Cancer Practitioner Personal Statement

Cancer Practitioner Personal Statement
For many years now, Scientists have struggled to help medical practitioners treat their patients
according to their symptoms and provided customized healthcare on a personal basis. However, how
personal can medicine get? In 2003, researchers obtained a complete human genome from which the
sequence and map of all genes in the human body can be used as a reference. With this development
we are a step closer to treating cancer and other diseases.
Most cancer treatments engage in trial and error basis of treatment which puts so much of pain, and
risk on the patient. I worked on a similar problem in my role as a research assistant in a project
funded by the NIH XYZ Clinical Translational Research Center. It was on the use of metabonomics
for the early detection of ovarian cancer. We worked on the development of various multi–class
modelling techniques like support vector machines, random forests, k nearest neighbours and partial
least squares using RNA–seq data from PQR that helped estimate accurate sensitivity, specificity,
and positive predictive values of the tumour and develop predictive models for cancer.
I have worked on DNA methylation, RNA expression, clinical patient data based on the Pancreatic
Adenocarcinoma dataset through the cancer genome atlas (TCGA). The study ... Show more content
on Helpwriting.net ...
I worked on a dataset that analyzed the malignant and benign prostate tissues that provided insight
into a gene expression signature for prostate cancer. The goal was to conduct several prediction
algorithms including principal component regression, elastic nets, partial least squares on the
samples containing gene expression profiling by array to accurately predict prostate cancer. It was
based on variables like factors that included disease state, and genotype/phenotype
... Get more on HelpWriting.net ...

Hippocampal Shape Analysis
Many studies focus on the statistical analysis of volume measurements of the hippocampus as for
example for the study of Alzheimer's disease [6]. But such studies do not capture the shape
complexity of the structures observed in MRI. Analyzing shapes of structures can provide a better
understanding of the anatomical variability of the brain, which is a very challenging problem since
healthy brains have a high variability, and it is important to understand the mechanisms, the
morphological changes and the impacts of diseases.
Predicting or classifying dementia based on hippocampal shape analysis is an active field of
research. In [7], Statistical Shape Models (SSMs) have been used to model the variability in the
hippocampal shapes among the population. In [8], they characterize shape abnormalities of
subcortical and ventricular structures as well as the subregions of the hippocampus and the
amygdala in subjects with MCI or AD within framework of Large Deformation Diffeomorphic
Metric (LDDMM). In [9] they propose a template–based analysis of anatomical variability in
populations, also in the framework of LDMM. ... Show more content on Helpwriting.net ...
This method solves the correspondence problem by the alignment of the spherical parametrization
using a first order ellipsoid [10]. In [11], they propose a method to automatically discriminate
between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly
controls, based on multidimensional classification of hippocampal shape features. This approach
uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi. However,
as this method establishes correspondence on simplified spherical models of surfaces, it is restricted
to surfaces with spherical topology and is computationally

Analysis of Variance: Anova
Analysis of variance (ANOVA): How and when it is used in research In statistics, variance refers to
the comparison of the means of more than two groups. The term "variance may mislead some
students to think the technique is used to compare group variances. In fact, analysis of variance uses
variance to cast inference on group means...Whether an observed difference between groups mean is
'surprising' will depends on the spread (variance) of the observations within groups. Widely different
averages can more likely arise by chance if individual observations within groups vary greatly"
(Analysis of variance. 2012, Stat Primer). Variances indicate the presence of change or the existence
of a statistically significant difference between two groups being compared. For every statistical
experiment, an alternative hypothesis and a null hypothesis is constructed. If "the variance between
groups exceeds what is expected in terms of the variance" the null has been disproven and the
independent variable is shown to have had a measurable influence upon the experimental population
(Analysis of variance. 2012, Stat Primer). In statistical experimentation, the hypothesis (which the
experimenter actually thinks or hopes will occur) is never technically proven; rather the null
hypothesis is refuted. The actual hypothesis of the experimenter is 'guilty until proven innocent'
(What is a null hypothesis, 2012, Null Hypothesis). A good example of the use of a null hypothesis
might be found

Are Termites Prefer Circular Paths Over The Ones With...
Author: Wiktor Kostecki; Group members: Ashley Milner, Upama Ferdousi, Jinyu Lan Termites'
Path Preference; an investigation of inclination towards shapes and angles. Based on the
observations and experiments conducted by all of the groups, the presented hypothesis: "Termites
prefer circular paths over the ones with sharp angles", is a logical extension of inductive reasoning,
which needed to be tested by a carefully planned and executed experiment. Following a long and
interesting debate, the students and lab instructor have reached consensus and decided to test the
hypothesis stated above. As the plausibility of the claim that termites are being drawn to
shapes/paths marked with a pencil was widely disproved by groups which used it in their
experiments, black ink pen with which those organisms seemed to be engaged with, was almost
universally agreed upon as the tool to use in the experiment for the path drawing. What is more,
some students suggested to test whether termites are attracted by the smell of the ink, but since no
data were produced in previous experiments, it would be considered an experimental gamble to
examine it, owing to the fact that the shapes were more extensively studied by lab participants.
Hence, developing an experiment that would rely solely on the sense of smell was disapproved after
an exchange of ideas between the instructor and the class, as forming and testing hypotheses based
on navigation solely by olfactory sense would not be dictated by a

3D Image Segmentation Paper
At first, the general objective of image segmentation in this project is to aid tumour detection by
delineating it from normal brain structures. Segmentation aids to detect, to diagnose, to classify the
type and to find the stage; thereby helping the treatment decision. In addition, it also helps to
monitor the treatment either chemotherapy or surgery.
Secondly, the human detection and diagnosis is an experience and knowledge drive process. This
often leads to variability among experts, especially detecting subtle changes on longitudinal studies.
A computational technique, either semi or fully automatic segmentation will aid at various levels of
the decision making process. Notably, segmentation faces challenges to match the human perception
and ... Show more content on Helpwriting.net ...
At first, the general outline would be to implement a supervised approach. This requires the training
dataset that include a cohort of MRI images of normal and Brain tumour. Both will be labelled,
trained and tested. We begin with 2D and extended to 3D.
The outline of the stages of the image processing pipeline is; realign (affine), skull scripting,
statistical edge detector (multi–scale, multi–modal), extract statistical features, use it to build SVM.
The robustness of implementation will be tested with Pixel Correspondence Metric (PCM). The
same pipeline will be followed for an unsupervised approach using leave one out (LOOCV). The
filter size, parametric and non–parametric features like mean, variance, rank, U Mann Whitney test
etc will be evaluated and an optimal performer will be selected.
The training data set will be acquired from Birmingham Children Hospital. In addition, a multi
centre collaboration with Children Oncology Group in North America (COG) and Paediatric Brain
Tumour Consortium (PBCT) can provide variety, as the paediatric brain tumour has a wide range of
histology Louis et

