Smoothing Dat The Estition Of Variance In Data

Smoothing Dat The Estition Of Variance In Data
ESTIMATION OF VARIANCE IN HETEROSCEDASTIC DATA
Abstract
Data which exhibit none constant variance is considered. Smoothing procedures are applied to
estimate these none constant variances. In these smoothing methods the problem is to establish how
much to smooth. The choice of the smoother and the choice of the bandwidth are explored. Kernel
and Spline smoothers are compared using simulated data as well as real data. Although the two seem
to work very closely, Kernel smoother comes out to be slightly better.
KEY WORDS: Smoothing, Kernel, Spline, Heteroscedastic, Bandwidth, Variance. 1. Introduction
Let us have the observations .The mean is given by , . The deviations of each observation away ...
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Given the errors are independently distributed. However if any of these assumptions are violated the
estimates obtained under the classical or usual assumption are not good. Therefore we hope to
obtain better estimates of when the estimation of the variance is incorporated.
Therefore, we need to investigate and incorporate the information about the variance estimates of
the errors which are needed for better understanding of the variability of the data. In heteroscedastic
regression models the variance is not constant. Often as in the case with the mean, the
heteroscedasity is believed to be in functional form which is referred to as variance function. We try
to understand the structure of the variances as a function of the predictors such as time, height, age
and so on. Two procedures of estimating the variance function includes the parametric and
nonparametric methods. The parametric variance function estimation may be defined as a type of
regression problem in which we see variance as a function of estimable quantities. Thus, the
heteroscedasticity is modeled as a function of the regression and other structural parameters. This
function is completely known, specified up to these unknown parameters. Estimation of these
parameters is what entails parametric methods.
However, for many practical problems the degree to which components of the statistical model can
be specified in a parametric form varies
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Study of Profitability of a Logistics Company
STUDY OF PROFITABILITY OF A LOGISTICS COMPANY USING ECONOMETRICS TOOLS
Executive summary
This study examines the impact of three factors, namely Sales, Fixed assets and Interest paid on the
profitability of a logistics company. Econometric tool of multiple linear regression model was used
for analyzing the impact of above factors on profitability of a major logistics company GATI
Limited. Based on the financial data of last 10 years 2000–2009 the regression analysis has revealed
that profitability of GATI ltd. is significantly affected positively by increase in fixed assets and
adversely affected by increase in interest paid. The impact of increase in sales volume on
profitability is positive but miniscule. In addition seasonality ... Show more content on
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Moreover, projects such as the Golden Quadrilateral program, National Maritime program, and
introduction of freight corridors in rails shall strengthen the growth. The current economic scenario,
which is largely impacted by the global slowdown however is expected to recover soon with the fall
in logistics costs due to the huge developments in infrastructure. With the rise in disposable
incomes, changing consumer preferences, fast emerging retail segments, infrastructure investment,
the Indian logistics sector and the 3PL markets are expected to witness explosive growth in the
successive years.
Constraints and Challenges
The biggest challenge faced by the organised logistics companies in India today, is competition from
the unorganized operators. This is coupled with increasing environmental pressures, government
regulations and subsidies for infrastructure development. Adding to all these, is the lack of "Industry
status" to the logistics sector.
Business Risks and Mitigation
The Year 2008–2009 for India was quite different from the expectations of industry stalwarts and
economy speculators. Increase in input materials costs, wages, interest and transportation forced
companies to cut costs. Logistic service providers therefore are required to be cost effective and
more efficient to compete in market place. High logistics and warehousing costs in India shows that
there are

Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) also known as Remotely Piloted Vehicle (RPV) which can
operate without human operator, plays important role in both civilian and military purposes. [3]
[8]Weather reconnaissance, search and rescue assisting operations in sea and mountains, aerial
photographing and mapping, fire detection and traffic control are examples of usage fields. [1] Path
Planning, crucial phase for navigation process, is about to determining optimal or near optimal path
between destinations with fulfilling constraints such as hostile threats. [2] Depending on the degree
of difficulty of the task, operations such as path planning should be fulfilled without human support.
[1] The autonomy of such use is advantageous in situations that require quick decisions. And
success of path planning heavily relies on selected algorithm. [4] Measure of success can be
categorized into two sections: feasibility which considers to safely moving objects to target and
optimality which is about to finding optimal solution. When a task difficult to accomplish by a
single UAV or it is cheaper, easier or faster than there may be a need would arise to use multiple
UAVs to fulfill task [8]. To solve some problems multiple UAVs path planning may be came into as
a constraint. [8] Search and rescue operations are examples of multiple UAVs usage areas. There are
different types of path planning architectures. It can be centralized or distributed. In centralized
systems; a universal path planer

Basic Idea Of Smoothing : Basic Concepts Of Setting
2. Basic idea of smoothing
If is believed to be smooth, then the observation at , near should contain information about the value
of at . Thus it should be possible to use something like local average of data near to construct an
estimator of . Smoothing of a data set , involves the approximation of the mean response curve in
the regression relationship. The function of interest could be the regression curve itself, certain
derivatives of it or functions of derivatives such as extrema or inflection points.
In the trivial case in which is a constant, estimation of reduces to the point of location, since an
average over the response variables yields an estimate of . In practical studies though it is unlikely
that the ... Show more content on Helpwriting.net ...
This smoothing parameter regulates the size of the neighborhood around meaning a local average
over too large a neighborhood would cast away the good with the bad. In this situation an extremely
over smooth curve would be produced, resulting in a biased estimate . On the other hand defining
the smoothing parameter so that it corresponds to a very small neighborhood would not shift the
chaff from the wheat .Only a small number of observations would contribute none negligibly to the
estimate at making it very rough and wiggly. In this case the variability of would be inflated.
Finding the choice of smoothing parameter that balances the trade – off between over smoothing and
under smoothing is called the smoothing parameter selection problem.
3. Choosing the smoother
Some of the smoothing techniques include Kernel, Spline, Locally weighted regression, Recursive
Regressogram, Convolution, Median, Split linear fit and K–Nearest Neighbor among others. One of
the most active research areas in Statistics in the last 20 years has been the search for a method to
find the "optimal" bandwidth for a smoother. There are now a great number of methods to do this.
Unfortunately none of them is fully satisfactory. Here a comparative study of the two mostly used
and easy to implement smoothers is presented. The Kernel and the cubic spline smoothers .The
comparison is preformed on a simulated data set. Looking at

Using Adaptive Response Surface Regression
3 Methodology
The developed optimisation routine makes use of adaptive response surface regression to use a
limited initial amount of FE models to feed an optimisation routine which is specifically designed
for general thermal problems where parameters linked to the general heat equation can be optimised
or estimated using experimental input data. The algorithm uses a pan and zoom function to move
through the design space and delivers faster predictions with fewer iterations than standard updating
routines [35, 41].
3.1 Adaptive response surface method
The adaptive response surface optimisation routine is used to optimise numerical models with a lot
of data points and the time reducing by the algorithm increases as the number of parameters rises
[40]. The routine is designed to handle multiple–output time series data [35]. The optimisation
procedure can be divided into following steps:
1. Starting reference simulation points are ran and a correct object function is built of the difference
between the FE model and the target value (experiment or validation model).
2. The FE model is replaced by a meta–model of response surfaces to decrease the optimisation time
but remains an accurate approximation.
3. The optimisation routine is run on a specific object function. It is possible to use multiple
objective functions or build an objective function related to multiple outputs.
4. The estimated parameter values are used as input parameters for a new FE model that corrects the

