Investigating Correlation Coefficient Of Correlation

Investigating Correlation Coefficient Of Correlation
a. I can use here z test for one sample proportion since I want to compare if there is a significant
difference between the sample proportion (i.e. PKU rates in that certain state) and the population
proportion (U.S. PKU rate distribution). Also since the sample size is sufficiently large, then it does
not pose any problem with the normality (since np > 5). b. In this case to get the correlational
coefficient of the variable if it is ordinal, then I could use Spearman rank rho coefficient and if it is
interval or ratio level I could use the Pearson product coefficient of correlation. In the end I can
check if the correlation is statistically significant by using the t test for significant correlation
coefficient. c. I can use here t–test for dependent samples (because of the phrase "before and after"),
since the same group of sample will measured twice, before a treatment, and after a treatment. Of
course, we need to check if all the assumptions are met, such as normality, equality of variance and
randomness of samples. d. I can use here Chi square test of independence. Since we want to know if
there is a relationship or difference between the college students' responses to the likert question,
based on their major fields of study (nutrition, nursing, and social work) So we want to test if the
two variables are dependent or independent with one another. Assumption of Chi square must also
be checked. e. Either I can compute for the mode of the distribution (for descriptive
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An Analysis Of Recommendation Algorithms
An Analysis of Recommendation Algorithms (Status Report) K. Suzanne Carroll School of
Information University of Texas Austin, TX 78701 U.S.A. ksuzannecarroll@utexas.edu Ankita De
Department of Computer Science University of Texas Austin, TX 78701 U.S.A.
ankitade@cs.utexas.edu 1. INTRODUCTION Recently recommendation system use has risen in
popularity as their algorithms interpret user preferences and guide customers to movies to watch,
books to purchase, or restaurants to dine. This popularity, along with competitions where students
build novel recommendation systems, peaked our interest in the mechanics behind recommendation
algorithms. We have design our project to explore and evaluate the algorithms which influence how
recommendation systems operate. Following the submission of our project proposal a month ago,
our project has changed course. Given our team 's relative inexperience in the field of
recommendation systems, we are now interested in conducting an analysis to fully understand how
the various recommendation methods and techniques perform with mutliple datasets. This status
report will explain the new methods we intend to evaluate and introduce our chosen datasets. We
will explain and summarize this new project within the Summary section, go into detail on the exact
data, tools, and methods we will use in the Methods section, and provde a progress update in all of
the remaining sections. 2. SUMMARY Our project came about because we are both interested

Single Neuron Character Recognition Essay
Nelson Mandela Metropolitan University Character Recognition – Single Neuron WRCI 411 –
Assignment 1 RXXXXX XXXXXX – 2100XXXX August 2013 RXXXXX XXXXXX –
s2100XXXX WRCI 411 – Assignment 1 August 2013 Contents List of Figures and Tables
........................................................................................................................ 1 Figures
................................................................................................................................................. 1 Tables
.................................................................................................................................................. 1
Theory ... Show more content on Helpwriting.net ...
The output is based on a function, usually a step function or sigmoid function (basically a rounded
or differentiable step function). The neuron should output a 0 until it 'fires' when it should output a
1. This allows it to be used as a logic function. For this case the neuron should fire for only one
letter/character (and an interfered equivalent) and for all other possible cases it should not fire. The
strength of a neural network is that it can 'learn' or be 'taught' a pattern and recognise this pattern.
From this it should be able to make decisions for cases it has not seen before. There are many
methods for teaching a neuron, most relying on derivation to find the slope of the so–called weight–
space (some rely on brute–force, but these are seldom used). The basic Gradient Decent rule acts on
the slope of the weight–space and will be used for this report. Method Teaching The code was
written in m–code and implemented by MathWorks Matlab R2011a. The artificial neuron is taught
by manipulating the values of the elements in the weight matrix (W in the code) until the error in the
output matrix (from the dot product of the teaching set (X) and W) is lower than a set number
(0.005). This is achieved with a while loop. Within the while loop is another while loop which loops
another error checking loop though each of the vectors of the X matrix. The code edits each of the
elements of the W matrix by using the equation: = − η − ′ Represented in

An Open Entry Distance Learning Institution With Over...
1 CONTEXT The Open University UK (OU), is an open entry distance learning institution with
over 173,000 students [1]. There are no entry requirements for students wishing to study with the
OU leading to a high level of diversity amongst the student cohort in terms of prior experience and
qualifications. The majority of students have taken a break from formal education before joining the
OU and it is frequently their first experience of distance education. Approximately 21,000 OU
students have declared a disability and many of those receive additional support to help them with
their studies. The OU model of distance learning is to provide student with pre–prepared module
materials which may be in print or electronic format, supplemented ... Show more content on
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That work informed much of the academic development around assessment feedback within the
OU, particularly in the Maths, Computing and Technology faculty, where she was based. In
interviews with students, Walker found the top two most common responses students had to
comments were; they didn't understand the comment or needed further explanation or more
positively that the comment was useful for future work. This was in line with the findings of other
studies around the student experience of feedback which reported students find it lacking specificity
[?Higgins], difficult to understand [?Chanock] and confidence reducing [?James]. This suggests that
whilst there is much feedback being offered to students by their tutors, frequently the comments
provided are not being understood or do not comply with the Ramaprasad definition that feedback
should bridge the gap. In the intervening years since Walker's research, the OU have encouraged
Associate Lecturers to develop their feedback style to include more explanations and examples,
pointing out that their efforts in providing feedback are wasted if the student does not understand
them or is unable to act upon them. If the purpose of feedback is to bridge the gap between
attainment and target achievement then it might be expected that more feedback would be given
where there is a bigger gap to bridge. Thus, it might be expected that the volume and or type of
comments provided to students might differ

Mphil
1/22/07 252corr (Open this document in 'Outline' view!) L. CORRELATION 1. Simple Correlation
The simple sample correlation coefficient is [pic] or if spare parts [pic], [pic] and [pic] are available,
we can say [pic] Of course, since the coefficient of determination is [pic][pic] , [pic] and it is often
easier to compute [pic] and to give the correlation the sign of [pic] . But note that the correlation can
range from +1 to –1, while the coefficient of determination can only range from 0 to 1. Also note
that since the slope in simple regression is [pic] , [pic] or [pic] or [pic]. The last equation has a
counterpart in [pic] , where [pic] is the population correlation coefficient, so that testing [pic] is ...
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Example: [pic] applicants are rated by [pic]officers. The ranks are below. [pic]Note that if we had
complete disagreement, every applicant would have a rank sum of 10.5. [pic]. The Kendall
Coefficient of Concordance says that the degree of agreement on a zero to one scale is [pic]. To do a
test of the null hypothesis of disagreement [pic], look up [pic] in the table giving 'Critical values of
Kendall's [pic] as a Measure of Concordance' for [pic] and [pic], [pic]so that we accept the null
hypothesis of disagreement.. Example: For [pic] and [pic] we get [pic], and wish to test [pic] Since
[pic] is too large for the table, use [pic]. Using a [pic] table, look up [pic] . Since 9 is below the table
value, do not reject [pic]. 4. Multiple Correlation If [pic]is the coefficient of determination for a
regression [pic], then the square root of [pic] , [pic] is called the multiple correlation coefficient.
Note that [pic] where [pic] is the sample variance of [pic], and that for large [pic], [pic]. 5. Partial
Correlation (Optional) If [pic], its multiple correlation coefficient can be written as [pic] or [pic].
For example, in the multiple regression problem, we got three multiple correlation coefficients [pic],
[pic] and [pic] If [pic] and we compute the partial correlation of [pic]we compute [pic] , the
additional explanatory power of the third independent variable after the