Identity Recognition Ear Recognition
Ear Recognition
Ear images can be acquired in a similar manner to face images, and a number of researchers have
suggested that the human ear is unique enough to each individual to allow practical use as a
biometric. The Alfred system presented by the researchers (Zhang. K., et. al., 2006) suggests that for
identity recognition– ear shape can be used as a unique and comparable feature. The objective of
this system was to extract ear shape features for recognition. Active shape model is used to model
the shape and local gray texture of the ear and the steerable features are used to extract from the ear
image ahead of active shape models to provide accurate guidance for shape location. Final
classification is been proved to be successful in gait recognition is proved by Eigenshape. Ear
database experiments shows an encouraging performance.
In the Alfred system, the difficulty application is the location of the center point in the system.
Alfred has created a 12 measurement system known as Alfred system as shown below, ... Show
more content on Helpwriting.net ...
For identity recognition, the system suggests that ear shape can be used as a unique comparable
feature. Active shape model are used to effectively model both the local appearance and shape of ear
in a statistical manner. Active shape model is a powerful statistical tool for shape, but it suffer from
illumination changes and local minima in optimization and the resultant is dependent on
initialization. The best candidate point is included in the profile perpendicular to the landmark
contour and the target point must be long enough. To find the target, the more points the profile
includes, the

Essay On Dynamic Membranes
3. Inclusion/Exclusion criteria: Cells' data bank will be screened to determine the dynamic
membrane properties for each cell group and to ensure they meting all inclusion and exclusion
criteria. Cells will be excluded from the study if they fall under one of the following: (1) Unhealthy
neural cells (2) neural cells that have different diameter or geometric shape (3) cells that don't
contain all the required parameters values, (4) Cancer cells (5) Uncomplete data set. 4. Sample size
calculation: To determine the sample size needed for all groups, an a priori power analysis,
conducted using G*Power 3.0.10 software, calculated the required sample size n = 30 per group
bases on an ANOVA test that has 80% power to detect a significant ... Show more content on
Helpwriting.net ...
Alternatively, it is possible that the statistical test find significant difference between trials, which is
an indication of the in consistency of the algorithm. In this case we would revise out algorithm and
experimental design, we also may increase the number of trials to re–examine the consistency of the
algorithm. Also the statistical test may reveal no significant difference between groups, in this case
we will re construct the groups based on manipulation of three parameters, and we will revisit the
range of variation for each parameter. 2. Exp2: We predict a significant difference between groups,
waveforms and the interaction between both of them, and the post hoc test will reveal that groups
with variation in Gnamax, Gleak and α will be more selective than groups with them constant. This
is an indication that Gnamax, Gleak and α can be used to improve stimulation selectivity. However,
the test may show no significance between groups, in this case we will revise the parameters
combinations. Also, We may run the statistical test relatively to the stimulus strength (K) instead of
NOC. In this case we should revise the parameters variation range and confirm that all cells is stable
when running the test. 3. EXp3: Results from experiment 2 will guide us

Data Mining And Multimedia Data
ABSTRACT
Data mining is a popular technology for extracting interesting information for multimedia data sets,
such as audio, video, images, graphics, speech, text and combination of several types of data set.
Multimedia data are unstructured data or semi–structured data. These data are stored in multimedia
database, multimedia mining which is used to find information from large multimedia database
system, using multimedia techniques and powerful tools. This paper analyzes about the use of
essential characteristics of multimedia data mining, retrieving information is one of the goals of data
mining and different issues have been discussed. The current approaches and techniques are
explained for mining multimedia data. In this paper applications and models of multimedia mining
are discussed clearly. Nowadays Multimedia mining has become popular field in research area.
Keywords: Data Mining, Multimedia Mining, Clustering, Classification.
1. INTRODUCTION
1.1 Multimedia data mining: Multimedia data mining is used for extracting interesting information
for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of
several types of data set. Fig.1 explains about multimedia mining it is a subfield of data mining
which is used to find interesting information of implicit knowledge. Multimedia data are classified
into five types, there are (i) text data (ii) image data (iii) audio data (iv)video data and (v) electronic
and digital ink [2]. Text data

Statistical Analysis Of Age Based Customers Behavior
Running Head: HISTOGRAM ANALYSIS
Running Head: HISTOGRAM ANALYSIS 14
Statistical Analysis of Age–Based Customers Behavior
Name:
Institution:
Date:
Table of Contents
Introduction 3
Frequency Distribution of Customers based on their Ages 4
Measures of Central Tendency 5
The Arithmetic Mean 5
The Median 5
The Mode 5
Probability Distribution and Probability Density 6
Skewness of the Age Distribution 8
Kurtosis of the Histogram 10
Conclusion and Recommendation 13
References 14
Introduction The following report establishes the buying habits of the customers and determines
what motivates them to come to our business. From research done elsewhere, it was noted that the
age as well as the income level are major determinants of the buying habits of customers. In order to
establish whether this really applies to our business as well, a survey was sent to a sample of 200 of
our customers. The customers were asked to give their age and level of income only and they were
rewarded with a gift card of $25 towards their next purchase. The response rate 62 percent (124 out
of the 200 customers responded) and makes a good sample of customers that can be used in the
statistical analysis of the customers? buying habits. The statistical analysis will involve the
development and examination of the frequency distribution (histogram) chart, the probability
distribution graph, the skewness of the histogram and the kurtosis of the histogram.
Frequency Distribution

Statistical Methods in Measuring Corporate Performance
The purpose of this paper is to provide an overview of commonly used statistical data in the
measurement of corporate performance by examining various models and methods.
Corporate performance can be defined as fundamental measures of organizational aptitude used to
assess the "health" of the organization and to provide focused direction to operations while
supporting managers. In order to measure the company's intangible assets such as customer
relationships, internal processes, and employee learning and development while aligning the
corporation's overall business strategy, statistical analysis of performance measurement data must be
utilized.
The task of evaluating the latest performance measures and aligning business strategy ... Show more
content on Helpwriting.net ...
Each is a reaction to the findings of the one before until a pattern can be observed. This is also rarely
used in performance measurement.
Probability Distributions
Range and distributions depend upon the amount of data available. The information sought in
performance measurement is usually continuous, that is, measurable – and therefore denoted as f(x).
Since the probability or distribution of possible probabilities is continuous, the likelihood that the
probability is any specific point estimate is really zero. The range for probability then depends on
the level of valid, available information.
A probability distribution indicates all values that a random variable can assume and shows the
likelihood of occurrence. By examining distributions of data, important characteristics such as
shape, location, variability, and unusual values can be detected. The conclusion as to the
relationships between different variables can then be generated from the observation of patterns in
data.
An example of a normal distribution test is the Gauss curve. The bell shaped curve represents the
odds of each outcome on the range of several outcomes. In a normal distribution observations are
distributed symmetrically around the mean, 68% of all values under the curve lie within one
standard deviation of the mean and 95% lie within two standard deviations3. This is good for
intervallic continuous variables.
Another type of distribution is done when the results are not ranged along a