Using Adaptive Response Surface Regression
3 Methodology
The developed optimisation routine makes use of adaptive response surface regression to use a
limited initial amount of FE models to feed an optimisation routine which is specifically designed
for general thermal problems where parameters linked to the general heat equation can be optimised
or estimated using experimental input data. The algorithm uses a pan and zoom function to move
through the design space and delivers faster predictions with fewer iterations than standard updating
routines [35, 41].
3.1 Adaptive response surface method
The adaptive response surface optimisation routine is used to optimise numerical models with lots of
data points and the time reducing by the algorithm increases as the number of parameters rises [40].
The routine is designed to handle multiple–output time series data [35]. The optimisation procedure
can be divided into the following steps:
1. Starting reference simulation points is running and a correct object function is built of the
difference between the FE model and the target value (experiment or validation model).
2. The FE model is replaced by a meta–model of response surfaces to decrease the optimisation time
but remains an accurate approximation.
3. The optimisation routine is run on a specific object function. It is possible to use multiple
objective functions or build an objective function related to multiple outputs.
4. The estimated parameter values are used as input parameters for a new FE model that

What Is An Interpolation Equation
1. INTRODUCTION OF LAGRANGE POLYNOMIAL ITERPOLATION
1. 1 Interpolation:
First of all, we will understand that what the interpolation is.
Interpolation is important concept in numerical analysis. Quite often functions may not be available
explicitly but only the values of the function at a set of points, called nodes, tabular points or pivotal
points. Then finding the value of the function at any non–tabular point, is called interpolation.
Definition:
Suppose that the function f (x) is known at (N+1) points (x0, f0), (x1, f1), . . . , (xN, fN) where the
pivotal points xi spread out over the interval [a,b] satisfy a = x0 < x1 < . . . < xN = b and fi = f(xi)
then finding the value of the function at ... Show more content on Helpwriting.net ...
. . + ________________________________________ f4 (x0 – x1) (x0 – x2)(x0 – x3)(x0 – x4) (x4
– x0)(x4 – x1)(x4 – x2)(x4 – x3)
(0.3 – 1)(0.3 – 3)(0.3 – 4)(0.3 – 7) (0.3 – 0)(0.3 – 3)(0.3 – 4)(0.3 – 7) =
________________________________________ 1+
________________________________________ 3 + (–1) (–3)(–4)(–7) 1 x (–2)(–3)(–6)
(0.3 – 0)(0.3 – 1)(0.3 – 4)(0.3 – 7) (0.3 – 0)(0.3 – 1)(0.3 – 3)(0.3 – 7)
________________________________________ 49 +
________________________________________ 129 +
3 x 2 x (–1)(–4) 4 x 3 x 1 (–3)
(0.3 – 0)(0.3 – 1)(0.3 – 3)(0.3 – 4)
________________________________________ 813
7 x 6 x 4 x 3
= 1.831
Figure 1: Example of Lagrange interpolation 4. APPLICATIONS OF LAGRANGE POLYNOMIAL
INTERPOLATION

 Lagrange polynomials basis are used in the Newton–Cotes method of numerical

Fabrication Of Four Wheel Steering Mechanism
FABRICATION OF FOUR WHEEL STEERING MECHANISM
A PROJECT REPORT Submitted by
BISHT VIKRAM D. SINGH 121000119023
SHINDE LALIT M. 121000119026
BIJARNIA DINESH O. 121000119034
JADHAV PRASHANT P. 121000119104
In partial fulfillment for the award of the degree
Of
BACHELOR OF ENGINEERING
in
MECHANICAL DEPARTMENT
Sigma Engineering college, Matar
Gujarat Technological University, Ahmedabad
May 2016
SIGMA ENGINEERING COLLEGE, MATAR
MECHANICAL DEPARTMENT
2016
CERTIFICATE
Date: 20/04/2016
This is to certify that the dissertation entitled "FABRICATION OF FOUR WHEEL STEERING
MECHANISM" has been carried out by Bisht Vikram D. Singh (121000119023), Shinde Lalit M.
(121000119026), Bijarnia Dinesh O. (121000119034), Jadhav Prashant P. (121000119104) under
my guidance in partial fulfillment of the degree of Bachelor of Engineering in Mechanical (8th
Semester) of Gujarat Technological University, Ahmedabad during the academic year 2015–16.

Guide: Head of Department (I/C) Principal
Mr. Sachin Jadav
Mechanical Department, SEC, MATAR. SEC, Matar.
Seal of Institute
ACKNOWLEDGEMENT
For successful working on the "Fabrication of four wheel steering mechanism" we would like to
thank some

Narrative Reflection
Friends, families, and colleagues frequently ask me about the classes I am taking in my doctoral
program. For most of the classes, they just smile and nod while feigning interest and that is the
extent of our conversation on the topic. However, when I tell them that I am taking a "statistics"
course, the most common reaction is a wide–eyed look with a grimace formed on their countenance.
The usual comments are, "Statistics sucks!", "I'm terrible at math!", or "You poor thing!". I just
smile, because I have a little secret...I really enjoyed the two statistics classes I have had so far, 702
and 873. I LOVE math and taught middles school math for eight years, so getting to take a class that
involves getting to use my math skills and enjoyment of analysis of data makes me giddy. Now,
don't get me wrong, this doesn't mean that several times this semester I didn't feel totally
overwhelmed and wanted to pull my hair out while banging my head on the counter. I was there!
THE most rewarding part of attempting something difficult and challenging is the point where the
lightbulb comes on and understanding (or partial understanding with some concepts) is FINALLY
reached. Overall, my experience in 873 was exciting because it was challenging and now I have a
better understanding of statistics and I have developed further skills for analyzing and using data.
These are important gains as data analysis is a big part of what I do in my job as an elementary
principal. When I do

Choose The Appropriate Joinery Technique When Building...
To: ENGL–315–45 Classmates
From: CJ Peterson
Subject: How to Choose the Appropriate Joinery Technique when building wood furniture
Date: April 6, 2016
Many people want to build their own furniture and other wood projects, but aren't sure how to start.
Different types of projects require different types of joinery, but your average Do–It–Yourselfer
(DIYer) can do most beginner projects.
It may seem overwhelming to many of you to take on a new task such as building your very own
furniture. Choosing the wrong joint could lead to additional unnecessary steps, weaken the object,
and cause premature failure of the furniture. Alternatively, it could just plain look unattractive – a
thing to be avoided for sure.
Choosing the appropriate joinery technique is quite simple as long as you follow the appropriate
steps. I have been building furniture and remodeling homes for eight years, which means I have
done quite a bit of woodworking. I am hoping to share this experience with my classmates so that
they can reach the very attainable goal of building their own furniture.
A good place to begin is to identify the main joints, consider what uses each is best used for, and
examine your own ability to execute each. These joints have all been around for a long time; they
have been tested in numerous applications. In this report, I highlight some of the main joints and
their applications.
Joint Options and Selecting the Correct Joint For Your Project
The