Sig Tailed
How do I interpret data in SPSS for Pearson's r and scatter plots Correlations Box Take a look at the
first box in your output file called Correlations. You will see your variable names in two rows. In
this example, you can see the variable name 'water' in the first row and the variable name 'skin' in
the second row. You will also see your two variable names in two columns. See the variable names
'water' and 'skin' in the columns on the right? You will see four boxes on the right hand side. These
boxes will all contain numbers that represent variable crossings. For example, the top box on the
right represents the crossing between the 'water' variable and the 'skin' variable. The bottom box on
the left also happens to represent this ... Show more content on Helpwriting.net ...
Warning about the Sig (2–Tailed) value When you are computing Pearson's r, significance is a
messy topic. When you have small samples, for example only a few participants, moderate
correlations may misleadingly not reach significance. When you have large samples, for example
many participants, small correlations may misleadingly turn out to be significant. Some researchers
think that significance should be reported but perhaps should receive less focus when it comes to
Pearson's r. So what about the scatterplot? You can find your scatterplot in your output file. It will
look something like the graph below. You will see a bunch of dots. Your scatterplot can tell you
about the relationship between variables, just like Pearson's r. With it, you can determine the
strength and direction of the relationship between variables. Relationship strength Try to imagine a
line that connects the dots in your scatterplot. Is this an easy or difficult task? This task can help you
determine the strength of the relationship between your two variables. If your variables have a
strong relationship, it will be easy for your to imagine a line connecting all of the dots. For example,
in our example scatterplots, the dots seem to go together to form a straight line. However, some
scatterplots do not look like this. With some scatterplots, the dots are scattered about so

The Role Of Indian Fdi On Nepalese Economic Growth
3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of
Investment and Export in real terms from the period 1989/90 to 2013/14. The concerned variables
are transformed into logarithm and hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t
〖LnI〗_t and 〖LnX〗_t . Fully Modified Ordinary Least Squares (FMOLS) is the main
econometric methodology used in this paper to examine the role and impact of Indian FDI on
Nepalese economic growth. The FMOLS of economic growth of Nepal on Indian FDI augmented
with level of investment and export has been used to find the magnitude of long run relationship
between the variables under study. GDP is taken as the proxy for Nepalese economic growth. Some
attention is necessary while employing FMOLS test. The variables under study must be
cointegrated. So before applying the FMOLS we examine the cointegration by method of Johansen's
(1990) cointegration test. Prior to employing the Johansen's Cointegration test we perform unit root
test using ADF method. FMOLS method was designed by Phillips and Hansen (1990) to estimate
the cointegrating regressions. This method employs a semi–parametric correction to eliminate the
problems created by long run correlation between cointegrating equation and stochastic regressors
innovations. This method is used to modify the least squares to account for serial correlation effects
and for the endogeneity in the regressions that result from the existence of cointegrating

The Variance Of The Unexpected Market Returns Essay
Because and are iid, the variance of the unexpected market returns is: and the covariance between
the unexpected stock and market returns is: Therefore, combining (B.1) and (B.2) gives the
expression for the noise beta of stock i: The approximation is likely to be good in non–concentrated
markets, like the U.S. stock markets. In the particular case in which all stocks are of equal size (i.e.,
), then we are in the presence of the base case of minimum market concentration ( and ). In this
particular situation, (B.3) is reduced to , corresponding to the framework of Ball (1977). In this case,
equation (15) is reduced to , implying that the unconditional beta will be driven towards one as the
predictability of the market returns goes to zero: . What happens to the noise beta when stocks are of
various sizes? To answer this question, it is helpful to derive the noise beta of a typical portfolio.
From (B.3), the noise beta of the equally weighted portfolio of stocks is given by: while the noise
beta of a value–weight portfolio is: It also comes from (B.3) to (B.5) that even if the stocks are of
various sizes, the noise betas of the stocks and portfolios will also be close to one when the level of
market concentration is low. Indeed, when the degree of market concentration tends to perfect
competition, all terms in RHS of equations (B.3) to (B.5) will be close to one. For instance, as of
December 2015, there were about 7,209 U.S. stocks listed in CRSP database, for a total

Correlation as a Measure of Association Summary
Correlation as a measure of association summary
BSHS/435
January 24 2016
Correlation as a measure of association summary
Introduction
In this essay I will describe correlation is a measure of association as well as describe different
methods of establishing a correlation between variables. In this essay I will also explain advantages
and disadvantages of each method, were each must be applied, and provide particular circumstances
and examples in which a researcher may want to establish correlation
Describe correlations as measured of association
"A correlation is a statistical to determine the tendency or pattern for two (or more) variables or two
sets of data to very consistently" (Creswell, (2012). any ... Show more content on Helpwriting.net ...
In situations where every/or nearly/case has its own unique rink, and there are no or a few ties, the
data are said to be fully ordered" (Monette, Sullivan & DeJong, (2011).The interval data " the
most used measured of association for interval data is the correction or Pearson's r. The correlation
coefficient is mathematically related to both phi and Spearman's rho, making comparisons among
them possible" (Monette, Sullivan & DeJong, (2011).
"There are several different kinds of relationships between variables. Before drawing a conclusion,
you should first understand how one variable changes with the other. This means you need to
establish how the variables are related – is the relationship linear or quadratic or inverse or
logarithmic or something else" ("Relationship Between Variables ", n.d) advantages and
disadvantages for correlational research methods
"Correlation is a measure of association that tests whether a relationship exists between two
variables. It indicates both the strength of the association and its direction (direct or inverse). The
Pearson product–moment correlation coefficient, written as r, can describe a linear relationship
between two variables" Correlation (n.d). As a human service professional and completing research
there are advantages and disadvantages to correlational research methods, such as using
correlational research it allows us to collect data and determine the strength and direction of what it
is we

Essay about The Matrix
<a href="http://www.geocities.com/vaksam/">Sam Vaknin's Psychology,
Philosophy, Economics and Foreign Affairs Web Sites
It is easy to confuse the concepts of "virtual reality" and a "computerized model
of reality (simulation)". The former is a self–contained Universe, replete with its "laws
of physics" and "logic". It can bear resemblance to the real world or not. It can be
consistent or not. It can interact with the real world or not. In short, it is an arbitrary environment. In
contrast, a model of reality must have a direct and strong relationship to the world. It must obey the
rules of physics and of logic. The absence of such a relationship renders it meaningless. A flight ...
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A group of human miscreants succeeds to learn the secret of the Matrix. They form an underground
and live aboard a ship, loosely communicating with a halcyon city called "Zion", the
last bastion of resistance. In one of the scenes, Cypher, one of the rebels defects. Over a glass of
(illusory) rubicund wine and (spectral) juicy steak, he poses the main dilemma of the movie. Is it
better to live happily in a perfectly detailed delusion – or to survive unhappily but free of its hold?
The Matrix controls the minds of all the humans in the world. It is a bridge between them, they
inter–connected through it. It makes them share the same sights, smells and textures. They
remember. They compete. They make decisions. The Matrix is sufficiently complex to allow for this
apparent lack of determinism and ubiquity of free will. The root question is: is there any difference
between making decisions and feeling certain of making them (not having made them)? If one is
unaware of the existence of the Matrix, the answer is no. From the inside, as a part of the Matrix,
making decisions and appearing to be making them are identical states. Only an outside observer –
one who in possession of full information regarding both the Matrix and the humans – can tell the
difference.
Moreover, if the Matrix were a computer program of infinite complexity, no observer (finite or
infinite) would have been able to say with any certainty whose