Analyzing The Strengths Of Qualitative And Quantitative...
Q–methodology was first introduced by psychologist William Stephenson (1902–1989). "Q–
methodology combines the strengths of both qualitative and quantitative methods" (Brown, 1996).
The method is one that studies subjectivity and can be used on various researches. Subjectivity can
be defined as "a person's communication of his or her point of view" and it is always stems from a
person's "internal frame of reference" (McKeown & Thomas, 1988). An internal frame of reference
can refer to the personal viewpoint of the respondent; in this research, it is the viewpoint of several
people for reasons why project are delayed. This frame of reference has been established through the
experience of working on or being in other ways involved in ... Show more content on
Helpwriting.net ...
Hence, this reference provides much more information than a 'yes, no' question ever did (McKeown
& Thomas 1988). Q–methodology uses the concept of ranking as simplified in Table 4.2. By asking
multiple people from SHTC to rank the 40 statements, the prioritization of these criteria comes
forth. All statements are based on people's opinion on why project delay, yet the comparison with
other people's opinions and ranking the statements will bring about the real subjectivity (Brown,
1993). 4.1.2 The Q–analysis process As the method revolves around the ranking of statements, the
process starts by defining these statements. This set of statements, also referred to as the Q–sample,
should comprehensively represent the concourse: the sum of all things people say or think about the
issue being investigated. The concourse is most typically collected through interviews and focus
group discussions. In addition, sources such as publications, essays, or any other sources related to
the issue may be used. The collection of the concourse and the Q–sample is described in the
previous chapter. What is in fact investigated is not just the concourse itself, but the inherent
"operant structure" that exists in the concourse or the Q–sample (Dryzek, Clark, & McKenzie,
1989). The Q–sample often exists of statements, however, it can be created from basically anything:
from words to paintings or even songs (Brown, 1996). With the Q–sample, the respondents can
participate in the

Marketing Analytics And The Other Job Roles
Introduction into Analytics
Business Analytics and the other job roles connected to this area has increasing job trend in the last
few years, newer and newer jobs appearing in the market, which was not exist 5–8 years ago. Since
I am working in this area, and also an interesting area, I would like to introduce this to the
audience.I am interested in this topic not only because I am working in this area, also because as a
non–statistician person I still could learn many ways of simulating forecasting future events, and in
many cases it is fun, or at least interesting that we can solve the same business problem with many
different techniques!
First of all, let me explain what is Business Analytics?
According to1Gartner (2013) „Business analyticsis comprised of solutions used to build analysis
models and simulations to create scenarios, understand realities and predict future states."
So in summary we are using many types of analytical and statistical methods to transform the raw
data into a shape where we can get information from it, and get a conclusion. Based onthese
informationand conclusions we will be able to make business decisions, which might affect the
future success.
Regarding the Business Analytics we need to understand that it has different levels, based on the
business value it has, and also based on the complexity to apply it. In regards to the complexity
required to apply, we are moving from the descriptive type of analyzes through the predictive, into
the

Statistical Methods Of Psychology Journals : Guidelines...
Wilkinson, L. (1999). Statistical Methods In Psychology Journals: Guidelines And Explanations.
American Psychologist, 54(8), 594–604. Retrieved September 10, 2015.
In the mid 90's, the Board of Scientific Affairs (BSA) of the American Psychological Association
(APA) convened a Task Force on Statistical Affairs whose goal was to "elucidate some of the
controversial issues surrounding applications of statistics including significance testing and its
alternatives; alternative underlying models and data transformation; and newer methods made
possible by powerful computers" (BSA, personal communication with the author, February 28,
1996). This task force consisted of statisticians, teachers of statistics, authors of statistics, journal ...
Show more content on Helpwriting.net ...
Properly defining the population is crucial. When the word population is used, many think of
humans or animals, but population can also consist of observations on research articles, adjectives,
as well as living things. The population is crucial because it will affect almost every conclusion in
an article. Sampling procedure, as well as inclusion and exclusion criteria should be emphasized as
well as the sample size for any subgroups. It also is important to include if you are using a
convenience sample or subjects that are selected randomly.
Assignment
Random assignment will allow for the strongest possible causal inference that is free of extraneous
assumptions. Wilkinson (1999) suggests the researcher provides enough information to show that
the process in making the assignments is in fact random. It is recommended to use a pseudorandom
sequence from a computer generator or published tables of randomized numbers. This also allows
other researchers to check the methods used later. Confounds of covariates are commonly
encountered when using nonrandom assignment, and can affect the outcome. It is best to attempt to
determine the covariates, measure them adequately, and then adjust for any effects. If the researcher
adjusts this by analysis, any assumptions made must be explicitly stated, tested, and justified.
Sources of bias should also be taken into consideration.
Measurement
Most studies have variables that must

Analyzing Market Trends And Developments Essay
Interpret market trends and developments
From: sometimeskub@hotmail.com
To: ling@barklycollege.com
Subject: Analysing the market report for TEE company
Dear Ling,
Here is the marketing analysis report of T–House company according to BSBMKG507 Interpret
Market Trends and development.
The full report is attached. Please reply me with feedback about the report.
Kind regards,
Sanchai N.
Assessment Task 2: Project – Market analysis
Introduction
T–House company is selling chestnut imported from China under Australian brand. The company is
now distributing chestnuts to buyers around Melbourne. Two main customers are the local
restaurants and vegetable and grocery stores. The company has planned to promote chestnuts to
wider market by aiming to others regions around Australia. T–house also launched new product
from chestnut that is "cooked and peeled chestnut" which can be found around Australia at Coles
supermarkets.
Executive summary
This report intends to discuss the following topics:
Interpreting marketing data and development by using statistical analysis methods
Analyse potential impact of business relating to market trends
Use of central tendency and correlation between sets of data for numeric interpretation of market
data
Reviewing business performance by performing qualitative analysis of comparative market
information
Evaluate market performance of existing competitor and their products or services to identify
opportunities and threats
Analyse performance