A Case Study on Cost Estimation and Profitability Analysis...
ISSUES IN ACCOUNTING EDUCATION
Vol. 26, No. 1
2011
pp. 181–200
American Accounting Association
DOI: 10.2308/iace.2011.26.1.181
A Case Study on Cost Estimation and
Profitability Analysis at Continental Airlines
Francisco J. Román
ABSTRACT: This case exposes students to the application of regression analyses to be used as a
tool pursuant to understanding cost behavior and forecasting future costs using publicly available
data from Continental Airlines. Specifically, the case focuses on the harsh financial situation faced
by Continental as a result of the recent financial crisis and the challenges it faces to remain
profitable. It then highlights the importance of reducing and controlling costs as a viable strategy to
restore ... Show more content on Helpwriting.net ...
Continental's internal forecasts indicated that a further decline in passenger volume should be
anticipated throughout 2009, with a recovery in travel possibly occurring by the middle of 2010.
To summarize, adverse economic conditions in the U.S., coupled with the rise in fuel costs, were
dragging down Continental's profits and relief was unlikely through the foreseeable future.
THE DECISION TO REDUCE FLYING CAPACITY AND THE IMPACT ON
OPERATING COSTS
Given the situation described above, management needed to act swiftly to restore profitability.
Several strategic options were evaluated. Since the U.S. and much of the world was facing a severe
recession, the prospect for growing revenues by either raising airfares or passenger volume seemed
futile. Contrary to raising revenue, Continental's managers believed that raising fares could
potentially erode future revenues beyond the present level. Discounting fares did not seem a
plausible solution either, because given the severity of the economic situation a fare cut could fall
short in stimulating additional passenger demand and lead to lowering revenues.
Thus, because management anticipated that revenues would remain flat for most of the year, the
only viable short–term solution to restoring profits was a substantial and swift reduction in operating
costs. This could most effectively be accomplished in two ways. First, through a reduction in flying

A Short Note On The Mission Of Scooter Indi
Vision аnd Mission of Scooter indiа Mission To fulfill customer 's needs for economic аnd sаfe
mode of roаd trаnsport аnd quаlity engineering products through contemporаry technologies. Vision
To grow into аn environment friendly аnd globаlly competitive compаny constаntly striving to meet
the chаnging needs of customer through constаntly improving existing products, аdding new
products аnd expаnding customer bаse. Objective Providing economicаl аnd sаfe meаns of
trаnsportаtion with contemporаry technology for movement of cаrgo аnd people.Providing eco–
friendly, flаwless аnd reliаble products to fulfill customer needs. Аchieving customer sаtisfаction by
providing products аt right price аnd аt right time. Future of Scooter Indiа Pаst is deаd аnd gone, we
аre stаnding on the threshold of todаy, plаnning for the future. In the process we аdded one wheel,
shifting the geаr from two wheeler to three wheeler аnd propose to аdd аnother, entering in to the
аrenа of four wheeler, though for а limited segment – the segment of zero emission. The quаlity hаs
moved from product to process to people. Only good quаlity people cаn work out quаlity processes
аnd provide quаlity products аnd services Environment is our greаtest heritаge аnd its protection,
our highest responsiblity. Green process design аs аlso green product design hаve аssumed
importаnce. Eco–design of the products аnd process is the tаsk for tomorrow. Аccordingly, product
development is the obsession for future.

Image Processing Essay
Abstract: – A Measurement is must before going to the further calculations in various fields of work
or study. In order to find out something we definitely need some calculations. In different sectors,
determining exact size and shape are progressively becoming an issue and based on that the latency
is going up. As we cannot measure everything with a scale or a tape, we use some optical methods
of Image Processing. In this paper, we present an approach that can be used to determine the lengths
and some other degrees of measurements like diameter, spline, Caliper(perpendicular angle) etc. We
used mostly the Image Processing techniques because all the measurements are done on an Image.
We also use some other techniques like Euclidean ... Show more content on Helpwriting.net ...
The image can be enhanced to mark down the accurate end points. It actually can mark the end of a
single pixel which is almost invisible as a single pixel to the naked eye. A set of operations need to
be carried out respectively to achieve this. Initially the image need to be acquired and smoothened to
mark the pixel actually need to be. Then the neighborhood pixels collision should be eliminated
followed by the image segmentation. Finally, using the Euclidean algorithm the exact length can be
found.
II. IMAGE AQUSITION AND SMOOTHING: –
In Image Processing mostly the initial step will be the Image acquisition and smoothing. As the
input for the tool of any Image Processing technique is an image, the input image should be taken
and enhanced in all the ways possible. Enhancement involves smoothing the image, grey scaling,
removing the unwanted blur, differentiating the subject from background and so on. In this project,
for enhancing or smoothing the image we use the median filter. The median filter is non–linear
digital filtering technique where the noise reduction is the pre–processing step before going to the
further processing. Because the signal is big in the case of images, we chose median filter as it can
handle the larger signal and the run–time is literally less. The major advantage of the median filter is
the edge preservation. It processes each signal individually and replaces the edges of the pixel with

The Shot Boundary And Classification Of Digital Video Essay
Shot boundary and classification of digital video is most important step for effective management
and retrieval of video data. Shot transitions include abrupt changes and gradual changes. Recent
automated techniques for detecting transitions between shots are highly effective on abrupt
transitions. But finding gradual transition is major challenge in the presence of camera and object
motion. In this paper, different shot boundary detection technique has studied. The main focused on
to differentiated motion from various video effects noise, illumination changes, gradual transition,
and abrupt transition. Specially, the paper focuses on dissolve detection in the presence of camera
and object motion Keywords: Shot boundary, Gradual transition, Abrupt Transition, Video Retrieval
I.INTRODUCTION: The advances in the data capturing, storage, and communication technologies
have made vast amounts of video data available to consumer and enterprise applications [1].
However, interacting with multimedia data, and video in particular, requires more than connecting
with data banks and delivering data via networks to customers, homes or offices. We still have
limited tools and applications to describe, organize, and manage video data. The fundamental
approach is to index video data and make it a structured media. Manually generating video content
description is time consuming and thus more costly to the point that it's almost impossible. This is
because of the structure of video data,

Notes On The And Flow Data
2 DATAAND OPERATIONALIZATION
Tostudy the impactofpoliticalaffinity onFDI,I employ the datafrom UNCTAD,whichrecord bilateral
FDI stock and flow data for more than 200 countries from 2001 to 2012. Since FDI flow data can be
a poor conceptual choice for answering political questions (Kerner, 2014;
Sorens and Ruger, 2015; Benacek et al., 2014), I choose to use only the stock data.
One possible drawback for UNCTAD data is its compilation by reports from different countries,
which have varied reporting rules and rigorousness. As a result, it may have serious measurement
errors or substantial missing values. However, this weakness appears to plague the other two major
FDI data resources (IMF and OECD) as well. 1 In addition, since biased or ... Show more content on
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This choice is mainly out of convenience. It is also workable to use the outstock data instead. The
interpretation will be similar.
2.1 POLITICAL AFFINITY
Admittedly there is no perfect measure for two countries' political closeness. To somehow proxy this
measurement, I use the ideal points compiled by Erik Voeten and colleagues from the UN general
assembly roll–call voting data. 3 This dataset has ideal point estimates for countries from 1946 to
2014. To measure political affinity for a given country pair, I take the absolute value of the
difference between their ideal points and code them as "idealPoint". In this regard, the smaller value
the idealPoint variable is, they closer is the country pair's relationship.
2.2 CONTROLS
As mentioned before, to somehow mitigate the reporting country's biasness, I need to apply controls
over the country's general FDI volume. More importantly, the volume of FDI stock in a certain
country is substantially affected by the attractiveness of the destination and the financial caliber of
the donor country. In this regard, I tally the total volume of both the instock FDI for the recipient
country and the outstock FDI for the donor country. The former is used as a proxy for the
destination's investment attractiveness and the latter for the donor's financial caliber. This way I can
sidestep the use of other economic controls and focus on studying how political affinity affect FDI.
3This dataset can be downloaded from Harvard.

