Linear Programming Processes For Optimization

Linear Programming Processes For Optimization
Abstract: – This paper discusses the linear programming model. Also, it describes the general conditions needed for utilizing linear programming
processes for optimization. It expands on the geometrical interpretation of these problems and relates the process to algebraic findings. In addition, it
discusses various algorithm utilized to solve optimization problems. Furthermore, it explores the validity of solutions and weather the optimal solution
is the best solution to the linear programming problem
Key–Words: – canonical form, constraints, feasible solution, feasible region, infeasible solution, interior point method, objective function, optimal
solution, simplex method, slack variable, standard form, unbounded region.
1Introduction The focus of human nature has always been to derive and associate behaviors that may occur in our daily routines. Throughout history,
we have observed the necessity to understand and derive those patterns. Once the comprehension is achieved, it is within our desire to determine if
the best possible way to reach a particular goal was attained. In our modern society, major importance is placed on several fields to grasp
comprehension on efficient ways to benefit and excel based on all possible restrictions. Furthermore, due to limitation of resources, it is imperative to
consider as much information as possible to make adequate decisions and remain effective. Concerns in providing efficiency and effectiveness have led
to the
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Advantages Of Linear Programming Models
ANS 1(a) – All Decision Science models have some assumptions on which they are based as well as having certain components unit. Linear
programming models is not different and they have been based on clearly some of the assumptions
Linear programming models always use linear arithmetical relationships for in lieu of a company's decision given to a business objective and the
resource restriction
The four workings of a linear programming model are as follows
1–Decision Variables
2–Objective Function
3–Restrictions or Constraints
4–Parameters
I would start with defining each of the component
Decision Variables– In general terms decision variables are mathematical symbols which put on the levels of activity of an organization or it ... Show
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The advantages of EOQ models are as follows
1– minimizes holding cost – Storing inventory is an expensive process for any business . The main advantage of this model is that it helps in
decreasing the storage or holding cost of goods by getting at the optimum inventory level . If the holding costs are high in a firm then that firm may
point to more orders of few items to reduce the cost
2– minimizes ordering cost – the aim of any firm is to minimize costs and the ordering costs are no exception . The EOQ model helps in reducing the
ordering costs by placing less orders in a year but of more items .
3– Ease of operations – EOQ does not only provide information about the optimum amount of inventory to be ordered but it also helps in providing
information such as the reorder level , lead time and the amount of safety stock to hold . All these information help in running the business efficiently
and minimizing the

Mixed Linear Analysis
mixed linear model (Zhang et al. 2010) implemented in the GAPIT package (Lipka et al. 2012) in R. determines the trade–off between misclassifying
training examples and minimizing the norm of the weights. Parameter controls the band of the insensitive zone that in turn affects the number of
support vectors in building the regression function. Bigger means lesser support vectors and produces more 'flat' estimates. III. A LGORITHM D
ESCRIPTION Decision Tree (DT) is used to build regression or classification models in the form of a tree structure. It predicts the value of a target
variable based on simple decision rules inferred from the data features. It breaks down the dataset into smaller subsets while concurrently an associated
decision... Show more content on Helpwriting.net ...
For analysis purpose, we used image data acquired on 8 July 2016 while manual measurement of the canopy height was done on 25 July 2016.
Coefficient of variation (CV) was found higher for canopy height estimated from image when compared to canopy height from ground truth data. We
believe that the higher variation in the table (between manual and image data) is because of the difference in the dates of manual measurement and
capturing of images. Eq. 2 в€‘ iв€€Nm (y i в€’ C m ) 2 Eq. 3 Random forest is a variation of Decision Tree which generates different regression trees
at training time and outputs the mean prediction of the individual trees. Support Vector Machines are a particular type of algorithms that uses kernels
and provides control on deciding the margin or the number of support vectors. The basic goal is to map nonlinearly the data into a high– dimensional
feature space and then a linear model is generated in the feature space. The quality of estimation is measured by the insensitive loss function. High
performance accuracy of the model can be achieved by tuning C, kernel and epsilon parameters. Parameter C Fig 4: Correlation of Canopy height
taken manually with canopy height extracted from Image data 5B. Growth pattern analysis from

The Linear System In Crisis
1)The linear system is a system in crisis because they don't tell us the entire story of where our stuff comes from and where it will end up. The
linear system cannot be run together with a finite planet. 2)Like the video said "It's our stuff that somehow got on somebody else land." that means
the U.S. are beginning to get their resources from the third world (somebody else resources). This means that the U.S. are now destroying another
environment just for resources. The community that lives in that environment and force to work 3)Externalized costs are cost of production that
somebody else pays for. This means that big companies are not paying their workers much and sometimes even skipping on worker's health
insurance. 4)She realized that she didn't really "paid" for that radio it was the people who suffered by the U.S. hands. Those people were third world
people (lost of their natural resource space) people who are getting sick due to toxic in the air, and Congo kids. 5)He told them to shop! Our nation
has become consumers because it's the primary way of the demonstrating our value. In other words, we have adopted an identity of consumers. Golden
arrow of consumption heart of the system. Top priority. 6)It was design 7)Planned obsolescence is another way of calling useless stuff that
manufactured to last you... Show more content on Helpwriting.net ...
That person sends an email indicating that he/she will make it his/her lifetime mission to eradicate Asians. The school campuses are trying to find ways
in protecting those group of people from hate speech without risking the erosion of speech rights. According to the first amendment it states that there
shouldn't be any limit on freedom of speech but could this on going problem be an

Margaret Atwood 's Happy Endings
Humanity's inherent desire for results and rewards belittles the process of a journey, undervaluing/deprecating the character development that comes
within, which notably can be portrayed in Margaret Atwood's "Happy Endings". In her metafictional short story, Atwood includes six different
scenarios that are labeled A to F, which briefly describes the characters' lives, ultimately ending with death. Moreover, the names of characters recur in
each scenario, referencing one another throughout the entire short story. Particularly when Atwood's short story "Happy Endings" was published, people
noticed that she was questioning gender roles and Canada's progress towards equality (Seaman). In recent times, critics see her short story attacking ...
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Unfortunately, in this case, the Bible oppresses women by limiting them to remain in their household. Hence, Atwood is stunned that the Bible even
enforces such a discriminatory idea; she refers this imposition as "evil", evidently indicating that she defies the Bible's principles as well as
illustrating herself as a feminist. With the establishment of an "Equality Clause", Atwood's role as a civil activist influenced her to write "Happy
Endings" to further criticize the arbitrary roles and norm that negatively impacted women. Almost a year prior to the publication of Atwood's short
story, an "Equality Clause" was included in the Constitution of Canada , guaranteeing men, women and the disabled equal rights. In a 1994 Booklist
review, Donna Seaman, the adult editor of Booklist, mentions that Atwood "continues to question the roots of [society's] assumptions about gender
roles, testing ...[Canada's] shaky sense of progress toward equality" ("Good Bones and Simple Murders"). Doubtlessly, Atwood's short story displays
how women easily fall prey to sexism. As of recently, Rena Korb, an American feminist, points out that Atwood criticizes "societal conventions,
particularly those surrounding the roles of women" (2). For instance, in Version B, Mary suffers from a one–sided relationship with John in which she
invites