Human Behavior Analysis On Video Surveillance Systems
ABTRACT
Human behavior analysis plays a vital role in video surveillance systems. In this paper review the
steps processing for identify human in video comprise video segmentation, motion and object
detection, object tracking, object classification, action and activity recognition. A few methods,
techniques and approaches are reviewed with pros and cons. Finally, literature review of human
behavior analysis is explained. This review gives an overview of human behavior analysis.
Keywords:
Human behavior analysis, video segmentation, object tracking, object classification
INTRODUCTION
Human behavior analysis in video surveillance is an emerging research domain and much research
is being done in areas such as image processing [1], ... Show more content on Helpwriting.net ...
Devices and technologies like Charge–Coupled Devices (CCDs) camera, Closed Circuit Television
(CCTV), thermal camera, High Definition (HD) camera and night–vision are widely used in video
surveillance to gather information and to observe people, events and activities [10]. At present there
is an increased desire and need in automated video surveillance application intended for evaluating
human behaviors.
Human detection in video is complicated due several factors such as variable appearance of people,
camera positions, and wide range of person poses, illumination variation, brightness, backgrounds,
contrast level and human occlusions [ ]. Different people perform the same action differently and the
same person performs the same action differently at different times. Due to huge diversity of human
body size, appearance, shape and human actions to perform automatically recognize actions are very
challenging [12]. Steps for processing a video are video segmentation, motion detection,
background modeling, foreground segmentation, object classification, motion tracking, activity
recognition, and camera handoff and data fusion [].
The main objective of this paper is to provide a review of human behavior analysis in video
surveillance. The review is not exhaustive, but gives an overview of the major steps involved in
human behavior analysis and some of the techniques which are used in these steps. The rest of this

Measures Of Association Are A Type Of Descriptive...
Measures of association are a type of descriptive statistic used in order to find any variables that are
related to each other. The associations created by chance among variables tend to decrease in a
larger sample size. There are three components to measures of association; the direction of the
association, the strength of the association, and the statistical significance of the association.
Depending on what is being measured it is important to choose the correct method for determining
associations. For example, if one is looking only at nominal variables, then it would be best to use
the lambda method for detecting association. Some other tests for measures of association are
Somer's D test, Cramer's V test, Phi test, and the Gamma test. A regression analysis determines how
strong an association is among a single dependent variable and independent variables. It also used to
explain any differences that are seen in the dependent variables by using information from the
independent variables. ("Regression example: descriptive analysis"). Typically, when running any
type of regression analysis, there are a few factors that are taken of special interest. These include
the strength and direction of the associations (inverse or positive relationship), which independent
variables are actually important for influencing the dependent variable, and, with given independent
variables, being able to predict a set of values for the dependent variable. When running a regression

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

Length Of Stay Analysis
3.4.4. Patients total Length of stay in the hospital Patient's length of stay needs to be accurately
evaluated to build a complete and accurate simulation which replicates the real life situation. Using
previous data directly would not give us exact time as data has so much variability in it. Hence we
used Expert Fit statistical software to make distributions of patients LOS from previous data. Using
such distributions gave us more accurate values on patients LOS with no errors. Distribution were
made such that they fit the simulation software SIMIO to implement the distributions analyzed in
SIMIO. To describe calculated Length of stay for each patients we further explained the analysis of
length of stay in detail below and to make it ... Show more content on Helpwriting.net ...
Density Histogram plot for LOS for SDS patients in PACU Figure 13. Distribution function
difference plot for SDS patients LOS in PACU From above calculation for each phase of same day
patients their total length of stay was calculated from the formula below. Total length of stay for
SDS Patient is [(8.660280 + Random.LogLogistic (3.983742, 94.44468)) + (23.998799 + Random.
Gamma (1.820359, 29.215313)) + (15.451090 + Random. Lognormal (Math. Log (49.635313),
0.500759)) + (1 / Random. Weibull (2.503230, 0.019615))] TCI Patients Length of Stay Analysis:
Table 6. TCI patient length of stay in PACU Data Characteristic Value Source file Observation type
Real valued Number of observations 471 Minimum observation 35 Maximum observation 446
Mean 125.7219 Median 105 Variance 5,032.32 Coefficient of variation 0.56425 Skewness 1.79274
Table 7. TCI patient in PACU Relative Evaluation of Candidate Models Figure 14. Density
Histogram plot for LOS for TCI patients in PACU Figure 15. Distribution function difference plot
for TCI patients LOS in PACU From above calculation for each phase of same day patients their
total length of stay was calculated from the formula below. Total length of stay for TCI Patient is
[(5.015277 + Random.LogLogistic (3.971320, 98.764988)) + (Random.JohnsonSB (2.303474,
1.204585,

Statistical Control
STATISTICAL CONTROL OF HOT SHOT PLASTIC KEYCHAINS
TBD
Webster University
BUSN6110, Operation and Project Management [ 2/1/2015 ]
Abstract
This term paper examines a case study with Hot Shot Plastics company in which statistical process
control (SPC) with variable measurements using X bar and R control charts is used to determine
whether the process variability is in control. Sample data are utilized to demonstrate how to use X
bar and R control charts to check if all the sample points are within the control limits. Patterns on
the control charts are analyzed to understand the possible reasons that the process is out of control.
Keywords: [control charts, statistical process control, patterns]
Statistical Control of Hot ... Show more content on Helpwriting.net ...
| Competitors are investing a lot of capital to use expensive computer and laser controlled machines
that produce good quality keychains. | It does not cost much to sample the curetimes. | Reading the
precise curetimes are susceptible to human errors. | If successful in achieving statistical control of
curetimes, Hot Shot Plastics can effectively compete with low cost offshore competitors because
there will be less number of discarded defects. | Other offshore companies are eager to sell the
plastic keychains at a very low price because offshore labor cost is much lower than the US. |
Causes and effects of the problem
Less accurate and precise curetimes of the plastic keychains cause less than desirable edge quality of
the keychains. Poor edge quality of the keychains makes it difficult to obtain the required shape of
the keychains.
Use of the X bar and R control charts in understanding of the problem
Many quality characteristics can be expressed in terms of a numerical measurement. A single
continuous and measurable quality characteristic is called a variable. Control charts for variables
usually lead to efficient control procedures and are used extensively.
It is a standard practice to control both the mean value and variability of the

Human Autoimmune Diseases
Abstract–Immune system failure can cause many human autoimmune diseases which make body
produce antibodies in the blood which attack human antigens not foreign antigens and it can cause
many serious and chronic illnesses. This phenomenon results in an auto–immune response which
generate the auto–antibodies (ANAs) during this response. This auto–antibodies (ANAs) may be
identified through the indirect immunofluorescence test (IIF). But unfortunately, today the IIF is still
a subjective method as well as time and labor intensive. Thus, to address these problems, computer
Aided Diagnostic (CAD) systems have been developed which automatically classify a human
epithelial cells type–2 (HEp–2) cell images into one of its known patterns. This ... Show more
Then, check them under a fluorescence microscope after adding a fluorescence tag which binds to
the antibodies according to the type of present antibodies. Hep–2 cells take one of six different
patterns which are: homogeneous, fine speckled and coarse speckled, centromere, nuclear,
cytoplasmic [6]. However, IIF is a subjective method in discovering immune diseases, it depends
too seriously on the experience of physicians and unfortunately the accuracy of inter–laboratories
agreement drops to 76% for recognition of stained patterns which require more detailed diagnosis
[7]. This drops is due to many reasons like the Quantitative information which supplied to
physicians are not enough, reading from systems and optics are varying from each other, irradiation
occurred through the photo–bleaching effect caused by the light [8] and the reproducibility of the
diagnostic protocol is low [9]. In recent years, for these reasons and more there has been increasing
interest in employing various image analysis techniques for classification of HEP–2 cells images.
Many CAD systems which have been proposed in this field are concerned with shape and
morphological features [10–23]. Cordelli et al. [11] consider a heterogeneous set of texture features
belonging to different categories, statistical descriptors, spectral measures, local binary pattern
(LBP) and morphological descriptors.