What Are The Five Public Datasets?
The proposed approach was evaluated using 5 public datasets based on two types of experiments. It
was compared to two related registration methods and three state–of–the–art methods in terms of
accuracy, solution regularity and computational cost. Subsection{Data} 5 public available brain
MR image datasets (BrainWeb, CUMC, IBSR, LPBA, OASIS) were selected in our experiments.
These datasets have been used in image registration projects [#Klein2009, #Ou2014, #Hellier2001,
#Rohlfing2012] for performance evaluation. In these datasets, the whole head was captured by a
variety of imaging scanners and protocols. Accordingly, images had different size, voxel spacing
and contrast level. All images were provided with corresponding label ... Show more content on
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These labels were combined to produce an initial non–background binary mask. We then used the
c3d tool provided by the ITK–SNAP package [#Yushkevich2006] to sequentially perform a three–
step operation on the initial mask: dilate by one voxel, fill holes with face connectivity and erode by
one voxel in each dimension. Such an operation resulted a solid mask for the brain only region.
Structures outside the mask were stripped to generate the brain images. An additional bias field
inhomogeneity correction algorithm was performed based on the N3 algorithm [#Sled1998] using
the c3d tool. CUMC: 12 images were acquired on a 1.5 T Siemens scanner by the Columbia
University Medical Center. These images had relatively large variations in contrast. 128 class labels
were provided covering detailed brain structures. This dataset was used for evaluation in
[#Klein2009] and a cached copy http://www.synapse.org/#!Synapse:syn3217817 was maintained by
the author of [#Klein2009]. We used this copy in our experiment because the original data source
was inaccessible. IBSR: the Center of Morphometric Analysis at the Massachusetts General
Hospital provided the Internet Brain Segmentation Repository (IBSR) dataset. It contained 18
images with three types of voxel spacings. A skull stripped version [#Rohlfing2012] including label
modification was provided as an update to the original data at http://www.nitrc.org/projects/ibsr. 43

Nt1310 Unit 3 Assignment 1 Study Guide
All substates of $S_{1,L_2}$ describe the actual mating process. $S_{3,L_3}$ describes the first
contact between the spline shaft and its socket. $C_U$ of $C_{3,L_2}$ is defined that the acting
forces and torques are above the sensor noise level and that there is a nearly constant $z$–position.
$C_R$ demands that both workpieces have not been in contact before. So the state $S_{3,L_3}$ or
$ll$textit{firstContact}$gg$ describes how the spline shaft is inserted into the socket. The end of
the mating task is represented by state $S_{4,L_3}$ or $ll$textit{finalMating}$gg$. For being in
this state, $C_{4,L_2}$ requires a high $z$–force peak and after that a constant $z$–position.
Another substate is $S_{2,L_3}$ or $ll$textit{preBlocking}$gg$ and this substate is discussed in
detail during the description of the online fault detection in
Subsec.~ref{subsec:OnlineFaultDetection}. The last substate is $S_{1,L_3}$ which is named
$ll$textit{mating}$gg$ again. That is because this ... Show more content on Helpwriting.net ...
The first is named $S_{6,L_3}$ or $ll$textit{break}$gg$. In order to be in this state,
$C_{6,L_2}$ has to be fulfilled and this condition requires a constant $z$–position and constant
forces and torques. It is defined by the domain expert that a constant $z$–position and changing
forces and torques are equal to the condition $C_{5,L_2}$. The corresponding state is named
$S_{5,L_3}$ or $ll$textit{blocking}$gg$. This is a very interesting state because it contains a
problem during task execution. On higher decomposition levels, the substates of this state also
describe how the problem was handled in order to solve it. But on higher levels, the domain expert
has to be aware of the maximal human movement frequency in order to identify intended human
actions. If a state does not take enough time, it can represent an intended movement. So the domain
expert has to define a $C_R$ according to the maximal movement frequency of a

External Estimates
Measures from External Estimates
External Estimates of the Number of Abortions Pooled and by Individual Year
The counts of the total number of abortions for the study period years come from published
information from the Guttmacher Institute's census of abortion providers (Finer and Henshaw 2003;
Jones and Kavanaugh 2011). Following prior research, I used the CDC's age distribution for each
year to calculate the expected number of abortions that occurred in the age distribution of Add
Health for each year, except in the years when an age distribution was collected by the Guttmacher
Institute's survey of abortion patients (Kost and Jones 2007; Fu et al. 1998). Because the CDC data
is provided in ages ranges (under 15; 15–19; 20–24; 25–29; 30–34; 35–39; 40+), the Add Health
data was restricted to best match these ranges (see Appendix 2 for further details).
The overall quality analyses (years 1994 through 2007) could potentially include pregnancies
between the ages of 10 (the youngest individuals in Wave 1 in 1994) and 32 (the oldest individuals
in Wave 4 in 2008). Though it should be noted that pregnancies under the age of 13 would be
somewhat improbable considering the median age of menarche in the U.S. is 12.43 ... Show more
content on Helpwriting.net ...
For example, in 2005 women in the Add Health sample would be between 21 and 30. Women 30
and over were excluded from the analyses and the comparison group was aged–matched based upon
the CDC estimates for women aged 20–24 and 25–29. Spline and linear interpolated age estimates
from the Guttmacher Institute's survey of abortion patients were compared with the estimates used
to test the robustness of these analyses, and no significant differences were found when comparing
the 95% confidence intervals (see Appendix 3A and Appendix 3B). Overall, these external estimates
were treated as known population counts, despite the fact that some unquantified error does

Analyse The Effect Of Shape Of Notch On The Strength Of...
There are several types of experimental and research works that have been developed to analyse the
effect of shape of notch on strength of alloy steels. In spite of the fact that the fracture tests on the
notched bars have been conducted to analyse the shape of notch on tensile strength of bar but no
studies have been found within the literature on EN–8 about various shape of notches. An extensive
review and discussion of work have been done on the analysis of shape of notch on twisting strength
of alloy steel. The details are as follows: Barsoum et al in 2014 [1] presents a finite element
modelling framework to determine the torsion strength of hardened splined shafts by taking into
account the detailed geometry of the involute spline ... Show more content on Helpwriting.net ...
Fonte et al in 2006 [3] Suggested that Most of catastrophic mechanical failures in power rotor shafts
occur under cyclic bending combined with steady torsion: Mode I (ΔKI) combined with Mode III
(KIII). An analysis of the influence of steady torsion loading on fatigue crack growth rates in shafts
is presented for short as well as long cracks. Long cracks growth tests have been carried out on
cylindrical specimens in DIN Ck45k steel for two types of testing: rotary or alternating bending
combined with steady torsion in order to simulate real conditions on power rotor shafts. The growth
and shape evolution of semi–elliptical surface cracks, starting from the cylindrical specimen surface,
has been measured for several loading conditions and both testing types. Short crack growth tests
have been carried out on specimens of the same material DIN Ck45k, under alternating bending
combined with steady torsion. The short crack growth rates obtained are compared with long crack
growth rates. Results have shown a significant reduction of the crack growth rates when a steady
torsion Mode III is superimposed to cyclic Mode I. A 3D Finite Element analysis has also shown
that Stress Intensity Factor values at the corner crack surface depend on the steady torsion value and
the direction of the applied torque. Citarella et al in 2010 [4] Worked on Comparison of DBEM and
FEM crack path predictions in a notched shaft under torsion, they analyzed that the rather