Linear Technologies Essay
Case 1 Linear Technologies
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2007120262 к№ЂнљЁм„
Three main issues arise when it comes to dividend policy in firms. The first issue is whether dividend is needed or not and the second issue is
regarding which one would be the best option among various payout methods. Lastly, the third issue is about dividend rate. Whether these issues will
affect corporate values has been debated over the years. This paper will talk about such issues through the case study of Linear Technology.
1. Why dividend is needed.
Linear Technology's payout policy, unlike many competitors in the Semiconductor Industry, has a relatively large portion in dividends. Linear has
provided steady dividends ... Show more content on Helpwriting.net ...
Since the firm's stocks are growth stocks, the cash used to repurchase stocks lacks the opportunity of generating high cash flows. Accordingly, the
market price would result in decreased future earnings, EPS, and the firm value of Linear. The number of outstanding shares, instead of the price, will
decrease. While the price of stock would increase just as the amount of cash paid out to repurchase the outstanding stocks. It is important that in both
cases, earnings and earnings per share before the payment are not affected.
3. About the dividend rate
Firms judge the rate of dividend initiations by earnings. However, simply put, if dividend rate changes depending on the change of earnings, the
fluctuation of dividend will increase. This would not be good. Because cutting dividends means uncertain future cash flows. If a company cuts
dividend rate, shareholders will need higher opportunity costs of capital, as a result stock prices will go down. Thus, Linear has retained constantly
increasing dividend rates in small amounts. Under the theoretical assumptions such as M&M, there is no difference whether firms pay out dividends
or not. And if the cost of capital is lower than a firm's ROE, no dividend can raise a firm's value. However, considering the real–life factors, firms
should keep on steady level of dividend rate or repurchasing shares. Repurchasing shares seems to be a better solution. As a

Characteristics Of Understanding The Culture And Society...
"The mother art is architecture. Without an architecture of our own we have no soul of our own civilization."
––Frank Lloyd Wright
Architecture is an important aspect of understanding the culture and society of any civilisation. Today I will be looking what similarities and
differences can be seen between the Minoan architecture from 1900BC to 1150BC and the Roman architecture from 27BC to 180AD. I'll be mainly
focusing on the palaces of Knossos and Phaistos of Minoan, Crete and The Colosseum ad the Pantheon of Rome.
The architecture of Bronze Age Crete seems to be defined by the archaeological discoveries of Knossos, Phaistos, Maila, Zakros and Palekastro.
These four palaces and one town are the only major pieces of Minoan building from ... Show more content on Helpwriting.net ...
Occupation continued in Knossos until 1200 BC when it was destroyed by fire, Knossos appears to have been kept intact until the fire as a sort of
headquarters while all the other palaces were destroyed. The ruins we have today from discoveries made by Sir Arthur Evans in 1900 AD are the
remains from after the Palace of Knossos was burnt down. Knossos is a clear example of monumental (of great importance or size) Minoan architecture.
There are several major courts that make up the Knossos palace; these include the large, rectangular Central Court, the West Court, the West and East
Wings, the theater, royal chambers, shrines and the Throne Room. Like most Minoan buildings the Central Court seems to be the main focus of the
building. Stone was the primary building material, but also included a timber frame for flexibility. Mortar linked the stones together and wood was
used for all the columns, doors, windows and roofs. The interior walls were plastered with clay and often had bright frescoes painted on them. The
floors were generally beaten down earth, but sometimes had small pebbles stuck into them. Lighting and ventilation was primarily utilized through
light–wells, vertical airshafts that ran the full height of the buildings. Drainage was controlled through a series of terracotta pipes and stone ductwork.
The best–preserved section of this drainage system is below the East Wing. Polythyron doors are common feature of Minoan palaces and Evans

Linear Regression Model
Econ 122B Problem Set 2 Name(Print)______________________
Due in class Feb 6 UCI ID_____________________________ MultipleВChoice Questions (Choose the best answer, and briefly explain your
reasoning.) 1. Assume we have a simple linear regression model: . Given a random sample from the population, which of the following statement is
true? a. OLS estimators are biased when BMI do not vary much in the sample.
b. OLS estimators are biased when the sample size is small (say 20 observations).
c. OLS estimators are biased when the error u captures perseverance and selfвЂђ control, and you believe that ... Show more content on Helpwriting.net
...
b. be unbiased if using a large enough sample.
c. be biased, because the variance of efficienc/ability depends on training.
d. be unbiased, as long as both prod and training are recorded correctly.
8. Suppose you have the following estimated equation,
500 84 , where Burger refers to weekly number of burgers sold on average in In&In Burger joint and price is in US dollars. What would be your
estimate of the slope if price were in GB pounds (assuming 1 GB pound = 2 US dollars) AND you use daily number of burgers sold rather than
weekly? a.
вЂђ12
b.
вЂђ84
c.
вЂђ168
d. вЂђ24
9. Using data on 4,137 college students, the following equation was estimated by
OLS:
1.392
4,137,

0.0135
0.00148
0.273
where colGPA is measured on a fourвЂђpoint scale, hsperc is the percentile in the high school graduating class (defined so that, for example, hsperc =
5 means the top 5%
3
of the class), and SAT is the combined math and verbal scores on the student achievement test.
1)
How would you interpret the estimated slope for hsperc?
How would you interpret the R2?
2) 3)
Suppose that two high school graduates, A and B, graduated in the same percentile from high school, but Student A's SAT score was 140 points higher
(about one standard deviation in the sample). What is the predicted difference in

Linear Programming, An Objective, Constraints, Alternative...
In order to run an organization to its best ability, operations managers can rely on linear programming as a way to guide them in to better decision
making in a more confident fashion. Linear programming gets the most effective use out of an establishment's resources, this is always the ideal
condition for companies, and nobody wants to be knowingly throwing resources down the drain. In order for an operations manager to successfully
administer a linear programming equation, the OM must have four requirements: an objective, constraints, alternative and linearity. An objective,
because linear programming is formulated to maximize or minimize some quantity, this is also called the objective function. Aconstraint is required
because having infinity options is just not logical and the OM needs to have boundaries for the objective. The next requirement, alternative, is needed
because without an alternative there would be no question, ergo there would be no need for linear programming. The final requirement, linearity, is
expressed in the equation as an inequality, it implies proportionality and additivity. Once an OM has these four requirement they can get to work
formulating the linear program. The most common problems are product–mix problems, or two or more products produced using a limited resource.
An operations manager would start by summarizing the information that is required to solve the issue at hand. An easy way to do this is to have a
table with rows and columns