Social Stigma Of Mental Illness
behavior is seen as threatening, society results to punishing them or avoiding them (Hinshaw, 2007,
p. 7). An example of this is that "each year in the United States between 5 and 6 million individuals
with mental disorders lose, fail to seek, or cannot find employment as a consequence of their mental
illness" (Hinshaw, 2007, p. 124).
Causes and Impact of Social Stigma How society responds to mental illnesses is mainly influenced
by the amount of knowledge they have regarding mental disorders (Hinshaw, 2007, p. 7). Thus "the
way in which mental disorders is defined has major implications for social responses" (Hinshaw,
2007, p. 7). People who suffer from mental illness have different behaviors than others; as a result,
people in society ... Show more content on Helpwriting.net ...
Stutzman provides insight on a different way to define mental illnesses because, like Hinshaw, she
does not find that "behavior that is deviant from statistical and societal norms necessarily means
someone is mentally ill" (personal communication, May 25, 2017). Instead in the field of
psychology and psychiatry, mental illnesses is also "defined as behaviors, thoughts, and emotions
that cause distress and impairment in someone's life" (K. Stutzman, personal communication, May
25, 2017). Instead of being a deviance of social and statistical norms, "mental illness is more a
reflection of impaired functioning in the realm of social, occupational, and personal issues (K.
Stutzman, personal communication, May 25, 2017). Efforts have been made to shift the model of
mental illnesses from a "dichotomy" to "view mental illnesses in more shades of grey" (K.
Stutzman, personal communication, May 25, 2017). The media also greatly impacts the social
stigma of mental illnesses (Corrigan & Watson, 2002, p. 17; Davey, 2013; Friedman, 2014). The
mentally ill are often negatively portrayed in movies and the news (Corrigan & Watson, 2002, p. 17;
Davey, 2013; Friedman, 2014). In fact, results from an analysis of "English–language movies
featuring at least one main character with schizophrenia" released from 1990–2010 depicted that "a
majority of characters displayed

Comparing Techniques For Collecting Data On Primary And...
STUDENT NAME: Bailey Jacobs EMAIL: bjaco50@eq.edu.au TEACHER: F Stevenson D
Harman K Podlich A Brown Statistical Inferences Purpose: In this unit we have studied how to:
comparing techniques for collecting data in primary and secondary sources. Making sense of the
position of the mean and median in skewed, symmetric and bimodal displays to describe and
interpret data. Constructing histograms and back–to–back stem–and–leaf plots. Calculate relative
frequencies Investigate data used and its validity Now it is your turn to demonstrate your fluency
and understanding of these concepts in applying your knowledge from simple through to complex
application in real life and theoretical contexts. Task Are we a typical Australian Year 9 class? In this
task you will collect, process and compare data of body measurements from two groups: your
mathematics class and an ABS CensusAtSchool sample. Your teacher is looking for evidence of
mathematical understanding, fluency and problem solving and reasoning abilities, not only correct
answers. When using a calculator, show enough working so that your teacher can see the method
used. Content: Identify everyday questions and issues involving at least one numerical and at least
one categorical variable, and collect data directly from secondary sources. Construct back–to–back
stem–and–leaf plots and histograms and describe data, using terms including 'skewed', 'symmetric'
and 'bi–modal'.

Six Sigma Essay
1. Summary of the topic:
In this document we have done a detailed study of Six Sigma and Lean Six Sigma and how they
help Business Analysts to maximize their skill sets to attain maximum process improvements in
their activities. We introduce the topic, provide a historical timeline and disucuss its relevance to the
topic. After that, we analyze current state and use the information to provide results in Analysis and
Synthesis section.
Introduction:
Process Improvement or Business Process Improvement is an aspect of organizational development
(OD) in which a series of actions are taken by a process owner to identify, analyze and improve
existing business processes within an organization to meet new goals and objectives. Six Sigma,
Lean and ... Show more content on Helpwriting.net ...
You are 2,000 times more likely to reach your destination when you fly than your luggage is –
because airline safety operates at a level higher than 6 sigma, while baggage reliability operates at
about 4. 3 to 4 sigma means two short or long landings at major airports every day, while a 6 sigma
process means one short or long landing at major airports every 5 years. [3]
It is essential that surgery goes right. 3 to 4 sigma means five thousand incorrect surgical operations
per week while a 6 sigma process means less than two incorrect surgical operations per week. [3]
Lean:
Lean maximizes throughout by reducing waste. It started out in manufacturing and production, and
later branched out to be adopted in other areas. It is increasingly being deployed in startups, and in
strategy analysis.
Lean Six Sigma:
Lean Six Sigma combines Lean's time–focused waste–elimination philosophy to dynamic analytical
problem solving toolkit of Six Sigma.
Waste is a non–value adding activity. 7 types of wastes: Transportation, Inventory, (Excess) Motion,
Waiting, Overproduction, Over–Processing, and Defects (TIMWOOD). 8th category: Unused
creativity.
DMAIC (Define, Measure, Analyse, Improve, Control) is a sub–methodology helps improvement of
the existing processes. It is often necessary to iterate through the Measure–Analyze–Improve steps.
Define Stage: Benchmark and baseline the process. Structure and decompose the process into
controllable sub–processes.
Measure Stage:

The Current Status Of The U.s. Economy
Despite the common assertion that all math is inherently difficult, statistics is rooted in simple
mathematical concepts. Descriptive statistics, as depicted by author Charles Wheelan in Naked
Statistics: Stripping the Dread from the Data, is a way to summarize raw data in order to make
immense data sets more manageable and understandable (2013). As Wheelan points out, everything
from presidential elections to baseball games can be summarized using statistics (2013). One of the
most prevalent uses of statistical analysis is to summarize data in order to reflect the health of the
U.S. economy, specifically through gross domestic product (GDP). Wheelan touches on the subject
of how statistics can be used in conjunction with economics; however, there is much more he does
not mention. The health of the U.S. economy is imperative to every American. It dictates whether it
is a good time to splurge on a pricey vacation, invest in a new company, or save for the future. The
only way to truly understand the current status of the U.S. economy is by being able to interpret
statistical analyses accurately, which may be trickier than it appears. Just like any other topic, it
starts with the basics (mean, median, etc.), but Wheelan soon makes it apparent that things become a
bit more complicated when one looks past the fundamentals of statistics (2013). Interpreting
statistical analyses is more than just being able to read charts and graphs. Descriptive statistics is
rooted in simplicity;