Adaptive Smoothing Tractor Spline For Trajectory...
documentclass{article} % use "amsart" instead of "article" for AMSLaTeX format
usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
geometry{left=1.5cm,right=1.5cm,top=1.5cm,bottom=1.5cm} usepackage{graphicx}
usepackage{amssymb} usepackage{indentfirst} usepackage{amsmath} usepackage{amsthm}
usepackage{subfigure} usepackage{siunitx} ewtheorem{theorem}{Theorem} ewtheorem{lemma}
{Lemma} usepackage{rotating} usepackage{lscape} usepackage{natbib}
providecommand{keywords}[1]{ extbf{ extit{Keywords: }} #1} itle{Adaptive Smoothing Tractor
Spline for Trajectory Reconstruction} author{Zhanglong Cao, Matthew Parry} %author{Zhanglong
Cao,$^1$ Matthew Parry,$^1$ %affil{$^1$University of Otago} %affil{$^1$University of Otago/
Department of Mathematics and Statistics / New Zealand}} date{} % Activate to display a given
date or no date egin{document} maketitle egin{abstract} Trajectory of a vehicular system can be
reconstructed from noisy position data. Smoothing spline is an efficient method of reconstructing
smoothing curves. In conventional smoothing spline, the objective function minimizes errors of
observed position points with a penalty term, who has a single parameter that controls the
smoothness of reconstruction. Adaptive smoothing spline extends single parameter to a function
varying in different domains and adapting the change of roughness. In this paper, using Hermite
Spline, we

Biographical Essay: Martha Grace
With her dachshund sweaters, dangling glass earrings and heart warming smile, my grandmother,
Martha Grace, is a character from the moment she catches your eye. The wide and contagious smile
Grammy proudly wears could truly light up any room. However her looks are undoubtedly not her
most impressive attribute. During a time when very few women attended college, Grammy was able
to receive her Bachelor's degree from Smith College. Her life took off after college and she married
her first husband, Nathan Grace. After getting a divorce from Nathan whom which she had two
children with, Grammy became a stay at home mother. However this uneventful profession had
Grammy longing for excitement. In order to satisfy her desire for excitement and adventure, ...
Grammy is truly young at heart and can certainly make the dull, knitting grandmothers jealous. One
quality I admire about my grandmother is that she is often oblivious to others opinions of her. I will
never forget when she took my brother, Marshall, and I to the aquarium when we were about 8 and
10 years old. She told us she found a new way of finding her car when she forgot where she parked.
She proceeded to sound the car alarm in the parking garage until we located the vehicle. We got a
few strange looks and Marshall and I were completely embarrassed, yet she could not have cared
less. By observing my grandmother and her capabilities, I have been able to refrain from other
thoughts of how I look or dress and just be myself. Among all of the people I have met in my life,
Grammy is someone whom I admire most and am inspired by every day. When all is said and done,
I don't know many grandmothers who are as fun, caring and hardworking as the one I am proud to
call mine. Grammy is a remarkable and unforgettable person and I would feel so lucky if I were to
become even half the person she is

Random Forest, An Ensemble Learning Algorithm
3. ALGORITHM BASIS 3.1. Random forest The random forest is an ensemble learning algorithm
that combines the ideas of bootstrap aggregating [20] and random subspace method [21] to
construct randomized decision trees with controlled variation, introduced by Breiman [22].
According to the theory of random forest algorithm, for a collection of classifiers h1(x), h2(x), . . . ,
hK(x), and with the training set at random from sampled random vector Y, X, the margin function is
termed as: (1) where, I(.) represents the indicator function. This margin function measures the extent
to which the fraction of correct classifications exceeds the fraction of the most voted incorrect
classifications. The generalization error is given as: (2) where, the probability is over the space X, Y.
This depends upon the strength of the individual weak learners in the forest and the correlation
between them. By definition, in random forests, (3) Therefore, the margin function for a random
forest would be: (4) And the expected strength of the classifiers in a random forest is: (5) The
fundamental idea of the random forest is that at each tree split, a random sample of m features is
drawn, and only those m features are considered for splitting, where m = √N, N being total number
of features. For each tree grown on a bootstrap sample, the out–of–bag strength is monitored. The
forest is then re–defined based on this out–of–bag strength by de–correlating the irrelevant trees.
3.2.

The History Of Geographic Information Systems ( Gis )
Introduction:
Recent research on interpolation of climatological and meteorological information with the support
of Geographic Information Systems (GIS) has shown that interpolation has a large development
potential within climatology and meteorology. At the same time the demand for interpolated data
products are increasing, numerical weather models are working at higher spatial resolutions and may
be initiated by gridded data from observations. Interpolation is a method of getting new data from
some known data points. In India, many weather data is from official departments and there are
many weather sites, but in some areas it is difficult to obtain weather data, so we will use
interpolation methods to get climate data of that areas. Interpolation can be defined as the estimation
of an unknown value of the variable, at some point where no measurement is available, where the
estimate is made using known measurements obtained at a set of sample locations. With the advent
of Geographic Information Systems (GIS), numerous spatial interpolation methods have been
applied to create continuous surfaces of climate variables at various spatial (watershed, regional, and
global) and temporal (hourly, daily, monthly, seasonal, and annual) scales. The prediction of weather
condition is essential for various applications. Like weather prediction, soil moisture, climate data
monitoring, rainfall, population prediction, agriculture, image processing etc. Regression model,
Feed Forward

An Evaluation Of An Innovative Contribution Of My Work
Research Statement Mohamad S Hasan Modern technological advancements in automated data
production have produced a large increase in the scale and resolution of data sets. In a statistical
context, more information generates more hypothesis tests and opens new dimensions to discover
the targeted questions. However, many of the tests are redundant and, hence, reduce the efficiency of
the analysis. One potential solution to this problem is using external information to prioritize the
hypothesis tests most likely to yield true positive effects. One means of doing so is p–value
weighting. Many statistical methods have been proposed to up–weight and down–weight the p–
value in a multiple hypotheses setting. None of them are satisfactory, which necessitate extensive
research in this area. My methodological and theoretical research as well as a considerable portion
of my applied work addresses this issue with regard to high throughput and big data. An innovative
contribution of my work is the establishment of a new perspective on the analysis of high
throughput data for which relative effect sizes are very low and the true effect is hard to detect with
the usual statistical analysis, although external sources of information suggests otherwise. We
proposed a method referred to as Novel Optimal P–value weighting for High Throughput Data.
Many studies have that suggested diverse methodologies regarding the p–value weighting. Even
though, theoretically, these approaches propose

Components Of The Clutch Pack
There are three components that forms the clutch assembly. They are the clutch pack, one way
clutch and the band. These components help in the gear shifting and to have a smooth driving.
CLUTCH PACK The clutch pack contains alternating disks that fit inside a clutch drum in which
half of the discs are made up of steel. These disks have splines which fit inside the clutch drum on
the respective groves. The other half consists of a friction material bonded to their surface. These
disks also have splines but on the inner side which fit into the groves on the outer surface of the
adjoining hub. At appropriate times, the clutch pack is squeezed together by a piston which is
present inside the clutch drum that is activated by oil pressure so that the two components become
locked and turn as one. ONE WAY CLUTCH One way clutch is a device which have the effect just
like that of a bicycle, where it allows a component to turn freely in one direction but not in the other.
One way clutch is also called as sprang clutch. This is mainly used in ring gear so that it will not
turn in opposite direction. One way clutch is commonly used in the first gear. For example, place the
gear shifter in drive position and give acceleration. The vehicle begins to move normally and when
the acceleration is released the vehicle continues to move freely as if the vehicle is in neutral.