Linear Regression Line
Next the linear regression line is the line that finds the average of all x coordinates and the average of all y coordinates to create a linear formula
that shows the direction of the points and at which intensity the slope of the data is. The equation for finding the slope of the data provided is seen
on the right and the variables include, the correlation coefficient, and the standard deviation of x and y. This shows us the correlation of any two plot
points. If the slope is higher then it shows a more positive correlation and if the slope is a large negative then it shows a negative correlation. How true
the correlation is must be referred back to the correlation coefficient. The higher both of them are means the validity, reliability,... Show more content on
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The correlation coefficient was .11 which suggested that there was a slight correlation between the two variables. This was not as strong as I expected
to find because of crime rates in high density areas tending to be higher. The slope was also .021 which means even if there was a strong
correlation coefficient it would still be negligible. Density is not a contributing factor when in relation to crime rates, disproving hypothesis 1. Next
in table 10 the amount of police per square mile is substituted as the x interval where crime rate stays the same as y. The correlation coefficient was
.08 which is even less than density. This disproved hypothesis 3 because there is almost no correlation to the amount of police in an area and the
crime rate. The slope was also irrelevant at 3.6. Table 12 compared the correlation between graduation rate and crime. The correlation between the data
sets were –.624 wish is a strong correlation. When graduation rates suffer crime rates increase. The slope for this statistic is –110 which explains that
for every 1 percent from 100 a graduation rate is in a city there is an additional 110 crimes committed per 100,000 people. This is a huge slope which
shows how important education is in crime

Linear Regression Model Based On Recursive Feature...
last and the first 10 seconds of elbow flexion and extension task. And the number of flexion/extension was also counted as the 31st feature. More
detailed description of the features and their calculations are provided in Figure 3 and Table 1.
We compared features between two groups, non–frail and frail, categorized by TSFI (frail was identified when TSFI is over 0.27). One–way ANOVA
was employed for comparing two groups. And the features which have significantly difference (p < 0.05) between groups were preselected for
designing a model in order to select optimum parameters.
Optimum Feature Selection Based on Recursive Feature Elimination
Algorithm for optimum selection of parameters was based on linear regression modeling including ... Show more content on Helpwriting.net ...
4) Then, the accuracy of the redesigned model was calculated to identify frailty. 5) And, this procedure was repeated when the last feature is removed
for redesigning the model. 6) All accuracy values were compared. 7) If an accuracy value related to a feature excluded for modeling is the lowest, the
feature was eliminated. 8) After that, the same procedure was repeated without the eliminated feature by 1) to 7) until only one feature remained after
eliminating features. 9) Finally, the number of features were selected based on the performance of the model evaluated by the recursive feature
elimination for selecting the best model to identify frailty.
Evaluation of algorithm performance
Performance of the algorithm was evaluated using the validation set. We calculated the sensitivity, specificity,positive predictive value (PPV), negative
predictive value (NPV) and accuracy as follows:
Sensitivity (1)
Specificity (2)
Positive predictive value (3)
Negative predictive value (4)
Accuracy (5)
where true positive (TP) represents the number of correctly identified frail, false negative (FN) indicates the number of missed frail, true negative
(TN) refers to the number of correctly identified nonfrail and false positive (FP) stands for the number incorrectly identified as nonfrail. The area
under the ROC curve (AUC) was also calculated as an additional evaluation of the performance. In this

Linear Technology
Linear Technology
Linear Technology is a technology company that focuses on the different elements of semiconductors. The company mostly focuses on analog products
within the semiconductor portion of the electronic industry. Linear Technology was unique in their payout policy in the sense that they started with
announcing dividends and then continued onto repurchasing. Linear started dividends to gain the respect of investors as well as show that buying
shares in the company of Linear was less risky than all the other technology companies. Additionally, they repurchase stocks to offset the employee
stock options that the company had as a large component of the employee compensation, which helped Linear in the years of low or slow ... Show more
content on Helpwriting.net ...
Earnings and earnings per share are not affected by the dividend payout.
Another option Linear Technology has to exercise its excess cash balance, they can repurchase shares to increase the value of the firm. This
repurchase option is beneficial to the company and shareholders because in an open market share repurchase has no effect on the stock price. In
addition, by repurchasing shares the firm's earnings and earnings per share will increase. As shown in Exhibit B, by calculating the total numbers of
shares repurchased (total cash balance/price per share) and subtracting it from the number of shares outstanding will give us the number of shares left
outstanding after the repurchase to be 261,703,052. Exhibit B shows how this decrease in the number of shares drove up the earnings per share value
by $0.10 from $0.55 to $0.65. When the company repurchases shares instead of paying out in special dividends, the firm's value will increase and it
also allows the firm to retain its cash reserves within the company.
In general, companies pay dividends for a number of reasons. Dividends provide certainty about the company's financial well being. Many investors
prefer the steady and secure income that comes with dividends and see dividends as strength in the company and a sign of future positive earnings.
Dividend initiators tend to be large and stable firms with low growth rates

Analysis Of The Linear Model Using Microsoft Office Excel...
1. Introduction
It is acknowledged that real–world situations could be represented by well structured and formulated Linear Programming models. These mathematical
tools contribute significantly to business activities and can be used to tackle problems that constantly arise.
Franco Sounds is a company specialised in manufacturing Hi–Fi Amplifiers of top quality and its main goal, at the moment, is to minimise its overall
production and transportation cost.
This report presents a quantitative analysis which has been conducted in order to fulfil the company's goal. The primary purpose of it is to provide the
clients with the best recommendations drawn from the model's formulation and solution as well as to underline all the crucial assumptions.
The following objectives had been considered:
a.A formulation of the appropriate linear programming model for the optimal solution
b.An analysis and interpretation of the linear model using Microsoft Office Excel's Solver.
2. Definition of the problem
In three cities of Europe, such as Porto, Bratislava and Vilnius, Hi–Fi Amplifiers, are produced by Franco Sound. Each plant is respectively capacitated
with 2500, 1500 and 1000 units per month. The company's markets are located in ten different European cities as well, each of which has a demand
per month. (Table 1 – Appendix A) Its products are transported by road and ferry, via the shortest route, and the unit transportation cost, from each city
to each destination is ВЈ0.01 per/km.

Renaissance: Linear Perspective
Renaissance: Linear Perspective
Maurice Young
ART/101
July 21, 2013
Sara Shreve
Renaissance: Linear Perspective One of the major roles of the artist is to enable the viewer to see the world in a new and innovative way. This task
was a major challenge for the Renaissance artist before the 14th century on account of the artist not having the eyes to see or the skills to introduce the
world to linear perspective. Smarthistory (2013) states that linear perspective "creates an illusion of space from a single, fixed viewpoint. This suggests
a renewed focus on the individual viewer, and we know that individualism is an important part of the Humanism of the Renaissance" (para. 3).
Although beautiful and true to the style of the ... Show more content on Helpwriting.net ...
At every angle the eye seems to automatically focus on Christ even amongst other activity, "all roads lead to Christ." Da Vinci's depiction of the
religious scene comes to life to give reverence to the event.
[pic] Fig. 4 1503 Mona Lisa Leonard da Vinci Oil on poplar MusГ©e du Louvre, Paris Mona Lisa is famous for many reasons one because most of
the artwork during the Renaissance period were of biblical events and scenes. Da Vinci's Mona Lisa stood out among those artworks because it was
not of a religious text or martyr. The painting is an introduction to sfumato which is a technique Da Vinci used at the corners of Mona Lisa's lips and
the corners of her eyes exploring natural emotion along with detailed realness of her hands. Mona Lisa is an excellent example of created illusions of
space and depth within its beautiful landscape muted in the background (Totally History, 2012).
[pic] Fig. 5 1508–1512 The Flood Michelangelo Fresco Sistine Chapel, Vatican City Although a true sculpture at heart, Michelangelo (1475– 1564)
who is most famous for his sculpture of "David" and his fresco on the Sistine Chapel continues the list of painters who mastered linear perspective.
"The Flood" by Michelangelo is a painted biblical scene displayed on the ceiling of the Sistine Chapel. In this particular scene Michelangelo creates a
three– dimensional