Matrix the Movie and The Lathe of Heaven Essay
Matrix the Movie and The Lathe of Heaven
The world is not always what you think it is. Things change or can appear to be
different than what you originally thought them to be. So are the worlds in the Matrix and
the novel The Lathe of Heaven. What you thought you knew about your life just went out
the window. There are several similarities between the novel and the movie, and there are
many trends in the movies and novels societies that are portrayed in our society as well.
Also, each proves LeGuins theory on science fiction.
The Lathe of Heaven and The Matrix have many similarities. Both utilize the
number three by providing main characters in sets of three. Also, The Matrix brings an
even more ... Show more content on Helpwriting.net ...
The Lathe of Heaven has no future; the present is the basis of the plot. For
example, every time Orr has a session with Haber, Orr changes the present and
retroactively changes the past. Playing by LeGuins rules for science fiction helps shape
both stories and help put both into a league of their own.
The Matrix and Lathe of Heaven provide example of trends in modern society
throughout their plot. First, both the movie and the novel both portray characters that
have a God Complex. Meaning that they believe they have all the power and it is up to
them to create a world that they see fit. In the Matrix it is the Creator and in the Lathe

of Heaven it is Dr. Haber. In our society we have had and do have dictators that feel like
they are in complete control of how the world should be. There was Hitler, who decided
we need to exterminate over 6 million Jews and millions of others. A modern day
example would be George W. Bush. He acts with consent of the established government
and does what he thinks what is in our best interest, without listening to our own ideas
what we feel what our best interests are. This is dangerous to society just like in the
Lathe of Heaven Haber tries to control society with what he thinks is best, bringing
death and destruction by means of a deadly plague and virus. Bush has ordered the
bombing of dozens of

Advantages Of Principle Component Analysis
3.4. Principle Component Analysis (PCA)
Principle component analysis, also referred to as eigenvector transformation, Hotelling
transformation and Karhunen Loeve transformation in remote sensing, is a multivariate technique
[66] that is used to decrease dataset dimensionality. In this technique, the original remote sensing
dataset, which is a correlated variable, is distorted into a simpler dataset for analysis. This permits
the dataset to be uncorrelated variables representing the most significant information from the novel
[21]. The computation of the variance covariance matrix (C) of multiband images is expressed as:
Where M and X are the multiband image mean and individual pixel value vectors respectively, and
n is the number of pixels.
In change detection, there are two ways to relate PCA. The first method is counting two image dates
to a single file, and the second methods is subtracting the second image date from the corresponding
image of the first date after performing PCA individually. The disadvantages of PCA can ... Show
more content on Helpwriting.net ...
For example, Baronti, Carla [39] concerned PCA to examine the changes occurring in multi–
temporal polarimetric synthetic aperture radar (SAR) images. They used association instead of a
covariance matrix in the transformation to condense gain variations that are introduced by the
imaging system and that provide weight to each polarization. In another example, Liu, Nishiyama
[49] evaluated four techniques, including image differencing, image ratioing, image regression and
PCA, from a mathematical perspective. They distinguished that standardized PCA achieved the
greatest performance for change detection. Standardized PCA is better than unstandardized PCA for
change detection because, if the images subjected to PCA are not calculated in the same scale, the
correlation matrix normalizes the data onto the same scale

Essay on Film Noir Features in Blade Runner and The Matrix
Film Noir Features in Blade Runner and The Matrix
"The Matrix" has a main science fiction theme but also includes features of film noir films. It is
directed and written by Andy and Larry Wachowski. Other than Science Fiction and film noir the
film can also be classed as a Hybrid.
"Blade Runner" is more of a film noir film than "The Matrix". Although it does include action and
fighting scenes but these have film noir features in them. Most ideas about the film are connected
with film noir such as the mystery, the low key lighting and symbolic objects. It was directed by
Ridley Scott and written by Phillip K. Dick, Hampton Fancher and David Webb Peoples.
The main characters in "The Matrix" ... Show more content on Helpwriting.net ...
"The Matrix" and "Blade Runner" both include strong female characters. In "Blade Runner" the
strong female characters are Rachael and Zhora and in "The Matrix" it is Trinity. Audiences demand
strong characters because it is like a role – reversal, in most films men are the hero and leading
characters, which makes it unusual for women to be strong and powerful. The world war influenced
film makers to make films which had strong female characters to encourage women and make them
feel appreciated for all of the jobs that they were doing during the war whilst men were on the
battlefields.
In "Blade Runner" the main character, Deckard, is an ex–policeman and ex–blade runner. He is
found and reassigned as a blade runner to find some rogue replicants who have gone 'On the run'. In
"The Matrix" the main character Thomas A. Anderson is found by a group of people who have
found out that he is 'the one' who will stop "The Matrix", he is bought in to 'The real world' to stop
agents and make the world 'right'.
I feel that in "Blade Runner" the main issues and ideas that the film considers are that the society in
the future is very separated and there are no longer family structures, also that 'real' is a difficult
term to understand as there are replicants in their time and they are made to

Brief Explanation of the Basic Framework of the Principal...
PRELIMINARIES
This section expands a brief explanation of the basic frame–work of the principal component
analysis and fuzzy logic, along with some of the key basic concepts.
A. The principal component analysis (PCA)
The Principal component analysis (PCA) is an essential technique in data compression and feature
reduction [13] and it is a statistical technique applied to reduce a set of correlated variables to
smaller uncorrelated variables to each other. PCA is considered as special transformation which
produces the principal components (PCs) Known as eigenvectors. PCs are sorted decreasing i.e. the
first prin–cipal component (PC
1
) has largest of the variance. That is meanvar(PC1 );var(PC2
);var(PC3
);var(PCp
), where var(PCi ) ... Show more content on Helpwriting.net ...
PCA is considered as special transformation which pro–duces the principal components (PCs)
Known as eigenvectors
.PCs are sorted decreasing i.e. the first principal component
(PC
1) has largest of the variance; That is mean var (PC
1
) var (PC
2) var (PC
3) ... var (PC
p), where var(PC i ) expresses the variance of(PC i ) [14]. The PCA has characteristics and ability to
reduce redundancy and uncertainty. So this paper used the PCA as the preprocessing step on the
multispectral images to reduce the redundancy information and focus on the component; that have a
significant impact on the data.
B. Fuzzy logic
Zadeh [15] introduced the concept of fuzzy logic to present vagueness in linguistics, and to
implement and express human knowledge and inference capability in a natural way. The fuzzy logic
starts with the concept of fuzzy sets which is defined as a set without a crisp. It is clearly defined
boundary and can contain elements with only a partial degree of member–ship.The main power of
fuzzy logic image processing is in the middle step (membership function) [16] A membership
function defines how each value in the input space is mapped to a membership value (or degree of
membership) between the range of 0 and 1. LetXbe the input space andxbe a generic element ofX. A

classical set Ais defined as a collection of elements or objectsx2X, such that each xcan either belong
or not belong to the setA, AvX.

The Level Of Self Esteem And Academic Performance Of...
Abstract
This study was conducted to determine the relationship between the level of self–esteem and
academic performance of PSY105 student at the HELP University Campus in 2014. Convenience
sampling was used to collect the 30 samples from the population, without specific gender and
ethnicity. Based on the literature it is stated that having a high self–esteem produces higher
academic performance. The researcher used an existing Rosenberg Self–Esteem scale to obtain a
raw data of each participant. The statistical test used is the Pearson Product–Moment Correlation
Coefficient. The study was based on a non–experimental correlational design. The results shows a
significant negative correlation between the level of self–esteem and academic performance, this
concludes the level of self–esteem do influence academic performance.
The level of Self–Esteem and Academic Performance Self Esteem has been one of the elements that
play a role in the student's life of education. Past study has found high correlation between the level
of self–esteem as the elements that affects academic performance, it shows that majority of student
with higher self–esteem would be enthusiastically motivated to get involved in the learning
environment (Colquhoun Bourne, 2011). The aim of this research is to determine the correlation
between the level of self–esteem and academic performance. This research begin focusing on two
general factors of self–esteem first is the worth of self which

Psych 625 Statistics for the Behavior Sciences Entire Course
PSYCH 625 Statistics for the Behavior Sciences Entire Course
To Download This Tutorial Visit below Link http://www.studentsoffortunes.com/downloads/psych–
625–statistics–behavior–sciences–entire–course/ (Product Type) : Instant Downloadable
We invite you to browse through our store and shop with confidence. We invite you to create an
account with us if you like, or shop as a guest. Either way, your shopping cart will be active until
you leave the store.
All Tutorials will be E–mailed immediately after the Payment, Please Check your inbox or Spam
Folder and can also be downloaded by clicking on Tutorial Bucket.
For Any Other Inquiry Feel Free to Contact us: studentsoffortunes@gmail.com
For More Tutorials Visit: ( ... Show more content on Helpwriting.net ...
?
–1.6
82.0
?
?
1.8
69.0
?
?
–0.5
85.0
?
?
1.7
72.0
?
3. Questions 3a through 3d are based on a distribution of scores with and the standard Draw a small
picture to help you see what is required.
a. What is the probability of a score falling between a raw score of 70 and 80?
b. What is the probability of a score falling above a raw score of 80?
c. What is the probability of a score falling between a raw score of 81 and 83?
d. What is the probability of a score falling below a raw score of 63?
4. Jake needs to score in the top 10% in order to earn a physical fitness certificate. The class mean is
78 and the standard deviation is 5.5. What raw score does he need?

