all the alleged Henry Moore
item auctioned on eBay during the period of March 2005 through November 2007. It totaled 239
items. After they completed the gathering of the data, they required the help of two independent
evaluators. "The first was Stephen Gabriel, an expert on Moore, and the second was one of the
authors (JLG)." (Gastwirth & Johnson, 2011) They had a third person, Dr. H. Hikawa download
each item. They used digital photos of each of the items. "The files were e–mailed to the first
evaluator, while the second evaluator was given a printed version." (Gastwirth & Johnson, 2011)
Neither assessor was able to talk to the other about any of the art for sale during the study period in
order to ensure the independence of the evaluations. "The results of the study were summarized in
two 2 x 2 tables reporting the matched pairs classification for the two groups of artwork." (Gastwirth
& Johnson, 2011) Figure 1 is a copy of that table.
Prints Evaluator 2 Questionable Genuine Total
Evaluator 1 Questionable 6 10 16 Genuine 1 149 150 Total 7 159 166
Sculptures and Drawings Evaluator 2 Questionable Genuine Total
Evaluator 1 Questionable 59 6 65 Genuine 2 6 8 Total 61 12 73
The second part is their specificity, which is in some ways the reverse – that they know a genuine
article when they see one. These are far from being the same thing. An evaluator

Psych 625 Time to Practice – Week Three Essay
University of Phoenix Material
Time to Practice – Week Three
Complete both Part A and Part B below.
Part A
1. For the following research questions, create one null hypothesis, one directional research
hypothesis, and one non–directional research hypothesis.
a. What are the effects of attention on out–of–seat classroom behavior?
Null– There are no effects of attention on out of seat classroom behavior.
Non–directional– Attention affects out–of–seat classroom behavior.
Directional– No attention negatively instigates out–of–seat classroom behavior.
b. What is the relationship between the quality of a marriage and the quality of the spouses'
relationships with their siblings?
Null– There is no relationship ... Show more content on Helpwriting.net ...
The goal of inferential statistics is to end up rejecting the null hypothesis and concluding that a
significant relationship exists; therefore, the null hypothesis always presume no relationship.
4. Create a research hypothesis tested using a one–tailed test and a research hypothesis tested using
a two–tailed test.
One–tailed test hypothesis: The mean lifespan of a dog is greater than 100 years.
Two–tailed test hypothesis: The mean salary of behavioral therapists is not $75,000.
5. What does the critical value represent?

The critical value represents the point on the scale of test statistic value in which the null hypothesis
is rejected (Salkind, 2014). The critical value is also used to calculate the margin of error (Salkind,
2014). Lastly, the critical value is determined from the alpha or significance value of the hypothesis
test (Salkind, 2014).
6. Given the following information, would your decision be to reject or fail to reject the null
hypothesis? Setting the level of significance at .05 for decision making, provide an explanation for
your conclusion.
a. The null hypothesis that there is no relationship between the type of music a person listens to and
his crime rate (p < .05).
The null hypothesis is rejected since the p–value is below the significance level of 0.05.
b. The null hypothesis that there is no relationship between the amount of coffee consumption and
GPA (p = .62).
The null hypothesis is accepted because the

Statistical Assumptions Of Substantive Analyses Across The...
Nimon, K. (2012). Statistical Assumptions of Substantive Analyses Across the General Linear
Model: A Mini–Review. Frontiers in Psychology Front. Psychology, 3(322).
When using inferential statistics depends on the sampling technique used as well as the
characteristics of the population data. This dependency does assume that the sample and population
meet certain criteria. The criteria is called statistical assumptions, and if there are violations of these
statistical assumptions that are not addressed, than the data may not be interpreted correctly. In
particular, Type I or Type II errors may be increased or decreased incorrectly. Nimon's (2012) article
focused on assumptions associated with substantive statistical analyses across the ... Show more
content on Helpwriting.net ...
Using unreliable data may cause underestimation which increase the probability of Type II errors. If
there is correlated error, we may see unreliable data to be overestimated which would increase the
risk of Type I errors. To satisfy the assumption of error–free data, researchers may conduct and
report analyses using latent variables as opposed to observed variables. This technique is called
structural equation modeling (SEM). This is where latent variables are formed from item scores, the
former of which becomes the unit analyzed. It is important to note that SEM is a large sample
technique. A researcher can also delete a few items to raise the reliability of an observed score, but
this should be reported along with the accompanied estimated of the reliability with and without the
deleted items. Some researchers consider measurement to be an assumption while others do not.
Nimon (2012) does state because measurement levels play such a pivotal role in statistical analyses
decision trees, it would not be recommended to lower the measurement level of data unless certain
characteristics are met. This may discard important information and may produce misleading or
erroneous information. Yet another assumption, is the assumption that inferential statistics in
psychological and educational research produce population data that is normally distributed. This
does depend on the analysis conducted, such as univariate, multivariate,

Hrm 531 Week 4 Paper
The LV team is eager to learn what statistical significance is and why it's an important construct in
the study and use of inferential statistics.
Good Morning LV Team,
The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and
Z–score. Today's session is a little bit different, we will be talking about statistical significance.
Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis
while it is true and it separate random error from systematic error. When doing a study or research,
the statistical significance shows that the difference obtained were not caused by chance. Inferential
statistics, the T–test, partition noise from bias by studying a random sample than the population in
which we are interested and from the results we infer. The advantage of using sample than a
population, it is convenient, saves time, energy and money because n is smaller than population and
above all it helps to control systematic and random errors. When we are making a conclusion, we
should have a certain confidence or probability of being right and that is called the alpha level;
which the risk you are willing to ... Show more content on Helpwriting.net ...
There is a significance, but is it practical or meaningful? It depends on the context and circumstance,
therefore, 0.01 is not practical compared to the benefit I gain from buying the California Olive
Ranch brand which is lower in price with better quality (previous study). However, if studies had
shown that the quality was inferior, then 0.01 would be practical significance. Knowing whether the
probability is significance or practical helps us in our decision. If I want to increase the cost of X by
1cent and by competitors have a lower price with same quality, then it is not practical to increase the
cost by a penny and lose my customer. Do you have any question before we end our

Essay on Psych 610 week 7 homewrok exercise
Week Seven Homework Exercise Answer the following questions, covering material from Ch. 13 of
Methods in Behavioral Research Define inferential statistics and how researchers use inferential
statistics to draw conclusions from sample data. According to Cozby (2009) inferential statistics are
used to determine whether we can in fact make statements that the results reflect what would happen
if we were to conduct the experiment again and again with multiple samples Define probability and
discuss how it relates to the concept of statistical significance. Probability is the possible that an
outcome of an experience or an event will occur (Cozby 2009) Statistical significant and probability
are one in the same. A researcher is studying the ... Show more content on Helpwriting.net ...
One application of a Chi–square test is a test for independence. In this case, the null hypothesis is
that the occurrence of the outcomes for the two groups is equal. A Fishers exact test is used when
you have a small sample Reference Cozby, P. C. (2009). Methods in Behavioral Research (10th ed.).
Retrieved from The University of Phoenix eBook Collection database. Week Seven Homework
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