Essay On Hourly Cooling Load
Li et al. (2009) applied support vector machine (SVM) in propose of prediction hourly cooling load
for an office building in Guangzhou, China. They also compare their finds with results from back–
propagation (BP) neural network model. The results show the SVM has higher accuracy and better
generalization. The predictors for this study were normalized door dry–bulb temperature for current
and previous hour and the previous 2 hours, normalized relative humidity, and normalized solar
radiation intensity for current and previous hour and normalized cooling load was the target. The
month of July was used to train the model. May, June, August, and October data were used to the
model. Moreover, Authors used simulation software (DeST) to calculate ... Show more content on
Helpwriting.net ...
This method was implemented on a super high–rise building in Hong Kong. The data was the
measurement of the actual load from mid–June to early August in 2011. The root–mean–square error
(RMSE) and the R–square value the initial load prediction were 0.89 and RMSE 2144 kW
respectfully. The results of calibrated load prediction was improved. When errors of the past 2 hours
were used, the results showed the best agreement with the actual data with 0.96 R–square and
1058 kW RMSE [16]. Huang and Huang (2013) used Autoregressive Moving Average with
Exogenous inputs (ARMAX) model, Multiple Linear Regression (MLR) model, Artificial Neural
Network (ANN) model and Resistor–Capacitor (RC) network (a simplified physical model) to
predict the cooling load for an office buildings in Hong Kong. The inputs variables were previous 4
hour cooling load, dry bulb outdoor air temperature, solar horizontal radiation, and room
temperature set point. The compression results show MLR and ARMAX models have better
performance with the smallest mean MBE and mean standard deviation [17].
Sun et al. (2013) applied general regression neural network (GRNN) with single stage (SS) and
double stages (DS) to predict load. In double stage model, the first step is predict the weather data
for the next 24hours; the second step is to predict cooling load. Two hotels in China were chosen to
test and validate the models. The authors found that DS

Secure Data Transmission Through Multiple Hops
SECURE DATA TRANSMISSION THROUGH MULTIPLE HOPS IN WIRELESS SENSOR
NETWORK
1Pooja Gupta, 2Dr. Shashi Bhushan, 2Sachin Majithia, 2Harsimran Kaur
1Research Scholar, Department of Information Technology, Chandigarh Engineering College,
Landran
2Department of Computer Science and Engineering, Chandigarh Engineering College, Landran
Punjab, India
E–mail: 1 guptapooja2004@gmail.com,2 shashibhushan6@gmail.com,2
sachinmajithia@gmail.com, 2 harsimrangne@gmail.com
Abstract
Wireless sensor networks (WSN) are self– governing sensors that are widely distributed in the
respective environment. They are also referred as wireless sensor and actuator networks (WSAN).
They are used to observe physical or environmental conditions, for example, temperature, sound,
natural activities etc. They collect data from each active node and pass it to the network to the
centralized location. There are many flaws occurred in wireless sensor network like user
authentication as well as data travel in the network is not so much secure. We are developing a
technique in which firstly we are allowing the user to pass the hard security authentication scheme
and then the user can join the network. Further we are also providing a secure file transmission in
network via public and private key concept. In this way we maintain the secure and authenticated
transmission of data in predescribed environment.
Keywords– Data Transmission, Wireless Sensor

Smoothing Dat The Estition Of Variance In Data
ESTIMATION OF VARIANCE IN HETEROSCEDASTIC DATA
Abstract
Data which exhibit none constant variance is considered. Smoothing procedures are applied to
estimate these none constant variances. In these smoothing methods the problem is to establish how
much to smooth. The choice of the smoother and the choice of the bandwidth are explored. Kernel
and Spline smoothers are compared using simulated data as well as real data. Although the two seem
to work very closely, Kernel smoother comes out to be slightly better.
KEY WORDS: Smoothing, Kernel, Spline, Heteroscedastic, Bandwidth, Variance. 1. Introduction
Let us have the observations .The mean is given by , . The deviations of each observation away from
... Show more content on Helpwriting.net ...
Given the errors are independently distributed. However if any of these assumptions are violated the
estimates obtained under the classical or usual assumption are not good. Therefore we hope to
obtain better estimates of when the estimation of the variance is incorporated.
Therefore, we need to investigate and incorporate the information about the variance estimates of
the errors which are needed for better understanding of the variability of the data. In heteroscedastic
regression models the variance is not constant. Often as in the case with the mean, the
heteroscedasity is believed to be in functional form which is referred to as variance function. We try
to understand the structure of the variances as a function of the predictors such as time, height, age
and so on. Two procedures of estimating the variance function includes the parametric and
nonparametric methods. The parametric variance function estimation may be defined as a type of
regression problem in which we see variance as a function of estimable quantities. Thus, the
heteroscedasticity is modeled as a function of the regression and other structural parameters. This
function is completely known, specified up to these unknown parameters. Estimation of these
parameters is what entails parametric methods.
However, for many practical problems the degree to which components of the statistical model can
be specified in a parametric form varies

T-Hangers Case Study
D) Follow–up items 1) T–Hangers/T–Bars Marvin Hewitt followed–up with the committee on the
issue with the T–Hangers failing the Hi–Pot test. Marvin stated he meet with the manufacture and
got their recommendation on how to test the t–hangers which aligns with how the regional tech
services tests the t–hangers. Marvin will speak to Mofeid from Astoria to expedite the process to test
and ship the units back to the regions. 2) Homac Storm–Safe Breakaway Service Connectors Ray
Dominguez asked the committee if anyone had any experience or issues with the Homac Storm–
Safe Breakaway Service connectors. No one had any issues with the connectors and one of the
member sof the committee stated that the service groups and underground uses the connectors

A Relationship Between The Height Of Waitutu Forest Trees
Bivariate 3.9: Waitutu Forest Saplings Problem I am going to investigate if there is a relationship
between the height of Waitutu forest saplings, and their width (measured at breast height). Both the
explanatory variable and the response variable are measured in centimetres. This data was taken
from randomly selected trees in study plots of either 1.5 hectares or 2.25 hectares of the Waitutu
Forest in the summer. The data was published by the Landcare research NZ.ltd in the Waitutu
Forest, Southland 2001–2008. I think that the data will show a positive relationship in that as the
height of the sapling increases, so will the width. Plan I am carrying out this investigation as Waitutu
Forest is one of new Zealand 's largest forests, covering 45,000 hectares of South East Fiordland and
is part of the National park. According to the Department of Conservation, The Waitutu forest is one
of the largest areas of unmodified lowland forest left in the country. The unique build of the
landscape consists of marine terraces and gullied terrain. The species of plant analysed in this
dataset were sub–canopy (large shrubs or small trees), Broadleaf forest trees, and podocarp forest
trees. The study of undergrowth and young saplings allows understanding of growth and how each
plant develops, therefore allowing us to aid the continuation of the very significant Waitutu forests,
and therefore protecting our native flora and fauna. By investigating whether or not there is a
relationship