5. Who is the better student, relative to his or her classmates? Use the following table for
information.
Math
Class mean
81
Class standard deviation
2
Reading
Class mean
87
Class standard deviation
10
Raw scores
Math score
Reading score
Average
Noah
85
88
86.5
Talya
87
81
84
Z–scores
Math score
Reading score
Average
Noah
Talya
From Salkind (2011). Copyright © 2012 SAGE. All Rights Reserved. Adapted with permission.
Part B Some questions in Part B require that you access data from Using SPSS for Windows and
Macintosh. This data is available on the student website under the Student Text Resources link.
The data for Exercises 6 and 7 are in the data file named Lesson 20 Exercise File 1. Answer
Exercises 6 and 7 based on the following

Lab Report On The Urinary Concentration Of Tnf-
participant in sterilized urine containers and used to determine albumin in 24 h urine specimen. The
urine levels of the biomarkers were normalized to the urinary creatinine concentration to control for
variations in hydration status. Serum and urinary TNF–α: serum levels were measured by an
enzyme–linked immune absorbent assay (ELISA) using commercially available standard kits
(Quantikine high–sensitivity human TNF–α Research Diagnostic Systems, Europe Ltd, Abington,
UK). The urinary concentration of TNF–α was determined using an enzyme–linked immunosorbent
assay (ELISA) with a Human TNF–α Quantikine ELISA kit (DTA00C; RD systems, Minneapolis,
MN, USA). The study protocol was approved by the Ethical Committee of Faculty of Medicine,
Zagazig University and informed written consent was obtained from each individual. Statistical
analyses Were performed using the Statistical Package for the Social Sciences for Windows (version
22.0; SPSS Inc., Chicago, IL, USA). Data were expressed using descriptive statistic (mean ±
standard deviation) and were analyzed. One–way analysis of variance (ANOVA) test was done to
compare different parameters between more than two groups. Pearson correlation coefficient was
used to assess the association between serum and urinary TNF–α levels, clinical, biochemical tests
and other studied metabolic parameters in patients with diabetic nephropathy. P–values were
considered significant if 0.05).On the other hand there were no significant

A Report On The Data
Based on the objectives of the experiment, it is important to describe the credit datasets, the
classi ers, the combination techniques of the classi ers, and lastly the software used in carrying
out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classi ers and their
combinations in the two class classi cation problem analysis, two real life datasets taken from the
University of California, Irvine(UCI) repository were used. These datasets are described below [28].
3.1.1 German Credit Datasets This is a nancial dataset which is made up of 1,000 instances. This
dataset records 700 cases of creditworthy applicants and 300 cases of applicants who are not credit–
worthy. It contains categorical and symbolic attributes. The German credit dataset is of two forms
[26] [29]. The original dataset german.data consists of 20 attributes of which,7 are numerical
and 13 categorical). The german.data–numeric an edited copy of the original dataset which
consist of 24 numeric attributes. The german.data–numeric which has 24 input variable basically
represents 19 attributes with 4 of these attributes changed to dummy variables. The 20 attributes in
the German.data or the 24 attributes in the german.data–numeric are some basic information
about applicants needed in creating a score card which will be used to predict if an applicant will
default or not. This information is seen in the table below. Attribute Information Attribute 1 Status
of existing

Correlation Between Various Events Variables And The Total...
The twelve research questions are divided into three distinct categories, based upon the dependent
variable, in order to facilitate a greater understanding of the results. The first category, comprising
research questions 1 through 5, investigates the correlation between various event variables and the
total amount of financial gifts pledged at the Annual Dinner. The second category, including
research questions 6 through 10, investigates the correlation between various event variables and the
amount of financial gifts pledged at the table level. Finally, the third category, which contains
research questions 11 and 12, covers those questions specifically asked by the Geneva Annual
Dinner sub–committee regarding particular guest attributes ... Show more content on
Helpwriting.net ...
According to Acton (2013), if there are too few data points, the model will have insufficient data to
perform a robust analysis and the predictions will be inaccurate and largely influenced by random
factors. Supporting this notion, other research contradicts the prediction that Annual Dinners held
later in the week will generate more money. Network for Good (2006) found that Mondays,
Tuesdays, and Wednesdays are the days when donors are most generous. This finding is supported
by Chaffey (2013), who suggests that Tuesdays are the best days for marketing causes, and
reinforced by the #GivingTuesday movement, a global day dedicated to giving back, started in
partnership between New York's 92 Street Y and the United Nations Foundation (Giving Tuesday,
2014). Therefore, despite the current research findings, other research indicates that planning events
earlier in the week will lead to greater fundraising success.
Research questions two and three found a strong positive correlation between the total amount of
financial gifts pledged and both the number of guests in attendance and the number of donors. The
number of donors likely increases as the number of guests increases, possibly due to competitive or
audience effects. Triplett (1989) found that competition was sparked not only by the co–action of
other competitors, but also by an audience effect,

The Role Of Indian Fdi On Nepalese Economic Growth
tools and approach as the main analytical methods. It includes OLS regression after testing
stationarity, co–integration and causal relationships among the variables for time series data–set.
Present study employs secondary data on the concerned variables such as Indian FDI flows,
structure of Indian FDI, Nepalese GDP, investment and export from FY 1988/89 to 2013/14. The
necessary data are taken from Economic Survey 2013/14 and Department of Industry, Nepal. The
data are transformed into real terms taking 2005/06 as the base year and data sets in real terms are
converted into logarithmic forms hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t
〖LnI〗_t and 〖LnX〗_t .
Fully Modified Ordinary Least Squares (FMOLS) is the main econometric methodology used in this
paper to examine the role and impact of Indian FDI on Nepalese economic growth. The FMOLS of
economic growth of Nepal on Indian FDI augmented with level of investment and export has been
used to find the magnitude of long run relationship between the variables under study. GDP is taken
as the proxy for Nepalese economic growth.
Some attention is necessary while employing FMOLS test. The variables under study must be
cointegrated. So before applying the FMOLS we examine the cointegration by method of Johansen's
(1990) cointegration test. Prior to employing the Johansen's Cointegration test we perform unit root
test using ADF method.
FMOLS method was designed by Phillips and Hansen (1990) to estimate the cointegrating