Using Linear And Non Linear Derivatives
Nowadays, hedge become a significant element, which may determine the success of a business, to most of the multinational corporations. To form a
value maximizing hedging strategy, which is to identify the appropriate mix of linear and non–linear derivatives, turn into a major challenge towards
corporate risk managers. The article explains several questions about a multinational corporation's strategy in choosing and mixing the hedging
instruments. For instance, why do most firms use mainly linear derivatives; why do a substantially smaller number of firms use only non–linear
instruments; and what factors influence the use of linear or non–linear or the combination of the two derivatives. The researchers testified that the
external and internal of a company's business risk are the key factor towards the use of these two types of hedging instruments. In the practice,
companies face multiple business risk, which include price uncertainty and quantity uncertainty. In order to reduce the dispersion of operating cash
flows, managers have to contend with two risk sources, the price risk and the quantity risk. As quantity and price risk increase, it is more likely that
the firm would experience "over–hedging" costs from a strategy of matching a linear hedging position to an expected exposure level. Managers should
reduce the linear hedging position and substituted to some non–linear contracts. The authors illustrate the fact by providing a numerical business

Investment Strategy Linear Programming Essay
INVESTMENT STRATEGY REPORT Submitted to J. D. Williams, Inc. By Mizar Gonzalez Industrial Engineering Department Southern Polytechnic
State university 404–519–2792 February 20, 2008 EXECUTIVE SUMMARY This report is our recommendation for an optimal investment strategy
that would allow J. D. Williams, Inc. to maximize the annual yield of an investment of $800,000 in a diversified portfolio of funds. To find the
investment that would result in the greatest annual yield we have formulated a linear program that takes into account the requirements for the client of
J. D. Williams, Inc. The requirements for the investment portfolio can be found on the section titled "Problem Description" The greatest annual... Show
The client will realize an annual yield on the $800,000 investment of $94,133.33 if it is allocated in the following manner. The amount to be
invested in the growth fund must be $ 248,889. The income fund must have an investment of $ 160,000 and the money market fund must have an
investment of $ 391, 112. The solution is not surprising given the requirements for the investments. The amount invested in the growth fund yields
the greatest revenue but has to be less than 50% of the total investment and is also limited by its high risk index. The amount is greatest when
invested in the money market fund because it balances the greater risk the other funds are subjected to. Problem answers If the client's risk index
was increased to.055 the investment would be affected in the following manner. The optimal solution becomes $ 98, 800 and the funds would be
allocated in the following manner: Growth fund $ 293, 334, Income fund $ 160, 000, and Money Market fund $ 346, 667. If the original problem is
consulted and the annual yield of growth fund is downgraded to 16% the optimal solution would change to $92, 934 with the funds invested in the
following manner. Growth fund $ 293, 334, Income fund $ 160, 000, and Money Market fund $ 346, 667. The yield of the growth fund still justifies
having a large amount of the portfolio

Essay On Linear Matrix
Introduction
High performance techniques for sparse linear algebra are important within many application domains. Sparse matrix–vector multiplication (SpMV) as
a traditional and most important scientific computation algorithm on sparse matrices.
Specifically, SpMV is a core computing part of sparse linear systems and other scientific and engineering applications such as finite element methods,
magnetohydrodynamics, electrical power systems, signal processing, graphic processing, data mining, graph analytics, and so on. The performance of
SpMV can be improved using parallel computing. The implementation and optimization of SpMV on high performance machines such as GPU are
research hotspots. However, there are many challenges of compute SpMV ... Show more content on Helpwriting.net ...
SpMV is the significant importance of the solution of sparse linear systems which involved on many numerical methods such as finite difference
method, finite element method, and other engineering and scientific problems. The widespread importance of sparse matrix computation has resulted in
significant research efforts poured into implementations based on modern–day parallel computer architectures GPUs. Today, there are many
challenges of SpMV products such as load balancing among threads and the lack of existing effective memory access methods, differences on sparsity
patterns it has which cause no standard algorithm exist for all kinds of sparse matrices. This would complicate the compute of SpMV.2.2. Direct or
Iterative Approaches
According to [4] solving sparse linear systems or more specifically computing
SpMV can be done using direct methods and iterative methods approaches.
Direct algorithms give better results and performance than iterative. The most public direct solver is Gaussian elimination. Nevertheless, this concept
becomes inefficient when used with large linear systems due to memory limitations they have and they are not effective for large sparse matrices
[3][4]. This approach also goes back to the alteration of the matrix occur on the direct methods which be complicated when applied on large matrices.
This alteration also may change
the

Linear Momentum
Abstract:
This experiment studies the conservation of linear momentum through a carefully calculated collision. Two gliders were used on an air track with
different weights to demonstrate what would happen as gliders with varying masses collided into one another. Throughout the experiment the measured
values of the momentum before and after the collision were used to calculate the total momentum conserved.
Introduction:
The question of whether or not momentum is lost or conserved during a collision has been a topic of interest for many years. When two objects of equal
mass or varying masses collide what is the outcome? The gliders are used to vary the weights and the air track is used to take away all variables of
friction that could ... Show more content on Helpwriting.net ...
This velocity was multiplied by the mass of the glider to calculate both its momentum before and after the collision. There were three different
situations used in which the weights of the incident and target gliders were either equal or different. When the two masses are equal, the velocity
should be exchanged from the incident glider to the target glider. Therefore, the incident glider takes on the velocity of the target glider and the target
glider takes on the velocity of the incident glider. When the incident glider has a mass greater than the target glider, the target body takes up a velocity
in the opposite direction that is twice the incident glider.
After calculating the velocities and momentum of each collision, the percent uncertainty was calculated by gently pushing a glider through two
photo–gates and recording the velocity of the glider through each one. Using the following formula, we calculated the percent difference which is also
the percent uncertainty in our speed measurements: .
Using case 2, we calculated ratios of mass and velocity for each configuration. Using the following formula, we compared the ratio of mass and ratio of
velocity. We compared the ratios to see if momentum was conserved by seeing if the ratios agree within three times the percent uncertainty in the
velocities.
.
Table 1: Equal Mass Gliders
Incident Glider= 223.6 g