The Next Rembrandt Summary
Data is beginning of creativity in this case. In case of Anthem, data is beginning as well as an end
result. Data is used to share business insights using CIE
Data is the new money and this case exemplifies that. One can re–create past and innovate using the
data. So, Anthem should use the client data to prepare new models; may be with a intention to regain
lost customers for a client as part of WinBack campaign
"The Next Rembrandt" was created to highlight ING's dedication to innovation and its sponsorship
of classic art. Most of the data at Anthem is used for business life. It can be leveraged to create a
campaign that touches human soul
Deep learning algorithms were used here. Anthem mostly works with statistical analysis and
predictive

Breast Cancer Essay
Breast cancer is the most commonly detected disease in females everywhere [1]. There are several
efforts done to give solutions to Detect of breast cancer using thermal images. In this section we will
make a review for some of these iterations.
Araujo et al. [2] proposed a framework to use interval data to model breast strange shape as
malignant, benign and cyst. They used a segmentation method that drawing ellipsoidal elements that
selects the area of each breast. Ellipses were adjusted for the patient's breast. The morphological and
thermal matrices are used to calculate minimum and maximum temperature values were extracted
from. They presented a high accuracy supervised and unsupervised technique that used breast
thermal images. ... Show more content on Helpwriting.net ...
[6] and Hough transform. They used range of temperature in ROI, the mean temperature, standard
deviation and quantization of the higher tone in an eight level posterization. These features were
extracted from the entire breast image and breast quadrants. Support vector machine classifier gave
an accuracy of classification around 86%. The method has compared the entire image with other
related works presents superiors values of accuracy and sensitivity but lower specificity. Another
comparison is made using analysis of area under the ROC curve, concern this, results from this work
are better for Lacunarity measures, but lower when the Hurst coefficient is used.
Motta et al. [6] developed an automated segmentation method, which used thresholding based on
highest temperature of the breast for the detection of lower border. Upper limit of region of interest
(ROI) is obtained by detecting axilla. The center point of the segmented image is used for separating
right and left breast. But in an asymmetric breast, the center point of the breast may not detect right
and left breast ROI separation point.
Qi et al. [9] used Canny edge detection followed by Hough transform for the detection of lower
boundaries of the breast. They assumed that lower breast boundary has a parabolic shape. In an
abnormal and asymmetric breast, generalization of lower boundary as parabola may result in error in
the detection of ROI of breast.
The Hough transform method is also

The Placebo Effect Is A Phenomenon Of Human Health...
The placebo effect is a phenomenon of human health improvement due to the fact that one believes
in the effectiveness of certain effects, which in fact are neutral. The degree of this effect depends on
the degree of human suggestibility and external circumstances of suggestion. This is therapeutic
inculcation. It does not require any special skills, because criticality of consciousness can get
overcome by binding instilled information to the actual object, tablet or injection. The placebo effect
is a combination of a natural course of a disease, effects which arise in the diagnosis, monitoring and
nonspecific medical interventions (Goldacre, 2008). The problem is that it is not known what
specific effect self–hypnosis has in conjunction with medication. Also it is impossible to study of the
natural history of disease without surgery, as it is absolutely unethical. Development of approaches
itself is able to separate the placebo effect from pharmacologic action of any drug; that is a problem
too because it is complicated. In 1785 the term placebo entered the medical lexicon and was applied
to treatments that were known to be ineffective physiochemically but satisfied the patient's desire to
be treated (Wampold, 2016). The strongest factor of placebo effects is the belief of doctors and staff
in medicine effect. Many experiments had been conducted, have which demonstrated a placebo
effect. One of them was a textbook case for the study of effectiveness of reserpine's

Cluster Analysis And Factor Analysis
Introduction
Cluster analysis has many different algorithms and methods to classify objects(Saunders, 1994).
One of the challenges faced by the researchers in different areas is to organize their data which is
possible by cluster analysis, it is a data analysis tool which focus on classifying the different objects
into groups such that the degree of association of the objects in a same group is highest if they
belong and least if they do not belong. Cluster analysis is a simple term, it does not identify any
statistical method or model and also there is no need to make any assumptions about distribution of
data, it is used to form groups of relevant variables without providing any explanation (Stockburger,
n.d.).
Despite their popularity, cluster analysis do provide a great opportunity for confusion and
misapplication when compared to factor analysis, discriminant analysis and multidimensional
scaling (Saunders, 1994). Both cluster analysis and factor analysis is used to organize the data into
clusters or onto factors, most of the researchers who are new to this concept may feel that these two
analyses are same, but they differ in many ways, the main objective of cluster analysis is to
categorize the data, whereas factor analysis simplify the data, it explains the correlation in a set of
data and relate variables to each other (Verial, n.d.). Cluster analysis and discriminant analysis are
the two terms where we can often get confused, the basic difference between them is

Grade 1 Research Paper
Grade 1 – The resource selected to effectively support the recognition and classification of 2D and
3D shapes is a maths game 'Kangaroo Hop' accessed from Math Playground (URL
http://www.mathplayground.com/ASB_Kangaroo_Hop.html). The game can have up to 4 players
and the aim is to be the first to reach the end. To win, a player must correctly identify the shape that
comes up at the bottom of the screen and 'hop' onto it. For example, if the word sphere comes up,
the student must correctly identify the sphere and hop onto it. The game utilizes both 2D and 3D
shapes and also includes some shapes that children may not be aware of such as parallelograms and
pentagons. If the student chooses a wrong answer, they fall into the water and are given ... Show
more content on Helpwriting.net ...
It also acknowledges the content description as children use origami skills to fold their two–
dimensional nets into three dimensional objects, such as prisms and pyramids. Thinking about 3D
shapes through nets can be very beneficial to understanding the representation of shapes in multiple
forms (Tartre, 1990). They help students to conceptualise surface area, the logistics of volume and
side lengths through special visualisation. Before the construction of the 3D shapes, while children
are in the decorating stage, I will prompt thinking by asking how many sides their shape will have
when it's completed or what side of the net do they think will be the base. One key problem with the
worksheet selected, is that it names the 3D shape that will be created from the net. I will remove this
because I think it is essential for students to be able to recognise and correctly identify the shape for
themselves as this is something they are asked to do in most standardised maths tests. This idea is
supported by the NSW Department of Education as they recognise students must have the ability to
"perceive and hold an appropriate mental image of an object". In order to further support the
development of modelling 3D shapes, I will implement another creative activity where students can