BSE Sensex and Foreign Institutional Investment Study
RESEARCH DESIGN
Descriptive research is undertaken for study. Data collection was done from the website of BSE
SEBI of last two years. SOUCRES OF DATA:
SECONDARY DATA – Secondary data for the study.
 Internet site www.bseindia.com www.sebi.gov.in
 Book
Business statistics – By (S.P. Gupta)
– Help of faculty guide and friends.
RESEARCH INSTRUMENT:
 Statistical tools
–Coefficient of correlation
– Covariance
– Regression Analysis (R2 )
SAMPLE SIZE
Historical data from–
BSE – (01/01/2012 to 31/12/2013 year)
SEBI– (01/01/2012 to 31/12/2013 year)
CHAPTER– 4 DATAANALYSIS
DATE CLOSE FIIs Investment
31/01/12 17193.55 10357.7
29/02/12 17752.68 25212.1
30/03/12 17404.2 8381.1
30/04/12 17318.81 –1109.1
31/05/12 16218.53 –347.1
29/06/12 17429.98 –501.3
31/07/12 17236.18 10272.7
31/08/12 17429.56 10803.9
28/09/12 18762.74 19261.5
31/10/12 18505.38 11364
30/11/12 19339.9 9577.2
31/12/12 19426.71 24463.5
31/01/13 19894.98 22,059

28/02/13 18861.54 24,439
28/03/13 18835.77 9,124
30/04/13 19504.18 5,414
31/05/13 19760.3 22,169
28/06/13 19395.81 –11,027
31/07/13 19345.7 –6,086
30/08/13 18619.72 –5,923
30/09/13 19379.77 13,058
31/10/13 21164.52 15,706
29/11/13 20791.93 8116
31/12/13 21170.68 16,086
Coefficient of correlation– Karl Pearson's coefficient of correlation
Formula:–
The analysis of above data –
Coefficient of correlation 0.255260872

Health Outcomes Of The World Health
More than a century ago, the first health outcome measure was proposed by Florence Nightingale by
classifying patients into relieved, unrelieved and dead (1). Other guides such as mortality rates have
historically been used to measure health outcomes at a population level (2). However, the definition
of health has changed in the past century to include a wider view of outcomes which includes
freedom from disease, ability to perform daily activities, happiness, social and emotional well–
being, and quality of life. The World Health Organization (WHO) has defined health as physical,
mental, and social well–being, and not merely the absence of disease and infirmity (3). As a result
numerous measures have been developed in an attempt to quantify health. Health outcome measures
are tools used to evaluate an individuals' health using different health related parameters. Patient
Reported Outcomes Measures (PROMs) are instruments that are completed by patients and capture
one or more aspects of health (4, 5). The use of PROMs formally to monitor surgical outcomes in
England has been an important development (6). Since 2007, the Department of Health has required
the routine measurement of patient– reported health outcomes for all NHS patients via its PROMs
programme (5, 6). PROMs are increasingly used in decision–making to encourage a patient–
centered approach (5, 7). For this reason, PROMs must be valid, reliable, and clinically useful
measures. There are over 100 PROMs applicable

Article Review : Deep Correspondence Restricted Boltzmann...
Article Review : Deep correspondence restricted Boltzmann machine for cross–modal retrieval
Review Submission : ACN 5314.5H1 – Computational Modeling Methods in Behavioral Brain
Sci. Reviewer : Jithin Pradeep R jxp161430@utdallas.edu School of Behavioral and Brain Science,
The University of Texas at Dallas December 16, 2016. Deep correspondence restricted Boltzmann
machine for cross–modal retrieval: Jithin Pradeep Article Review. Article Review : Deep
correspondence restricted Boltzmann machine for cross–modal retrieval Abstract of article Cross–
modal retrieval task tries to exploit the correlation between the component using a canonical cor–
relational analysis. In simple word, cross model retrieval would involve retrieving an image using a
text input or image to generate a corresponding narration. In world where internet user throws up
bunch of multimodal content make it important to analysis the same. Modeling the correlations
between di erent modalities is the key to tackle cross model retrieval problem. In the paper,author
propose a correspondence restricted Boltzmann machine(Corr–RBM) to map the original features of
bi–modal data, such as image and text, into a low–dimensional common space by deploying two
deep neural structures using Corr–RBM as the main building block for the task of cross–modal
retrieval. The heterogeneous data are made comparable by optimizing a single objective function
(constructed to trade o the correlation loss and likelihoods of both

Spearman's Rank Correlation Essay
Spearman's Rank Correlation Coefficient Review Spearman's rank correlation coefficient is a
mathematic and statistics tool used to measure correlation or in other words, there is a number that
reveals how closely related, two sets of different data can be associated and how closely related they
are. This can only be used with information that can be put in rank order from highest to lowest. The
Spearman's rank correlation coefficient is also used to determine whether the two variables are
associated or related in any monotonic way, in which a variable will increase and the other variable
will also increase or it will decrease. As a result of this, the measurement will be converted to ranks
therefore researchers will use the Spearman's rank ... Show more content on Helpwriting.net ...
As part of this research Messina et al. used the Spearman rank correlation coefficient to help
determine any relationship between the mean score of patient satisfaction (independent variable)
and the admissions volume (dependent variable).The patients were given surveys/questionnaires to
be filled out which they graded on a ranking scale of their own satisfaction on the hospital type they
received their treatment. The results of this research using the Spearman rank correlation coefficient,
has shown that there is a significant but negative correlation between the variables in the combined
sample between admissions and patient satisfaction. The research performed by Messina et al.
(2009), definitely answers the research question if there is any relationship between patient
satisfaction and inpatient admissions across teaching and nonteaching hospitals. When one looks at
the Spearman's rank correlation coefficient results by itself you will see that there is statistically a
significant and positive correlation between the patient satisfaction and the admission volumes in
teaching hospitals (Messina et al.,

Factors Influencing The Adoption Behavior Of Coffee Producers
Selected Explanatory Variables
A micro approach was used to analyze which factors influence the adoption behavior of coffee
producers with regard to SMPs. We considered farm and management characteristics, the
socioeconomic profile of producers as well social capital indicators. Table 1 presents the descriptive
statistics of the data collected.
Several factors that were considered are accepted as common predictors of agricultural adoption in
developing countries. However, three questions related to explanatory variables were not included
in the final questionnaire. Firstly, we did not consider access to bank loans because coffee farmers in
Mexico are mostly unable to obtain investment credit from lending institutions. Coffee producers
usually have the option of obtaining short term financing (cash advances) either from local traders
or from coffee cooperatives for those who are members. However, the availability of long term
credit has virtually disappeared making it difficult for producers to defer the costs of implementing
over several years (AMECAFE, 2009).
Secondly, land tenure arrangements were not factored into the model since the communal land
system in Mexico (ejidos) remains the dominant type of ownership especially in the rural areas of
the south (Brown, 2004). Households in the sampled area continue to work the land under this
traditional system and therefore the inclusion of a variable to capture tenure security could amount
to model misspecification.
Thirdly,

SIG Interview Questions Essay
SIG Interview Questions 1. Torpedo question: 2 torpedoes, each with 1/3 probability of hitting/
sinking a ship 2. I have 20% chance to have cavity gene. If I do have the gene, there is 51% chance
that I will have at least one cavity over 1 year. If I don't have the gene, there is 19% chance that I
will have at least one cavity over 1 year. Given that I have a cavity in 6 months, what's the
probability that I have at least a cavity over 1 year? 3. What is the probability of 5 people with
different ages siting in ascending or descending order at a round table? 4. We are racing, and can at
any time signal to the other that we would like to double our bet. We've put down 100 to start and
during the race I signal to you I want to double ... Show more content on Helpwriting.net ...
Also, cards worth less than 5 are now valued at $0. What is the maximum price you are now willing
to pay for the game? 19. Marble weighing questions 20. How many ways can 5 different people sit
down at a round table? How many ways can 5 different people sit down in a line of chairs? What if
they were seated at a long table with exactly five seats (the 5 people will sit side by side)? 21. I want
to have a birthday party outside this weekend (i.e. need sunny weather). On Saturday, the chance of
rain is 60 percent, sun 40 percent. On Sunday, rain is 80 percent, sun is 20 percent. From there, the
interviewer can ask an array of questions: What are the chances I can have my party this weekend?
If I have my party, what are the chances my party is on Saturday/Sunday? Variation: You can hold a
party only if there is no rain on either saturday or sunday. The probability of rain on a saturday is
80% and 60% for sunday. What is the probablity you can hold a party at the weekend? Given that
you held a party, what is the probability that that it rained at the weekend? 22. Two blind men are
carrying 3 pairs of red rocks and 3 pairs of white socks. They hit each other in the mall and have to
redistribute the socks so that they receive the same amount of each color they had before. They can't
receive any outside help. How do they do it? 23. If i toss 3 pieces on a tic–tac–toe board at random,
and I pay you $9 if