Linear Algebra Toolbox
ICSSP–2014
LINEAR ALGEBRA TOOLBOX FOR GNU RADIO
Kokila M, KaviNandhini M, Vishnu R, Gandhiraj R*, Soman K P
Center for Excllence in Computational Engineering and Networking,
*Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham ,Coimbatore,Tamil
Nadu–641112
Email.id:kokilamani024@gmail.com,mkavinandhini@gmail.com
A B S T R A C T
The goal of this paper is to bring Linear Algebra and Digital Signal Processing together for efficiently solving the applied signal processing
problems. Linear Algebra tool box is created to visualize the signal by just clicking and dragging the concerned module. In MATLAB and other
platforms, tapping of the signals cannot be done and conventional code has to be written for solving the problem. GNU Radio is a freeware software
for processing signals through which tapping of signal can be done .The module is created in GNU Radio with predefined template and the platform
used here is C++.Further, the code is glued to Python using swig. The blocks are created in order to estimate energy, check orthogonality, compute unit
normalization, find Euclidean norm of a signal and least square fitting. The end user can visualize the signal in various plots. KEYWORDS: Linear
Algebra, GNU Radio, MATLAB.
1. Introduction
GNU Radio is a free and open source software development toolkit that provides signal processing blocks to implement software radios. This free
software toolkit for

Linear Dividend
9–204–066
REV: FEBRUARY 11, 2004
MALCOLM P. BAKER
ALISON BERKLEY WAGONFELD
Dividend Policy at Linear Technology
It was April 2003 and Paul Coghlan was pulling together his notes for Linear Technology's board meeting the following day. As chief financial officer
of the Silicon Valley semiconductor company,
Coghlan was responsible for making a recommendation about whether or not Linear should increase its dividend this quarter. Coghlan and Linear's
CEO Robert Swanson were pleased with the company's third–quarter financials for fiscal year 2003, but sales and net income still remained
substantially below Linear's record levels set in 2001. In addition, the technology industry was still emerging from a recessionary environment ... Show
In addition, analog fabs could be used for 10–plus years, while digital fabs often become obsolete within three to five years.1
Research and development expenses were also modest, peaking at $102 million in fiscal year (FY)
2001. Within the analog segment of the industry, Linear competed with Maxim, Analog Devices, and
1 Goldman Sachs, "Technology: Semiconductors," February 21, 2003, p. 91.
________________________________________________________________________________________________________________
Professor Malcolm P. Baker and Alison Berkley Wagonfeld, Executive Director of the HBS California Research Center, prepared this case. HBS
cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of
effective or ineffective management.
Copyright © 2003 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1–800–545–7685, write
Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored
in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means–electronic, mechanical, photocopying, recording, or
otherwise–without the permission of Harvard

Using Hierarchical Linear Modeling Methods
As research across all fields and topics evolve, techniques on how to improve research endeavors, such as statistical modeling techniques, become
important to utilize. Hierarchical linear modeling, similarly known as multilevel modeling, has been a statistical approach that has gained attention and
improved the analysis and interpretation of research data (Osborne, 2000). Hierarchical linear modeling is a regression–based statistical analysis that
considers the hierarchical (i.e., multiple levels; nested) nature of variables within a dataset. Hierarchical data is data that has an organizational structure
consisting of units of groups (i.e., levels) that are clustered under a larger group. Therefore, the variables within a dataset may have mediating effects
and such effects can only be explored when taking into account the clustering of groups in the data.
Prior to the wide spread use of hierarchical linear analyses, data that was structured hierarchically in nature was not analyzed appropriately due to the
neglect of considering the correlated, underlying relationships among independent and dependent variables. Considering the correlated parameters of a
proposed relationship within a dataset is of importance because the relating parameters would indicate that the variables are not independent. Thus,
violating the assumption of independence and increasing the likelihood to make a Type 1 error, in addition to resulting in biased standard errors and
hypothesis tests.

Linear Inequations
The unit we finished is called Cookies. The unit is about the system of inequalities, system of equations, using constraints, graphing, finding the
feasible region, soling for the profit/optimal line, and finding the P.O.I. We began the unit learning about solving for ( x,y ) then subbing 0,0 to
find out if they were true or false and graphing them to find the feasible region. Then we learned how to solve a system of equations using two
different methods. In this unit we learned how to graph a system of linear inequalities to find and use feasible region. The first step is to get
variable y by itself if by moving the variable x to the other side or if y is already alone leave it. After you solve for (y) move on to solving for (x) and
put in the (x,y) into the graph. To determine if your (x,y) are true or false you must sub 0,0 for x and y in of your inequality. Once solve for
inequalities given, you may find the feasible region in which all inequalities are found. You can use the feasible region to find where the inequalities
would be true and where the optimal line (profit or cost line)give the max–the last point the optimal line touches and min–the first point it touches. In
this... Show more content on Helpwriting.net ...
Inequalities are similar to equations because they both are solving for (x,y), wanting y by itself, what is done to one side must be done to the other
side and both have a variety of solutions. Inequalities are different from equations because one uses an equal sign all the time while the other uses four
different signs (–– removed HTML ––) , в‰¤ and в‰Ґ inequalities can have many answers while equation can only have one answers. System of
inequalities involves using different symbols such as greater than > , less than <, greater than or equal to в‰Ґ and less than or equal to в‰¤ with
many different answers. System of equations always uses = sign and have two methods of solving for (x,y) and also can only have one

Linear Function And Slope
Linear functions are represented in slope–intercept form, y=mx+b and are plotted on a coordinate plane as a straight line. m represents the slope, or
the measure of the steepness, of a line; b represents the y–intercept, the place where the line crosses the y–axis and the value of x equals 0. Slope is
calculated by dividing the change in y by the change in x between two points, or (y_2–y_1)/(x_2–x_1 ). Slope is commonly referred to as the average
rate of change or rise/run , the change in y values (rise) over the change in x values (run). x values of the table move the point horizontally along the
x–axis; y values of the table move the point vertically along the y–axis. When points of a linear function are displayed in a table, the top number of the
slope is the average rate of change in... Show more content on Helpwriting.net ...
As the slope becomes greater, the line will have a greater steepness when graphed. Lower slopes result in a line closer to being horizontal. Positive
slopes display a line that increases its y values as x values increase while negative slopes display a line that decreases its y values as x values
increase. Linear functions are used to display situations with a constant rate of change. This can include miles driven per hour or the cost of a
service for an amount of time. Quadratic functions are represented by the equation f(x)=ax^2+bx+c and are graphed in a U shape known as a
parabola. A quadratic is a function in which x^2 is the greatest power. This curve occurs when two lines are multiplied together. If the value of a is
greater than 0, the parabola is positive and opens upwards. If a is less than 0, the graph is negative and opens downward. In order to find the vertex,
the maximum or minimum value of a quadratic, begin by finding the x coordinate, the axis of symmetry, using variables from the given equation in
(–b)/2a. Insert the x value into the function and solve to find the y coordinate of the