Benefits Of Effective Registration Methods
The proposed method was compared to 5 other registration methods. In order to show the benefit of
using the proposed piecewise registration framework, we compared the proposed approach to a
nonlinear registration using a global DCT nonlinear model. To demonstrate the performance change
caused by replacing the linear model with the nonlinear model in a piecewise registration
framework, we compared the proposed approach to a piecewise affine registration approach. In
addition, to evaluate the standing of the proposed approach, we compared it to three established
registration methods: a) the SyN method [#Avants2008] provided in the Advanced Normalization
Tools (ANTs) software package http://http://stnava.github.io/ANTs. In ANTs, a novel ... Show more
content on Helpwriting.net ...
Second, recommended parameters for ANTs and DRAMMS were provided by their developers
when they were evaluated in [#Klein2009] and [#Ou2014]. Elastix maintained a database
http://elastix.bigr.nl/wiki/index.php/Parameter_file_database of parameter files that were used in
published work. Each of such parameter files was recommended with usage scenario, we could
select from existing configurations to get the best of Elastix. Third, the selected Elastix
configuration used the cubic B–Splines based model. Thus, the Elastix method can be considered as
a published implementation using a B–Splines based global nonlinear model. When it was compared
to the proposed approach with the piecewise B–Splines model, we could evaluate the gain of using
the piecewise framework. Subsubsection{Evaluation Metrics} For the intra–subject experiment, we
used the synthetic deformation as the ground truth and measured a rooted mean squared deformation
error (RMDE):mbox{RMDE}=sqrt{frac{1}{N_{M_{g}}}sum_{boldsymbol{x}in
M_{g}}leftVert boldsymbol{T}_{mbox{reg}}left(boldsymbol{x}right)–
boldsymbol{T}_{mbox{known}}left(boldsymbol{x}right)rightVert ^{2}}where
boldsymbol{T}_{mbox{reg}} and boldsymbol{T}_{mbox{known}} are the registered and
known deformation field, respectively; M_{g} is a mask image derived from the target image g, and
N_{M_{g}} is the number of voxels of the foreground region. This metric has the unit of

Essay On Cardiac MRI
Theory The pixel time profiles in cardiac MRI are extremely organized when we have perfect gating
and breath–holding. Some penalties for example; temporal Fourier sparsity (to exploit low temporal
bandwidth), temporal total variation (to exploit smooth pixel time profiles) or low–rank penalties (to
exploit the redundancy between the pixel time profiles) can be utilized to make the recovery from
under sampled data well posed. However, the compactness of the signal representations will be
extensively degraded in the presence of inter–frame motion, which can emerge due to breathing or
inconsistent gating; because of which, the performance of the above schemes will be extensively
compromised. We propose to defeat the above limitation by ... Show more content on
Helpwriting.net ...
The regularization term in Eq. (1) promotes the sparsity of the deformation corrected dataset〖 T〗
_θ.L, instead of L. Here, Φ(u) indicates an arbitrary prior to exploit the redundancy in the data; λ is
the corresponding regularization parameter. The primary advantage of the proposed algorithm is that
it can be utilized with any spatio–temporal priors on the deformation corrected dataset. The
particular priors can be chosen relying upon the particular application. The capability of the
algorithm to handle arbitrary image priors makes this methodology definitely different from
classical motion compensation algorithms that register each frame to a specific fully sampled frame.
The deformation field in Eq. (1) is assumed to be parametrically represented in terms of the
parameters. For example, Θ is the set of B–spline coefficients if a B–spline model is used to
represent the deformation field as in [24] and [25]. In this case, the spatial smoothness of the
deformation map is controlled by the grid spacing of the B–spline map. The spatial smoothness
requirements can also be explicitly forced using regularization constraints on the deformation field
as in [26]. Our approach is closely related to [26]. We suggest to use a variable splitting approach
[27], [28] to decouple the original problem in (1) to simpler sub problems. Specifically, we split the
deformation from the l_1 norm by introducing an auxiliary variable g. This enables us to
reformulate the unconstrained

A Note On Quantitative And Quantitative
Results
Question 3. Ӯ=b+mx or Ӯ=mx+b, Ӯ= dependent variableoverall, a= constant b, b1=predictor
1GRE score on quantitative b value, x1 = GRE score on quantitative. b2=predictor 2GRE score
on verbal b value, x2=GRE score on verbal. B3=predictor 3ability to interact easily b value,
x3=ability to interact easily. Equation– Ӯ=a+b1(x1) +b2(x2) +b3(x3)Overall college
GPA=2.250+0.002(GRE, quantitative+0.028(ability to interact). Step 1–If the model is significant
with a significant value of 0.014, less than 0.05. High F value (3.907), lower significance value
(.014). Step 2=Amounted accounted for=R2=.20320.3% of the variance is accounted for by the
predictors. There was a moderate effect size. There is a moderate correlation (R=0.451) ... Show
more content on Helpwriting.net ...
Linear Regression is a data analysis deliberated for computing the association between two variables
by connecting a linear equation to perceive information (Christensen, et al, 2014). An individual
enters data points into the calculator and the computer retains the course of the calculations and
completes the essential calculations for linear regression (Christensen, et al, 2014). Linear
Regression is a method for displaying the straight–line relationship between variables by using a
linear equation to observed data. Regression analysis is a collection of statistical procedures used to
explain or predict the values of a dependent variable founded on the standards of one or more
independent or predictor variables. (Christensen, et al, 2014). The two main kinds of regression
analysis are called simple regression, in which there is a single independent or predictor variable,
and multiple regression, in which there are two or more independent or predictor variables
(Christensen, et al, 2014). The straightforward notion of regression analysis is to acquire the
regression equation, and this calculation outlines the regression line that best fits the pattern of
reflection in the

Types Of Contingencies, Designing A Contingency, And Owner...
According to Gunhan and Arditi (2007), there were three types of contingencies, namely designer's
contingency, contractor's contingency, and owner's contingency. They claimed that the best method
to predict contingency was to use previous experiences. They mentioned that a detailed study of four
factors, namely site conditions, schedule constraints, project scope, and constructability issues could
play an important role either in preventing the CO or reducing the chances of needing a big
contingency money. Smith et al. (1999) stated that the wise decision on the amount of contingency
used while bidding could have effects on whether wining of the contract. They interviewed 12
contractors on the contingency calculation method and found that among these contractors, nobody
was aware of any kind of estimation method for the contingency amount. Whenever, these
contractors used contingency, they simply followed the traditional approach of adding some
percentages to the base cost as contingencies. Mac and Picken (2000) conducted a study on two
types of projects, namely estimating using risk analysis (ERA) and non–ERA projects. They made
comparison between 45 ERA projects with 287 non–ERA projects and found that ERA method
helped to reduce the unnecessary risk allowances in projects. According to the authors, Hong Kong
government was implementing this ERA technique in public construction projects. In the ERA
method, they described that the cost determined for fixed and variable