Georgia 's Craft Beer Industry
Problem Statement From the end of prohibition in 1933 to the turn of the new millennium, Georgia's
craft beer industry had seen more failure than success. Over a 67–year span, 9 of the 14
microbreweries to operate in the state failed. Was it the cost and availability of big beer and product
loyalty that caused this to happen?
But in 2013, Georgia's craft beer industry took a drastic turn. Microbreweries were opening at an
exponential rate. That year, Creative Loafing, an alternative news weekly, reported eight
microbreweries opening in the city of Atlanta, alone. Georgia had entered into a craft beer boom.
Today, there are 31 microbreweries operating in Georgia, and 24 more in planning, bringing the
total to 55 of these small–production breweries. What could have changed to cause an historically
failed industry to boom in such a short period of time?
Millennials.
According to U.S. Census data, Georgia's population of Millennials, defined as those born between
1980 and 2000, significantly increased between 2010 and 2014, the generation growing from 15.3%
of the population in 2010 to 26.61% of the population in 2014. (Pew Research, U.S. Census).
Earlier this year, Forbes reported that the group with the most purchasing power in the United States
had shifted from the Baby Boomers to the Millennials. It is estimated that Millennials in the U.S.
alone will spend around $200 billion annually by 2017.
The positive relationship between the population of Millennials in

Common Predictors Of Agricultural Adoption
Several factors that were considered are accepted as common predictors of agricultural adoption in
developing countries. However, three questions related to explanatory variables were not included
in the final questionnaire. Firstly, we did not consider access to bank loans because coffee farmers in
Mexico are mostly unable to obtain investment credit from lending institutions. Coffee producers
usually have the option of obtaining short term financing (cash advances) either from local traders
or from coffee cooperatives for those who are members. However, the availability of long term
credit has virtually disappeared making it difficult for producers to defer the costs of implementing
over several years (AMECAFE, 2009).
Secondly, land tenure arrangements were not factored into the model since the communal land
system in Mexico (ejidos) remains the dominant type of ownership especially in the rural areas of
the south (Brown, 2004). Households in the sampled area continue to work the land under this
traditional system and therefore the inclusion of a variable to capture tenure security could amount
to model misspecification.
Thirdly, analysis of the data indicated that all surveyed producers affiliated with a farmer association
had received extension support through their membership whereas no independent producer had
participated in any form of training . In addition, all of those surveyed who had indicated they were
members of a producer organization belonged to organic

ABB Case Study
Evolution of Organizational structure in ABB
1988: Matrix Organization
1990: Decentralization with 1500 legally independent units and 5000 profit centers.
1993: Matrix Organization according to product and region – Continental organization
1998: Product oriented sector based organization
2001: Customer oriented sector based organization
In 1993, the Group was organized on the one hand on a product–oriented basis into four corporate
divisions with a total of some 50 business areas and on the other hand on an area–oriented basis into
three geographic regions with 1,300 legally independent companies and a total of some 5,000
autonomous local units. The local units are set up as so–called profit centers with their own balance
sheets and profit and loss accounting. ... Show more content on Helpwriting.net ...
The weaknesses of the matrix system were found in the areas of authority and chain of command
that led to ambiguity and increased costs. Likewise, ABB encountered problems in its matrix
structure and had difficulty materializing its goals at the global level
Major restructuring and changes in its top management corporate retrenchment; significant
corporate changes that led to unloading the company's financial services and oil and gas divisions.
The company's performance turned into losses. Cancellations of projects that led to heavy losses and
downsizing were the end result. ABB could not recover from the East Asian crisis and saw its
revenues dry up in the region that led to a total loss of over $1 billion in 2001/2002.
CEO salaries and their exorbitant pension benefits have been severely criticized by the media. This
was major negative publicity for the company when it lost over $500 million during the same
period.
ABB allocated $1.2 billion to deal with these claims. In 2003, in the U.S. alone, 90.000 new
asbestos claims were filed raising the corporate liability to U.S. plaintiffs about $200

Unfolding Test Essay
chapter{Unfolding Tests}label{ch:UnfTest}
In order to measure the performance of the D'Agostini 2010 unfolding algorithm, and to compare it
with other algorithms, we have developed an unfolding test suite.
The main inputs of these tests are the MC microtree file and the algorithm to be tested. Where, The
microtree is a ROOT~cite{ROOT} file generated by our main analysis software,
HighLAND~cite{HighLAND}, containing all the necessary information about the events that
entered the selection process.
First, knowing the signal definition, and the binning for the truth and reconstructed variable
distribution, we process the microtree file to build the truth and reconstructed signal histograms, as
well as the smearing matrix.
Then, we ... Show more content on Helpwriting.net ...
Cref{fig:UnfTest_CoverageBins} shows the bin–by–bin coverage at 95% CL, of the unfolded
{color{red} four–bins} momentum distribution of the electron antineutrino.
subsection{Simultaneous Coverage}
We rely on the empirical simultaneous coverage with Bonferroni correction, cf.~cite[Section 5.2.2]
{KuuselaShapeConstraint}, for the whole histogram.
The Bonferroni correction accounts for the increase of type I errorfootnote{Type I error is rejecting
the hypothesis while it is true.} of multiple coverage tests, one per bin, by decreasing the
significance level of each test to be $frac{alpha}{n}$, where $alpha$ is the type I error level per
bin and n is the total number of bins in the histogram.
Consequently, the confidence interval for each bin is then given by
begin{equation}
text{confidence interval} = big[hat{lambda}_k – z_{1–frac{alpha}{2n}} sqrt{text{Var}
(hat{lambda}_k)}, ; hat{lambda}_k + z_{1–frac{alpha}{2n}} sqrt{text{Var}
(hat{lambda}_k)} big],
end{equation}
where, $hat{lambda}_k$ is the expected value of the unfolded histogram for bin k, ${Var}
(hat{lambda}_k)$ is its variance and $z_{1–frac{alpha}{2n}} $ is the standard normal quantile.
The simultaneous coverage test passes, if the coverage test for all the bins passes at the modified
significance level. The result of the simultaneous coverage test is shown in

The Appropriateness Of A Mean Variance Simulation Model...
The appropriateness of a Mean–Variance Simulation Model for a
Commonwealth public sector defined pension benefit fund
Kevin Lian
October 20, 2014
In this section, we will examine the use of a mean–variance simulation model on a commonwealth
public section defined pension benefit fund. Firstly, we will examine the expected medium term
returns using the reverse optimisation approach and discuss whether any adjustments are necessary.
Secondly, we will examine how appropriate the model is for the fund. Lastly, we will discuss how to
determine any inconsistencies if a mean–variance model is used.
Expectations for medium term returns
The market portfolio consisted of 8 asset classes 1 , in which we used an adjusted allocation size
based on APRA ... Show more content on Helpwriting.net ...
This is emphasised by the near singular correlation matrices
2 . Furthermore, the results are extremely sensitive to these mean and variance assumptions due to
the structure of the M–V model. This exposes our results to a potentially unbounded error size.
Thus, we suggest a Bayesian adjustment is needed to reflect our uncertainty about our assumptions.
1 AE – Australian Equity, IE – International Equity, LP – Listed property, UP – Unlisted Property,
AFI – Australian
Fixed Interest, IFI – International fixed Interest (Hedged), Cash – $AUD
2 This can be seen by determinants of the 5, 6, and 7 year correlation matrices, which were
2.31258E–48, –9.99469E–
34, and –1.72439E–20 respectively
1
Group B Part D ACTL4303 Assignment Student ID: 3374652
Furthermore, the results in Table 1 also requires some adjustments due to tax and the inherent
assumptions of the reverse optimisation approach. In general, the returns of each asset class need to
be subjected to the appropriate tax deductions and transaction fees. In particular, Australian equity
returns have not accounted for the franking credits. In addition, the method used to calculate the
risk–aversion coefficient result in time–invariant equity returns. Thus, adjustments are needed to
reflect time–vary equity risk premiums. In