Linear Regressions
This project set out to determine the accuracies of predicting movie box office revenue and classifying the success. Using various techniques and
data from reputable movie sites, we were able to generate numerous prediction and classification models with their own accuracies attached. We
were guided by what previous literature have covered on the two topics we wanted to investigate. The literature review gave us a clear pathway to
follow in terms of where to collect data, what type of data to use, what techniques and methods work best and what type of results to expect. There
were a few movie sites that were used that were used regularly by researchers. The type of data that the researchers collected were wither movie
features or social... Show more content on Helpwriting.net ...
We decided to keep movies in the dataset that were English and from the USA. We also wanted to only include movies that were released in USA
and abroad. The data we did not need was filtered out and the data features that were not pre–release were removed. When it came to predicting the
worldwide box office revenue, we wanted to use linear regression and SVM. This was because many researchers had used these techniques. When
using linear regression we realised that our data needed transforming as the linear regression assumptions were violated. The Box Cox transform
we used transformed our dependent variables and worked well. The assumptions of the linear regression were met. The forward selection procedure
used helped identify which variables were good predictors in the models. Linear regression has certain assumptions, so in order to not violate those
assumptions, it is crucial to pick the best variables for the model. The best model we found using this procedure met all the assumptions and gave
good prediction accuracies. The results obtained from the linear regression and SVM models indicated that the accuracies all of the models were very
high. Linear regression and SVM achieved similar results with SVM just slightly achieving better accuracy rates As we were predicting box office
revenue using pre–release data only, the accuracies of 80% plus were a good surprise. Transforming the worldwide box office revenue

Linear Analysis Lab Report
This experiment was performed to understand the process of linear least–squared analysis as well as developing the skills to use EXCEL and having
the criteria for the best line of fit to ones' graphs. Linear least–squared analysis is a statistical method to determine a line of best fit by minimizing the
sum of squares created by a mathematical function. A "square" is determined by squaring the distance between a data point and the regression line. For
the 1st order kinetics graph, the y–axis was for the sum of ln(A). The straight line equation came out to be y=–0.0224x–0.546 while the regression line
was at a 0.99564 which was very good. For the 2nd order kinetics graph, the y–axis was for the sum of 1/A. The straight line equation came out to be
y=0.0575x+1.6104 while the regression line was at a 0.97327 which was also quite good. Each data set had 9 degrees of freedom. The F–value for the
first graph was 2057.16 and the 2nd graph was 327.67. Introduction: We used a set of data using least–squares procedures and decided from the
evidence whether the 1st or 2nd –order kinetics was a better suited match to describe the data. Paying attention to data analysis and plots for these
graphs were the most challenging. The data involves a pair of measurements of an independent variable x and a dependent variable y. In this case, our
independent variable was Time(minutes) and the dependent variable was Abs(ODU). We preformed linear least
–squared analyses using EXCEL in
order to

Linear Regression ( LR ) Equation In Excel
Introduction The general intention of this Module Two Case Assignment is to generate a Linear Regression (LR) equation in Excel. We will be
formulating this equation by exploiting data gathered by our client, the New Star Grocery Company, this organization relies that their consumer influx
correlates with their monthly sales. Thus, we will commence this assignment by deliberating upon the means, in which we developed this equation.
Development
Henceforth, in developing this equation, we gathered the data provided by our clients with respect to the monthly sales and the amount of consumers
that shopped at New Star Grocery for year one. We next capitalized on the benefits of technology by harnessing the capabilities of YouTube.
Subsequently, we discovered a video broadcasted by Tom Kleen, which the author of the video coached us on the methods behind the exploitation of
Excel 2013. As a means, to produce our Linear Regression (LR) equation for this exercise (Kleen, 2013).
Therefore, the first step we took was to highlight our data that would be captured onto the X and Y axis. This data being the total number of
consumers that purchased products from the New Star Grocery Company on a monthly basis for year one, which was annotated onto the X axis. Next,
we captured the Y axis data which was the monthly sales totals. Important to note here that at this stage it is where our LR equation launches into
formulation. Thus, we now recognize our sales will either decrease or increase determined by the quantity of consumers (Nguyen, 2017).
Upon, identifying the aforementioned data we proceeded onto creating our chart. We then moved onto to literally type our chart title and axis titles for
this particular chart. Subsequently, we advanced forward and inserted the trend line, in addition, to discovering the manner. In which, we could access
and display our equation (Kleen, 2013). Therefore, the following equation was disclosed as an integral component of this process, this modus operandi
was displayed as y=0.648x+111.65. Consequent to, us completing the previously mentioned steps. We proceeded to search for methods via utilization
of YouTube with regards toforecasting sales for year two. Immediately, we

Linear Regression
Chapter 4
Multiple Linear Regression
Section 4.1
The Model and Assumptions
Objectives
Participants will: пЃ® understand the elements of the model пЃ® understand the major assumptions of doing a regression analysis пЃ® learn how to
verify the assumptions пЃ® understand a median split
3
The Model y пЂЅ пЃў o пЂ« пЃў1x1 пЂ« ... пЂ« пЃў p x p пЂ« пЃҐ or in Matrix Notation
Dependent Variable nx1 Unknown Parameters (p+1) x 1
Y пЂЅ XпЃў пЂ«e
Independent Variables– n x(p+1)
Error – nx1
4
Questions
How many unknown parameters are there? Can you name them? How many populations will be sampled? What are conceptual populations?
5

Major Requirements for Doing a Regression Analysis
The errors are normally distributed (not Y). Constant ... Show more content on Helpwriting.net ...
Problems if VIF > 10. Some people use the condition index. In order to avoid false positives, use the COLLINOINT option.
24
Variance Inflation Factor (VIF) Example
25
Collinearity Diagnostics – Not Adjusted
26
Collinearity Diagnostics – Adjusted
27
Body Fat Example
Variables
пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® пЃ® 28
Percent body fat from Siri's (1956) equation – dependent Age (years) Weight (lbs) Height (inches) Neck circumference (cm) Chest circumference (cm)
Abdomen 2 circumference (cm) Hip circumference (cm) Thigh circumference (cm Knee circumference (cm) Ankle circumference (cm) Biceps
(extended) circumference (cm) Forearm circumference (cm) Wrist circumference (cm)
What Is Being Tested by |t|
30
continued...
What Is Being Tested by Pr >|t|
31

Partial F–Tests
H o : пЃў3 пЂЅ 0 | all other пЃў 's are in the model
32
Interpretation – The Stable Table
Do I need this leg to have a stable table?
Nope!
33
...
Nope!
34
...
Nope!
35

...
Graphs
Predicted versus Y Residual versus Independents Student versus Independents Cook's D versus Weight Leverage versus Weight
36
Moral of the Story
пЃ®
Removing more than one variable at a time is a

Essay On Linear Regression
From Table 8 and Table 3, we were quickly able to identify that the regression assumptions were not going to be satisfied and that our dependent
variable needed transforming. Looking at the plot in figure 8, we could see that the residuals of the predictions were not following a straight line.
This indicated that linear regression was not suitable for the data. This led us to transforming the worldwide box office revenue variable as shown in
Table 9. Figures 4–7 showed that the transformed variable made a huge difference. The linear regression assumptions were satisfied and the residual
plots indicated the residuals followed a linear pattern. Considering Tables 10–13, we were able to find the best variables for the regression model at...
Show more content on Helpwriting.net ...
Accuracies of around 80%–81% were obtained and this showed that the linear regression model was strong. It is clear that transforming the dependent
variable has a key role in why the linear regression prediction accuracies are quite high. Considering that only pre–release data was used to predict the
worldwide box office revenue and the best model had three independent variables, it is a very high accuracy and shows how effective linear
regression can be. Table 16 and 17 displays the results of the SVM prediction models. The same three variables that were used to predict the
worldwide box office revenue was used again in the SVM algorithm. The results obtained from the models were slightly better than the linear
regression models. SVM is more advanced then linear regression so it is not unexpected that SVM has given us a higher accuracy. The SVM models
did not exceed the accuracy of the regression by much. The accuracies of the SVM models ranged from 81% –83%. Nevertheless the accuracies of the
models predicting the worldwide box office revenues are impressive and shows movie studios how effective pre–release data is. From Table 18, we
have a good idea as to how Random Forest performs when predicting the success of movies. The accuracies of the various regression models indicate
that the accuracies range form 82–85.71%. As there are 2 categories of success, Hit and Flop, the