Biological Theories Of Criminal Behavior
"Biological Theories of Criminal Behavior" Many biological theories of crime have been developed
to explain the analysis of crime and criminality. Scientific studies of research have formed theories
as to what makes a criminal a criminal and what social factors can contribute to criminality. As a
result, criminology scientists Cesare Lombroso, William Sheldon, and Sandberg believe that the
presence of certain physical traits made it more likely that an individual would become a criminal.
The three scientist each share significant theories of criminal characteristics, criminal personality
and criminal behavioral tendencies. Criminologist Cesare Lombroso studied scientifically the "born
criminal" Atavism theory, which he believed modern criminals share physical characteristics with
primitive humans, Sheldon studied the somatotype theory, the three body types relating to an
individual personality and Franz Gall discovered and studied the phrenology theory, "bumps on the
head" could indicate certain criminal tendencies. Each theorist philosophy of criminality conveys a
scientific hypothesis as to what biosocial elements of crime establishes an individual as a criminal.
In 1987, while conducting an autopsy of the brigand Villella, Lombroso experienced an unusual, but
interesting epiphany while examining the skull of criminal Villella Giuseppe. Cesare Lombroso
discovered an indention at the base of the skull that he perceived to be commonly shared only
amongst apes.

##thodontics And Dentofacial Orthopaedics
The present (in vivo) study was carried out in the Department of Orthodontics and Dentofacial
Orthopaedics, Faculty of Dental Sciences, King George's Medical University, Lucknow (U.P.),
India; in association with C.I.P.E.T (Central Institute of Plastics and Engineering Technology),
Lucknow (U.P.), India to evaluate and compare the effect of intraoral environment on the properties
of different alignment wires retrieved from the patients mouth(72 arches) after initial alignment (4
months) . Selection criteria of subjects were as follows: Inclusion criteria: 1. Upper and lower fixed
orthodontic treatment required. 2. Had not undergone orthodontic fixed appliance therapy before. 3.
The patient between 12 – ... Show more content on Helpwriting.net ...
The constant stress allows the design of orthodontic archwires that generate the continuous and
biomechanical forces that are communicated through the brackets for the leveling and alignment
stages of tooth movement. Cu–NiTi or Thermoelastic NiTi (Group III)– thermally activated nickel–
titanium has transition temperature close to the level of body temperature(Rohit Sachdeva, 19944).
An 0.014 inch(Type III, 35⁰ Cu–NiTi) round wire is used for this study(Fig.3).The martensitic active
alloy employs the thermoelastic effect to achieve shape–memory. Copper NiTi generates a more
constant force over a long activation span, is more resistant to permanent deformation and exhibit
excellent springback characteristics. Graded thermodynamic NiTi(Group IV)– 0.016 x 0.022 inch
rectangular arch wire is chosen for the study (Fig.4).A graded thermodynamic wire is a superelastic
shape memory nickel titanium wire that provides gradually increasing forces from anterior to
posterior segment, all within one arch wire. Therefore, graded thermodynamic wire can be deflected
or activated in such a way that it will produce significantly lower forces when deflecting it in the
area that engages relatively small anterior teeth, while it will gradually increase the force moving
from the anterior to the posterior segment of the wire(Kuftinec and StomD,20085). Hence, Group

Variation and Its Causes
Variation The divergence in a set of numbers in a given data range is referred to as variation. It has
common measures including variance, standard deviation and inter–quartile range that are used in
measuring variation among a set of data. Causes of variation In research data analysis, evidence
suggests that the main cause of data variation is measurement error, especially f the instruments
used are not guaranteed to provide accurate results (Jianqing, 1991). Nonetheless, chances of errors
may occur during data interpretation, recording and, or when reporting the results. These are
accredited to false assumptions that the measured quantity is stable and that the variation are based
on observational errors. Patterns of Variation There are various patterns representative of variation.
However, a bell–shaped curve is the most widely used variation pattern and is considered the normal
curve. In this curve, the highest point is indicative of the most probable occurrence. The other curve
pattern is bi–modal curve, which is dual peaked. This pattern easily identifies a process with a
special cause of variation and, which has undergone some changes. Variation pattern can also take
the form of a skewed curve that resembles a normal curve forced to lie on its sides. This is primarily
a representation of data that is not identical on both sides. Understanding Variation Variation is all
around people. There are variations in the ability to undertake functions; ways of

Case Study Of MRI
Subsequent images produced by MRI are important in monitoring the progression of MS. Lesions in
white brain matter regions would indicate damage to axons, but they do not necessarily indicate
someone has MS and can be the result of an accident. Neurologists find it useful to track the
progression of the disease by evaluating how lesions change in size and shape, and "accurate
temporal measurements of neural changes may lead to a better understanding of the disease"
(O'Donnell, p. 16, 2001). After MRI scans are performed on a patient, the images can be segmented
and an analysis can be done to determine how the disease has changed since the patient's last brain
scan. Neurologisits have several different classifications to determine how MS ... Show more
This approach is more consistent than manual delineation, but still time consuming and not
reproducible" (Jain et al., p. 372, 2015). Therefore it is important for neurologists to agree on a
single efficient method that will allow for more accurate and consistent results. When segmentation
is performed on an image, the goal is to divide the image into smaller non–overlapping sections that
encapsulate a type of component. This can be accomplished in many ways. Thresholding, for
example, simply separates objects based on their color value. Edge–based segmentation classify
objects by first using an edge filter. This approach "is usually less than perfect. Often, a scientist will
have to make changes to the results of automatic segmentation" (Glasbey & Horgan, p. 21, 1995).
Region–based segmentation group items based on similar properties and proximity. Sometimes
textures can disrupt these regions, which is problematic because "for most natural textures, simple
statistical measures are of little use" (Delmas). It might be possible to improve results by combining
all of these methods, but none of them consider the larger meaning of the images they process.
There are two perspective approaches to image segmentation. Top down segmentation is associated
with supervised methods, which means that "features belong

Measure Of Dispersion And Nonprofit Administration
Measure of Dispersion is a descriptive statistic that complements the measures of central tendency
as it indicates how data relates to a variable and its mean. As we examine the different types of
measure of dispersion within public and nonprofit administration, we will also cross–examine this
idea among other academic disciplines to discover how this statistical concept is used among other
researchers. In addition, we will learn the importance of the proper utilization of identifying
measures of dispersion and how to fully explain this idea. This is important because if we fail to
properly use the measures of dispersion, we can jeopardize the reliability of our research and the
validity of our data collected. Measures of dispersion reveals to a researcher how parts of data may
gather around the mean of the variable of interest. Using measures of dispersion will also allow the
researcher understand the population or sample of interest to help researchers draw better
conclusions. To understand measures of dispersion, we must first identify the statistical concepts
that help us measure dispersion. As we progressed through primary and secondary schooling, we
probably learned different mathematical concepts such as mean, median, mode, and range. These
mathematical concepts may be considered easy mathematical concepts, but they are part of complex
statistical ideas. Meier et al. (2015) discusses in his textbook the usage of range and other variety of
dispersion measures. He