Happiness Is A Personal And Multi Dimensional Notion
Happiness is usually expressed as the point to which an individual assesses the total worth of his or
her existence as a whole positively (Veenhoven, 1993), and regularly, considered on an ordinal
scale of answers to biased questions. Most experimental findings consume various kinds of self–
reported ideals on happiness or particular well–being dignified from various scaling methods.
Happiness is a personal and multi–dimensional notion – every distinct person depicts it in a diverse
way, it is comparative, and it is determine by an extensive range of influences, involving individual
personalities. Consequently, allowing it to be assessable and creating causal relationships a task. The
information and experimental studies distinguish between the natures of happiness and noticing the
connected expressions – life's satisfaction and well–being. The explanations of recognised ideas
overlap through the proposal on happiness and are separated into numerous categories. In this study,
the main ideas associated to happiness and their factors are shortened in order to pinpoint the major
distinctions between the suggested terms and to determine the theoretical agenda. The pursuit for
happiness is significant and is a must to human beings. It is a situation that majority of people
attempt to accomplish. Many of us can account, at least at some phase of our existent, being happy.
We all strive to live happy and fulfil our lives and want happiness for people that we love. So
happiness

Factors Influencing The Adoption Behavior Of Coffee Producers
Selected explanatory variables
A micro–level approach was used in order to test which factors influence the adoption behavior of
coffee producers with regard to SMPs. The model incorporates farm and management
characteristics, the socioeconomic profile of producers and social capital indicators. Table 1 presents
the descriptive statistics of the data collected.
Several factors that were considered are accepted as common predictors of adoption in developing
countries. However, three questions related to explanatory variables were not included in the final
questionnaire. Firstly, we did not take into account access to bank loans since coffee farmers in
Mexico are mostly unable to obtain investment credit from lending institutions. Producers usually
have the option of obtaining short term financing (cash advances) either from local traders or from
coffee cooperatives for those who are members. However, the availability of long term credit has
virtually disappeared making it difficult for producers to defer the costs of implementing new
practices over several years (AMECAFE, 2009).
Secondly, tenure arrangements were not considered since households in the sampled area are
governed by a communal land resources committee, an institution independent of the municipality.
Under this system, producers hold usufruct rights to their coffee parcels. While a 1991 amendment
to the Mexican constitution made the privatization of communal lands (ejidos) possible it remains
the dominant type

Essay on The Matrix
The Matrix Interweaves much symbolism, mythology, philosophy, and psychology. On the surface,
the movie challenges the dominance of technology in our culture and predicts an apocalyptic result
from the use of artificial intelligence. Yet, behind the human struggle for survival is a mythical
backdrop upon which are backlit some of C.G. Jung's basic ideas regarding the human psyche.
These Jungian ideas include the ego–Self relationship and how it relates to the persona, the shadow,
individuation, and the transcendent function.
The earth has been decimated due to a battle for control of the earth between the AI's and humans;
the Matrix camouflages this decimation. Humans are artificially created and sustained by the AI
superstructure. Then ... Show more content on Helpwriting.net ...
The reference to Jesus Christ in this scene implies that Neo represents the human psyche beginning
the individuation process. Neo meets a woman named Trinity at the party. Trinity tells Neo that she
is aware of his desire to know what the Matrix is. It's the question that brought you here. What is
the Matrix? (The Matrix). Trinity is the one who will lead him towards the underworld. Jung called
the her a soul figure, one that occupies an area or boundary between the personal unconscious and
the collective unconscious. Trinity is also associated with a person's calling or fate.
Trinity, as a three figure, is incomplete. Jung writes, the number three is not a natural expression
of wholeness, since four represents the minimum number of determinants in a whole judgment
(Storr 275). Jung believed that the number four represented wholeness in the human psyche. If one
were to re–arrange the name Neo slightly, it would be the word one The separateness of the
numbers one and the three in the movie, of Neo and Trinity, represent an incompleteness in the
human psyche. As we shall see, the joining of Neo and Trinity is what effects the change necessary
for Neo to overcome his adversaries at the end of the film.
Early resistance of the ego characters occurs after Neo's initial recruitment by Morpheus' group. Neo
has a meeting with his manager after arriving late to work. His manager says, You have a problem
with

Integration Of Proxy Model With Enkf Algorithms For Time...
Methodology
This section contains the mathematical background and stepwise procedure for EnKF algorithm, KL
expansion and ANN based proxy model. Integration of proxy model with EnKF algorithms for time
efficient assisted history matching is also explained in this section. Ensemble Kalman Filter
Algorithm
EnKF is a Monte Carlo data assimilation method, which has gained popularity over recent years for
assisted history matching due to its ability to include available observations sequentially in time.
Aanonsen et al. (2009) reviewed the application of EnKF in reservoir engineering for estimation of
reservoir parameters. EnKF procedure utilizes an ensemble of model states (e.g. realizations of
reservoir properties such as porosity and permeability) to estimate the covariance matrices used in
model updating process. Initial ensemble is generated based on the prior knowledge of the reservoir
derived from various sources as well logs, core and seismic analysis. In general, simulation
techniques such as Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS)
are used to generate multiple realizations. These realizations are consistent with the initial state of
the reservoir. In the next step, all the reservoir models in ensemble are forwarded typically using the
numerical reservoir simulation. Mean and covariance of predicted model states is calculated and
used in turn to calculate Kalman gain. Next, in the update (or analysis) step, each geological

Analysis of Auto Parts Industry/ Usa /Deviations, Betas...
HOME ASSIGNMENT Analysis of Auto Parts Industry/ USA /deviations, betas and correlations/
Module: Financial Markets Module leader: Prof. György Komáromi Written by László Földvári
Industry Analysis I have choosen five companies from the Capital Goods sector / Autoparts
Industry/ Nasdaq. The industry analysis is the essence and first step of getting a clearer view of the
market players. We have to know the most important macroeconomical circumstances of industry as
well. In the following pages I am going to analyze the industry, the choosen stocks and the brother
industries. I will try to find explanations for the changes caused by the financial crisis with the help
of calculated sigmas, betas and pair–wise ... Show more content on Helpwriting.net ...
Howevery the stock price is still the half ( 7,5$ ) as it was. Williams Controls Inc. WMCO: Nasdaq;
Cyclical Consumer Goods Services/Auto / Truck / Motorcycle Parts Williams Controls, Inc.
designs, manufactures and sells electronic throttle controls and pneumatic controls, and the
Company has begun selling electronic sensors for heavy trucks, transit buses and off–road
equipment. The Company also produces a line of pneumatic control products, which are sold to the
same customer base as its electronic throttle controls. These pneumatic products are used for vehicle
control system applications, such as power take–off 's and air–control applications. The Company
also sells sensors as separate product line, which includes two sensor technologies: contacting and
non–contacting. The Company sells the majority of its products directly to heavy truck, transit bus
and off–road original equipment manufacturers worldwide. Its customers include The Volvo Group,
Paccar, Inc., Daimler Trucks NA, Navistar International Corporation, Caterpillar, Inc. and Hyundai
Motor, Co. [pic] WMCO stock prices shows fall in the early 2008. As I have mentioned before, this
was the beginning of the crisis of American Automobile industry. The stock prices were declining
dramatically after 15 Sept 2008. For them the worst time