Introduction to Linear Regression and Correlation Analysis
Introduction to Linear Regression and Correlation Analysis
Goals
After this, you should be able to:
Calculate and interpret the simple correlation between two variables
Determine whether the correlation is significant Calculate and interpret the simple linear regression equation for a set of data Understand the
assumptions behind regression analysis Determine whether a regression model is significant
Goals
(continued)
After this, you should be able to:
Calculate and interpret confidence intervals for the regression coefficients Recognize regression analysis applications for purposes of prediction and
description Recognize some potential problems if regression analysis is used incorrectly Recognize ... Show more content on Helpwriting.net ...
sed to:
– Predict the value of a dependent variable based on the value of at least one independent variable – Explain the impact of changes in an independent
variable on the dependent variable
Dependent variable: the variable we wish to explain Independent variable: the variable used
Simple Linear Regression Model
Only one independent variable, x
Relationship between x and y is described by a linear function
Changes in y are assumed to be caused by changes in x

Types of Regression Models
Positive Linear Relationship Relationship NOT Linear
Negative Linear Relationship
No Relationship
Population Linear Regression
The population regression model:
Population y intercept Dependent Variable
Population Slope Coefficient
Independent Variable
y пЂЅ ОІ0 пЂ« ОІ1x пЂ« Оµ
Linear component
Random Error term, or residual
Random Error component
Linear Regression Assumptions
Error values (Оµ) are statistically independent Error values are normally distributed for any given value of x
The probability distribution of the errors is normal
The probability distribution of the errors has constant variance The underlying relationship between the x
Population Linear Regression y Observed Value of y for xi
y пЂЅ ОІ0 пЂ« ОІ1x пЂ« Оµ Оµi (continued)
Slope = ОІ1 Random Error for this x value

Predicted Value of y for xi Intercept = ОІ0
xi
x
Estimated Regression Model
The sample regression line provides an estimate of the

Linear Door-Openers
Linear garage door openers are the most popular brands that are available in the market today. They offer home security and garage safety with
full advanced alarm monitoring mechanism. You find in different brands for the commercial and residential protection in the market. In fact, the
linear products for garage have earned a good status due to its alarm program. It is known that it has the greatest concentration of radio frequency
with good engineering and security products. They are considered as highly reliable, highly functional and most advanced home security products.
linear garage door opener AVAILABLE WITH EASY ACCESS PROGRAMMING You can select linear garage door opener from low to high end
applications in different ranges. They can be operated easily with your favorite access digits so that you can opener and close it in an easy way with
the help of remote control. The remote has a special code that is connected to the opener. Once you enter the digits in it you can start programming the
garage opener. One thing to make clear is you can program the same code with many... Show more content on Helpwriting.net ...
This will be your pass code to unlock the door of the garage. There are only 15 seconds to enter the pass code. Find the learn button on the housing
of your linear garage door opener. This helps to operate control housing after mounting on the wall. Now press the numbers on the keypad of your
selected pass code. After entering it you can successfully operate the door to open and close. In case if it does not open, you have to repeat the
above steps once again till the garage door operates safely. Test the entered code by operating the garage door so that you can be satisfied with
function of remote control. One thing to remember is when you press the button of the remote control it blinks at a time then it means that it is
programmed in a proper way. MODELS OF LINEAR GARAGE DOOR

The Role Of Dea Is A Linear Programming Methodology...
The related research uses a variety of variables. Based on the nature of DEA, the number of variables in the model affects the results. Given imperfect
data, researchers often must make tradeoffs in selecting input and output variables. In this research, in order to verify the stability of the DEA model, a
stability test is conducted by changing the number of inputs and outputs. To get a fuller picture, four models are developed for this test. Spearman
correlation coefficients are calculated to assess the impact of the variation.
DEA is a linear programming methodology measuring the relative performance and efficiency of multiple DMUs when the production process is
composed of a difficult structure of multiple inputs and outputs [38]. A ... Show more content on Helpwriting.net ...
Details of the linear form of a CRS model may be found in Chapter 2 of Cooper [39]. The dual of the linear model reduces the number of
constraints and makes the linear problem easier to solve. It is given below [39, 40]: where is the measure of the efficiency of DMUo, the DMU in
the set of DMUs rated relative to the others; and is the dual weight assigned to DMUs. Constant returns to scale are assumed in the above model.
When the constraint is added, we have a variable return to scale model [6].
The DEA estimates of is an indicator of efficiency. It measures the distance between the observedinput/output combination and the efficiency frontier.
The Malmquist Productivity Index (MPI) is based on ideas similar to the DEA, but MPI allows comparisons between two periods. Assume the
technology at is implied in a set , where all feasible are included, i.e., .The output distance function, based on , is . The distance function increases
output as much as possible for given input and technology at time . Following FГ¤re et al. [41] and Boisso et al. [42], MPI is defined as:
Rearranging the terms in formula (4), following FГ¤re et al. [41], we obtain the subsequent formula:
The MPI is thus decomposed into two elements, efficiency change , and technological change . The distance functions in the MPI can be calculated by
linear programming methods similar to those used in the DEA. DEA constructs a production

Competency 209 Week 1 Assignment 1: Linear Algebra
Task #1: Linear Algebra
Competency 209.8.5: Linear Transformations 209.8.6: Matrices
Jennifer Moore
Western Governor's University
Part A: The rotation matrix is "a matrix, which moves a body as a rigid unit without altering its internal geometry" (Turkenburg). The work below
shows a rotation matrix that is different from the Identity Matrix, I.
WorkJustification The rotation matrix Substitute a theta that will not result in the identity matrix . Calculate sin and cos for this theta and the final
matrix to the left is the 2x2 matrix A. Matrix A is not the identity matrix.
To determine the location of point (3,2) when rotated using matrix A, matrix multiplication must be applied. The work below shows... Show more
content on Helpwriting.net ...
Once the values of the determinant are assigned, then solve for x. This will give you the fourth entry. The fourth entry of this matrix is 1, which since
matrix B was used it should have been 1.
Part C: According to Theorem 4, a 2x2 matrix A, , is considered invertible the determinant, which is , does not equal zero (Lay, 2015). The work below
shows that matrix M is invertible by having a nonzero determinant.
WorkJustification This is the given matrix M.
Since matrix M has a nonzero determinant, this matrix is invertible.
Theorem 4 also states that if the determinant does not equal zero is true, then (Lay, 2015). The work below shows that matrix M is invertible by
computing the inverse using the inverse formula for 2x2 matrices above.
WorkJustification The given matrix Using the inverse formula for 2x2 matrices, the inverse of the matrix M is .
Two matrices are row equivalent if one matrix can be changed to the other matrix by a sequence of elementary row operations. Theorem 8, The
Invertible Matrix Theorem, part b says, "A row is equivalent to the nxn identity matrix" (Lay, 2015). The work below shows the matrix M is row
equivalent to the identity matrix.