Statistics Essay
Executive Summary Business Statistics
In this assignment I compiled the data of the Nissan GT–R 3.8 (R35). The Data collected includes
the age, type and price which allowed me to make a statistic about how the age affects the price of
this certain model over several years. I will be using the correlation regression and scatter diagram
to get the regression line. As we can see, the price drops the elder the car is. Inside the range of the
diagram the prediction might be accurate. So we can tell very precisely how much the car is going to
cost in the next few years, but we won't be able to give a very precise prediction on how much the
car is going to cost after a long term (more than 10 years). This project shows the readers to justify
... Show more content on Helpwriting.net ...
X = 2.5 is within the range, which means that the estimation might be accurate as we are
interpolating. * When age of the car (x) = 10 Years old, price of the car will be (y) = Y= CHF 41853.
X=10 is outside the range, which mean that the estimation might be inaccurate as we are
extrapolating.
8.0 Referencing
1. Francis, Andre, 2004 Business mathematics and Statistics

Optimizing The Hypothalamic Hunger Regulation Mathematical...
OPTIMIZING AND VALIDATING THE HYPOTHALAMIC HUNGER REGULATION
MATHEMATICAL MODEL
Ms. Divya1, Dr. Saurabh Mukherjee2
1Research Scholar, 2Associate Professor, Department of Computer Science, AIM ACT,
Banasthali University, Banasthali–304022, email: jangid.divya@gmail.com
Hypothalamus has a significant effect on the physiological functions of human body like Hunger
regulation, Energy balance etc. A mathematical model is being developed which mathematically
explains the functionality of Hunger Regulation. Some hormones also acts effectively during this
process plays as important role in this model. Hypothalamic Hunger RegulationMathematical Model
(HhRM). We are using statistical optimization tools to optimize and validate this Model. The ...
This Hunger Regulation process is simulated with the help of Hypothalamic Hunger Regulating
Mathematical Model (HhRM)[2]. HhRM is a mathematical approach for this homeostatic function
of human body.HhRM divided into five different steps. Each step represents the combination of
mathematical functions and variables. A simple binary function G (h) shows that whether the
hormones are secreted by internal organs or not. The hormonal signals explain by the random
numbers.Daubechies Wavelet function interprets the movement of Hormonal signals through Vegal
Nerve.The response to the Hormonal Signals is being generated by the hypothalamic receptors. For
this the concept of signal generation is used with scaling function with Entropy. The receptors
signals transferred to Central Nervous system.
The mathematical model HhRM is as follows: dH/dt = G^ ' (h)+f(h)D4^ ' (h)+ Em(s)Sc '(s)
WheredH/dtis the change in the processing of Hypothalamus, H with respect to Time t, G(h) is the
binary function f(h) is fractal function, D4'(h) is the Daubechies function, Em(s) is entropy measure
and Sc'(s) scaling function.
2. Objective
The objective of our study is to optimize the mathematical model HhRM. In previous version of
HhRM the simple scaling function was being used. Here our objective is to study

NVIDIA : A Compute Unified Device Architecture
A. Compute Unified Device Architecture CUDA is a programming model created by NVIDIA gives
the developer access to GPU computing resources following through an Application Programming
Interface (API) the standard CUDA terminology. We will see GPU as the device and CPU as the
host programming language extends to C / C ++ Language. GPU programming is different from
model normal CPU models, and data must be clearly moved flat model between host and device
there is a multiple grid available for programmers' thread blocks are threads on the current structure
classes of 32 Threads multiplied by the name of Vars. The CUDA platform is built around a large–
scale similarity, where latency in memory access can be hidden waiting for other computations ...
C. Upper Bound on Performance When Simulating on the GPU It is not uncommon that algorithms
are bandwidth bound. Setting theoretical limit on maximum performance which can be obtained
from a GPU implementation important first step, which is also useful for identifying performance
after a real implementation. Implementation the COLE algorithm on the GPU is memory–intensive
due to the potential large number of point scatterers. A large number of point scatterers mean that a
large amount the process of memory needs to be processed, that is, memory bandwidth can be a
limiting factor. Outline of the primary parts of the COLE calculation. Each point scatterer is
anticipated onto the imaging line with an anticipated sufficiency that relies upon the sidelong and
elevational remove. After projection, the RF flag is gotten by convolving with a heartbeat
waveform. The scatterers are here attracted with zone corresponding to their outright dissipating
adequacy. The anticipated adequacy is reflected in the quality of the circle curves. In the least
complex case, a point scatterer is described by four gliding point numbers: three spatial directions
and a disseminating abundancy. As will be depicted in Section III, the consequence of preparing a
scatterer is a perplexing number, which requires two buoys. Utilizing 32–bit skims, which possess 4
bytes in memory, the aggregate number of bytes of memory movement is (4 + 2) · 4 bytes = 24
bytes for every

What Relationship Does Exist Between?
25– What relationship does exist between? a–The number of Followers and Downloads? b– The
number of Followers and Views? c– The number of Publications and Views? d– The number of
Downloads and Researcher 's Rank?
6– Does the number of Downloads is affected by the researcher status?
Solution:
a– Correlation between Followers and Downloads
Linear Regression Model
Regression line model is an approach to model the relationship between a scalar dependent variables
Y and one or more explanatory variables donated X based on the following equation:
Y= a + b .X
Therefore To find the relationship between Followers and Download we will find Correlation
Between the variables that will tell us the relationship between two variables and we will find ...
b–A correlation between Followers and Views?
To find the relationship between Followers and Viewers we will find Correlation Between the
variables that will tell us the relationship between two variables and we will find regression Line
Regression Analysis: Followers versus Views
The regression equation is
Followers = 9.694 + 0.000770 Views
S = 17.4377 R–Sq = 44.6% R–Sq(adj) = 44.5%
Analysis of Variance
Source DF SS MS

Questions On The Equation For Regression
Question 3–Results Question 3. The following equation was deduced from the Heredia, (2015)
question 3, and it was based on the equation for regression. These are the results: Ӯ=b+mx or
Ӯ=mx+b, Ӯ= dependent variableoverall, a= constant b, b1=predictor 1GRE score on
quantitative b value, x1 = GRE score on quantitative. b2=predictor 2GRE score on verbal b value,
x2=GRE score on verbal. B3=predictor 3ability to interact easily b value, x3=ability to interact
easily. Equation– Ӯ=a+b1(x1) +b2(x2) +b3(x3) Overall college GPA=2.250+0.002 (GRE,
quantitative+0.028(ability to interact). Step 1–If the model is significant with a significant value of
0.014, less than 0.05. High F value (3.907), lower significance value (.014). Step 2=Amounted
accounted for=R2=.20320.3% of the variance is accounted for by the predictors. There was a
moderate effect size. There is a moderate correlation (R=0.451) between the three predictors
variables. They are: (GRE on quantitative, GRE scores on verbal, and the ability to interact easily),
and the dependent variable is overall college GPA. B values–GRE scores on quantitative has the
greatest influence on the overall college GPA (B=.397) followed by the predictor the ability to
interact (B=0.145). The predictor GRE on verbal has a negative influence on the overall GPA (B=–
0.26). The predictor GRE score on quantitative is the best predictor (significance=.010). The GRE
on verbal is significant at .855 and the capability to interact easily is

Smoothing Dat The Estition Of Variance In Data

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