Maths 1a Imp Qstns
WWW.PAPERSHUNT.COM MATHS – FIRST YEAR 1 A VERY IMPORTANT QUESTIONS BY
WWW.PAPERSHUNT.COM AND HUNT FOR SUCCESS PUBLICATIONS. LONG ANSWER
QUESTIONS Functions : 01. Let f : A  B , g : B  C be bijections. Then gof : A  C is a
bijection. 02. Let f : A  B , g : B  C be bijection. Then ( gof ) 1  f 1og 1 03. Let f : A  B
, I A and I B be identify functions on A and B respectively. Then foI A  f  I B of 04. Let f : A  B
be a bijection. Then fof 05. Let f : A  B be a function. Then f is a bijection if and only if there
exists a function g : B  A such that fog  I B and gof  I A and, in this case, g  f 1
Mathematical Inductions : 06. Show that 49n  16n  1 is divisible by 64 for all positive integers n.
n(n 2  6n  11) ... Show more content on Helpwriting.net ...
The angle of elevation of the top of a tower from the foot of the building is twice the angle of its
elevation from the top of the building. The height of the building is 50 meters and the height of the
tower is 75 meters. Find the angle of elevation of the top of the tower from the foot of the building.
29. From a point on the slope of a hill, the angle of elevation of the top of the hill is 45°. After
walking 200 meters from that point on a slope which makes 15° angle with the horizontal, the same
point on the top of the hill, makes an angle of elevation of 60°. Show that the height of the hill is
100 HUNT FOR SUCCESS  6  2 meters  30. A pillar is leaning towards East and  and  are
the angles of elevation of the top of pillar from two points due West of the pillar at distance a and b
respectively. Show that the angle between the pillar and the horizontal is Tan  1   ba   b
cot   a cot   Demoviers Theorem : 2n 2n n 1 31. If n is an integer then show that (1  i) 
(1  i )  2 cos n 2 32. If cos   cos   cos   0  sin   sin   sin  then show that
(i) cos 3  cos 3  cos 3  3cos(     ) (ii) sin 3  sin 3  sin 3  3sin(  
  ) (iii) cos(   )  cos(    )  cos(   )  0 33. Find all the roots of the equation
x11  x 7  x 4  1  0 34. If n is a positive integer, show that :3: mail us @
huntforsuccess@gmail.com , cell: 798 3699 456 , Our books are available in selected book shops in
andhra

Scalable Graph Based And Ranking Computation Web Image Search
SCALABLE GRAPH BASED AND RANKING COMPUTATION WEB IMAGE SEARCH
A.Jainabee#1,R.Shobanadevi#1,K.Suganya#1.S.Indhumathi#2.
Computer Science and Engineering.
Bharathiyar Institute of Engineering for women.Deiyakurichi.
Jainabspm93@gmail.com
Assistant professor of Information technology.
Indhubtech11@gmail.com
Abstract–Graph–based grade model have been at length functional in in order repossession area. In
this paper, we heart on a well recognized graph–based model – the place on statistics diverse
representation, or assorted position (MR). Particularly, it has been productively applied to content–
based image retrieval, because of its outstanding ability to discover causal geometrical structure of
the given image database. However, a range of ranking is computationally very unpleasant, which
appreciably restrictions its applicability to large Databases above all for the cases that the query are
away from home of the folder (new sample) We proposition a book scalable graph–based grade
model called proficient Manifold Ranking (EMR), trying to address the shortcoming of MR from
two main perspective: scalable graph construction and efficient ranking computation. Specifically,
we build an fix graph on the database instead of a traditional k–nearest fellow inhabitant graph, and
design a new form of adjacency medium utilize to speed up the ranking. An likely method is
adopted for well–organized out–of–sample rescue. untried outcome on a quantity of great scale

The Differences Between The And The Method Of The...
4.5 EBLUP
In our model (1.1), Z_i b ̂_i reflects the difference between the predicted responses in the i–th
subjects and the population average. Thus, b ̂ verse subject indices can be used for identifying the
outlying subjects.
To assess the sensitiveness of subjects to the homogeneity of the covariance matrices of the random
effects, Nobre and Singer develop the method of influence methods from Cook (1986). The idea is
to put some weights to the var(b), i.e. var(b) = WG and then calculate |dmax|, which is the
normalized eigenvector associated with the direction of largest normal curvature of the influence
graph under a perturbation of the covariance matrix of the random effects (for detail, see appendix
or Cook (1986)).
First, we need ... Show more content on Helpwriting.net ...
θ_j ) V^(–1) (y–Xβ)
+(y–Xβ)^T V^(–1) (∂^2 V)/(∂θ_k ∂θ_j ) V^(–1) (y–Xβ)
–(y–Xβ)^T V^(–1) ∂V/(∂θ_j ) V^(–1) ∂V/(∂θ_k ) V^(–1) (y–Xβ)
+tr(V^(–1) ∂V/(∂θ_k ) V^(–1) ∂V/(∂θ_j ))–tr(V^(–1) (∂^2 V)/(∂θ_k ∂θ_j ))} = 1/2{–(y–Xβ)^T V^(–
1) ∂V/(∂θ_k ) V^(–1) ∂V/(∂θ_j ) V^(–1) (y–Xβ)
–(y–Xβ)^T V^(–1) ∂V/(∂θ_j ) V^(–1) ∂V/(∂θ_k ) V^(–1) (y–Xβ)
+tr(V^(–1) ∂V/(∂θ_k ) V^(–1) ∂V/(∂θ_j ))} as (∂^2 V)/(∂θ_k ∂θ_j ) = 0 k, j = 1, ..., q ∂V/(∂θ_i ) =
[■(ZWGZ^Tθ_i=〖σ^2〗_subject@Iθ_i=σ^2 )]
The next step is to find the second derivative of l(θ|W) with respect to w and θ evaluated at
evaluated at θ = θ ̂ and w = w0: (∂^2 l(θ|w))/(∂w_j ∂θ_i ) = 1/2{–(y–Xβ)^T 〖V_w〗^(–1)
(∂V_w)/(∂w_j ) 〖V_w〗^(–1) (∂V_w)/(∂θ_i ) V^(–1) (y–Xβ)
+(y–Xβ)^T 〖V_w〗^(–1) (∂V_w)/(∂w_j ∂θ_i ) 〖V_w〗^(–1) (y–Xβ)
–(y–Xβ)^T 〖V_w〗^(–1) (∂V_w)/(∂θ_i ) 〖V_w〗^(–1) (∂V_w)/(∂w_j ) 〖V_w〗^(–1) (y–Xβ)
+tr(〖V_w〗^(–1) (∂V_w)/(∂w_j ) 〖V_w〗^(–1) (∂V_w)/(∂θ_i ))–tr(〖V_w〗^(–1)
(∂V_w)/(∂w_j ∂θ_i ))} evaluated at θ = θ ̂ and w = w0 = I, i = 1, ..., q, j = 1, ..., q (∂V_w)/(∂θ_i ) =
[■(ZWGZ^Tθ_i=〖σ^2〗_subject@Iθ_i=σ^2 )] (∂V_w)/(∂w_j ∂θ_i ) = ∂/(∂w_j )
[■(ZWGZ^T〖σ^2〗_subject@Iσ^2 )] = [■(Z ∂W/∂w GZ^T0@00)] (∂V_w)/(∂w_j ) =
[■(0...0@⋮1_(j,j)⋮@0...0)]
Finally, we can calculate F ̈_i and get the largest absolute eigenvalue, |dmax| for every subject i: F ̈_i
= (∂V_w)/(∂w_j ∂θ_i )*(∂^2 l(θ))/(∂θ_k ∂θ_j )*(∂V_w)/(∂w_j ∂θ_i ) |dmax| = The largest absolute
eigenvalue of F ̈_i for i–th subject
Plotting |dmax| verse

Investigating Correlation Coefficient Of Correlation

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