Investigating The Statistical Properties Of An Individual...
Reaction Time
Aim:
To determine the statistical properties (mean, standard deviation and tendencies in data) of an
individual's reaction time when tested with audial and visual stimuli, as well as the importance of
these properties.
Notes and Comments
In this experiment students will use a reaction time test to determine the delay between audial and
visual stimuli being displayed and the student reacting. Once a large (N>>3) sample size is taken,
statistical calculations can be made to determine the mean, and standard deviation; these
calculations help us build a mathematical model that can then help predict the possibilities of data
points lying on a number. This is if the recorded data is of a normal distribution and follows a
Gaussian ... Show more content on Helpwriting.net ...
The equation for calculating the standard deviation is as follows:
When substituting values for deviation and sample size the standard deviation comes out to be:
Now that we have a visual representation of the data in graph form, as well as calculating the mean
and standard deviation it is clear and undisputable that this data set is of a normal distribution and
will follow all trends set by the Gaussian curve, this is because it has slightly over 68% of data
points within one standard deviation and slightly over 95% of all data points within two standard
deviations.
The same calculations can be done with the data from the sound test to calculate the mean and
standard deviation for the sound reaction test, the mean and standard deviations come out to:
Although each result carries uncertainty, these uncertainties some somewhat reliant upon the
computer hardware that is being used when undertaking the experiment. The hardware that is used
on a computer has a response time of anywhere from 1–100 milliseconds. For the light test monitor
response time is a big factor, a delay between the monitor sending information to the screen and that
information being displayed is called display lag and many professional computer gamers use IPS
monitors to reach a response time of 1

Methods of 3D Image Analysis
Introduction
Since the late 1980s, arguably the most successful method of 3D image analysis has been the
model–based segmentation approach. This approach, in its most mature form, matches 3D image
information to models which describe not only the expected shape and appearance of a structure, but
also statistically–based information about common variations in the structure of interest. In this way,
the natural deviations in organic structures are accounted for in the statistical shape model, or SSM.
This literature review seeks to present the present stake of the field in terms of representing
structures in this way, through a review of the methods of representing bones. Search algorithms
used to locate and align a model to the image data will then be discussed and described. Techniques
and algorithms used to model the appearance of bones will be analysed and commented on,
including the construction from, and correlation of, bones to their models. Case studies will be
mentioned throughout to demonstrate the application of SSMs to image segmentation needs. Finally,
some further applications of these models will be expanded upon.
Representation of Bones
There are a number of methods in which training data for SSMs can be gathered, and all involve
some type of volumetric image set to have been obtained. This is done primarily through the
modalities of computed tomography or magnetic resonance imaging, and sometimes both in
conjunction (1). Both binary and fuzzy voxel data can

Parameterization Essay
3.2.6.3 Parameterization This refers to analysis parameters and input layer preparation to run
FRAGSTATS in order to calculate spatial metric indices. This includes categorical map formatting
and setting analysis parameters (sampling strategies and specifying neighbor rule to delineate
patches). FRAGSTATS accepts several types of input image data formats, ESRI grid, ArcGIS raster
grid, GeoTIFF grid (.tif), ERDAS Imagine grid (.img), SAGA GIS binary format grid (.sdat) and
several others. For this study, classified maps with ERDAS Imagine grid (.img) formats used as an
input. All input maps were integer grids i.e., each cell assigned an integer value corresponding to its
class membership or patch type with the measurement units in meters. ... Show more content on
Helpwriting.net ...
3.2.7 Statistical Layer Generation and Zonal Metrics Analysis This is conducted mainly to address
and measure the direction and location of urban sprawl in the study area using ZonalMetrics
Toolbox. ZonalMetrics toolbox is a new open source tool developed by Adamczyk and Tiede (2017)
and written in python script to be added as toolbox in ArcGIS 10.2 or latest versions. It is such an
important python tool with a specialized functionality for calculating spatial metrics at zonal level.
The tool computes selected spatial metrics such Class Area (CA), Number of patches per class
(NPC), Zone Area (ZA), Percentage of zone (PZ), Total Class Edge (TE), Edge Density (ED) and
Diversity (SHDI) indices in the same way they are calculated in FRAGSTATS but here indices are
calculated per class and per specified zones. The toolbox has the following 3 available utilities
(Adamczyk, and Tiede, 2017): it accepts user–defined vector based categorical (polygon) layers for
direct analysis; it provides landscape metrics designed to calculations within any user–defined zones
for example regular gridded zones, administration zones, environmental zones, and last it provides
functionalities to generate specific zone (pies, hexagons) to serve as statistical

Frequency Distributions and Sampling
Frequency Distributions:
A normal distribution can be regarded as the most important continuous probability distribution in
statistics since it can be utilized to model several sets of measurements in business, industry, and
nature. For instance, normal distributions can be used to measure the systolic blood pressure of
humans, housing costs, and the lifetime of television sets through random variables. Generally,
normal distributions can have any mean and positive standard deviation as the two parameters
totally determine the shape of the normal curve during evaluation. In this case, the mean determines
the location of the symmetry line while the standard deviation defines how much the data are spread
out ("Normal Probability Distributions", n.d.). In the United States, the age at time of death is more
likely to closely approximate a normal distribution than annual income because data on income and
income inequality are more controversial. Annual income does not closely approximate a normal
distribution because there are different approximation methodologies and the fact that households
generally have more than a single individual. Furthermore, annual income does not closely
approximate a normal distribution because of the continued growth in income inequality due to
various factors such as the high rates of unemployment. On the contrary, age at time of death closely
approximates a normal distribution because the mortality rates in the United States have a

Statistical Tests and Meta-Analysis
1. a. Identify 3 methods statistical inference listed attached flowcharts. (1) Chi–square tests. (2)
One–sample t test. (3) One–sample z test. b. Describe in detail each method and how you could use
this method in health care administration. (1) Chi–square tests. This nonparametric test procedure
operates by tabulating a variable into different categories and then computing a corresponding chi–
square statistic (Chi–square tests, 2007). This so–called "goodness–of–fit test" is used to compare
the observed and expected frequencies in each of the different categories in order to determine
whether all of the categories contain the same proportion of values or that each category contains a
proportion of values that are specified by the user (SPSS, 2007). According to York (2002), "The chi
square is the appropriate statistical test for significant differences in selection ratios" (p. 253). The
chi–square test does not require assumptions concerning the shape of the underlying distribution that
is involved and all data are assumed to be random samples (SPSS, 2007). This test procedure has
been used to good effect in health care administration settings in the past, including the
determination of psychiatric assessment of patients using psychogeriatric assessment scales and
present state examination (Sood & Paramjit, 2006). Likewise, the chi–square test procedure was
used by Sood and Paramjit to analyze ICD–10 criteria for psychiatric diagnoses to identify the
breakdown of

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