Work

Speed Control of Linear Induction Motor Essay
Executive Summary
VVVF drives used to control the speed of LIM, many industrial automation worldwide such as Trains drives, Automatic Screen Door, Moving
Walkway, Lift and Escalator, etc., varying the speed of the drive may save energy compared with other techniques for flow control.
This mid–term report prepared for detailing how to design and build the structure of LabVIEW simulation for speed control of Linear induction motor
and explaining the characteristic of V/F Speed control for Induction motor obtain the data by LabVIEW.
The design and build of simulation has two parts, part one is simulation of LED indicator for motor speed recognize and part 2 is simulation of V/F
speed control of Linear induction motor. Firstly, design... Show more content on Helpwriting.net ...
Thus, the relevant equipment for interior of MTR Station to dredge the passengers crowd was important. Wherein the many equipment of Industrial
Automation worldwide were used the V/F Speed Control of Linear Induction Motor which the Trains themselves, Automatic Screen Door, Moving
Walkway, Lift and Escalator etc. Moreover, the MTR would also produce energy in a much cleaner, more energy saving, and more sustainable manner.
The neglect of this energy source is restricting the MTR from increasing its sustainability and accessibility.
Literature search
Methods of Achieving Sustainability In this project suggests several ways to make the system more sustainable. (Variable speed appliance for energy
saving included curtain pullers, trains themselves, automatic screen door, moving walkway, lift and escalator, compressors and pumps, catapults and
launchers.) There are many discussed with respect to their "Efficiency of use and Conservation". These are defined as follows:
пЃ¬Conservation: Changing policy and/or processes of the MTR system so that it requires less energy to operate.
пЃ¬Efficiency of Use: Changing the nature of technologies within the MTR so that they require less energy to operate. While both these approaches
are valid ways of increasing sustainability, it is important to tackle the problem of sustainability from numerous angles. It can be assumed that
developments will be made to achieve both conservation and high

4.03 Linear Regression
Using MINITAB perform the regression and correlation analysis for the data on CREDIT BALANCE (Y) and SIZE (X) by answering the following.
Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret.
The scatter plot of Credit balance ($) versus Size show that the slope of the 'best fit' line is upward (positive); this indicates that Credit balance varies
directly with Size. As Size increases, Credit Balance also increases vice versa.
MINITAB OUTPUT:
Regression Analysis: Credit Balance($) versus Size
The regression equation is
Credit Balance($) = 2591 + 403 Size
Predictor Coef SE Coef T P
Constant 2591.4 195.1 13.29 0.000
Size 403.22 50.95 7.91 0.000
S = 620.162 R–Sq = 56.6% R–Sq(adj) = 55.7%
Analysis of Variance
Source DF SS MS F P
Regression 1 24092210 24092210 62.64 0.000
Residual Error 48 18460853 384601
Total 49 42553062
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI 1 4607.5 119.0 (4368.2, 4846.9) (3337.9, 5877.2)

Values of Predictors for New Observations
New Obs Size 1 5.00
Determine the equation of the "best fit" line, which describes the relationship between CREDIT BALANCE and SIZE.
The equation of the "best fit" line help describes the relationship between Credit Balance and Size is Credit Balance ($) = 2591 + 403.2 Size
Determine the coefficient of correlation. Interpret.
The coefficient of correlation is given as r = 0.752. The

Capacity Planning Using Linear Programming
Company R's Strategic Capacity Planning
Using Mixed Integer Linear Programming
ABSTRACT
Company R is experiencing 2.07 more days in producing a certain line of hotdogs. Thus, incurring 51.75% more cost of Php 5, 764. 62 per week.
Through work sampling and time study, it was found out that the sealing section of the packaging line is the bottle neck activity with a standard
processing time of 2.86 min/kg. Additional machine is needed.
Employing strategic capacity planning through formulation of mixed integer linear programming model, to meet the annual demand, the resulting
optimal machine combination was 3: Two single chamber floor model vacuum sealers and one double chamber floor model vacuum sealer. This gives
the maximum net ... Show more content on Helpwriting.net ...
Furthermore, it is desired to formulate a mixed integer linear programming model that finds the optimal combination of machines and maximizes the
net present value of such project subject to budget, demand, and available machines constraints.
V. SCOPE OF THE PROJECT To maximize the impact of the project, the study focused on the hotdog line that comprises almost 73% of the
demand being processed every week. Only the packaging line of the hotdog process was dealt with. Data collected were from the outputs of January
2011 to February 2011. Future demands for all hotdog lines were gathered. The company set policies for acquiring and maintaining equipment were
also considered. VI. REVIEW OF RELATED WORKS AND LITERATURE
According to Anderson et al, linear programming framework has served many applications in a wide variety of production, finance, marketing, and
distribution problems. The authors added that linear programming models can help managers in efficiently maximizing profits or minimizing costs
when faced with constrained resource –allocation decisions.
Several applications of linear programming in the business are scheduling, transportation, managerial and cost accounting. Profit–oriented companies
greatly evaluate capital investments they are to shelf out, since miscalculations will incur great damage to them and so the more efficient LP models
are used. Most applications are

Change In Linear
Change in linear. It's a consequence of moving forward. You can have positive and negative effects, sometimes no effects at all. Change is
unpredictable. The texts I have chosen to analyse are the poem, "All the world's a stage" by William Shakespeare which explores the change during
different parts of one man's life and the stages he goes through. The second text is the movie 'Star Trek" (2009) is a sci–fi themed movie about a
rebellious young man named James T. Kirk whose life was changed by the faith of a Starfleet captain who sees in the rash youth the special qualities
that a member of Starfleet must possess to succeed in the challenging and often dangerous world their profession puts them in. I believe that the texts I
have chosen display... Show more content on Helpwriting.net ...
In 'All the world's a stage" Shakespeare writes of a man who has a negative attitude towards growing up and life in general. This is shown through
the negative words that Shakespeare uses to describe his life. For instance: even at the earliest stage, "At first the infant, mewling and puking in
the nurse's arms" is shown being bad? Analyse the quote, write the effect. Why did Shakespeare write it like this? Due to the man's attitude toward
change and growing up he experiences life at its worst, remembering only the bad experiences. In contrast Star Treks entire franchise is based on
learning news ideas, embracing different cultured, moving forward and accepting change. It's only when you look closer you see individuals that
are struggling the accept change or try to become better. We see this in James T. Kirks beginning when he lives in Iowa and is basically a drop out
with no prospects though really smart. Kirks own change over a short period of time results in him being an overall better person, Kirk's attitude
towards change is a determined positive one, as seen in his hearing, "I don't believe in no–win scenarios" where Kirk changes a no–win test so it's
winnable. Kirk changes also had a tragic backstory, his father dying when he young and living with an abusive step–father. The previous texts character
did not have the excuse of a tragic backstory and yet he still looked on the world with disgust. Kirk

Linear Macromolecules
Deoxyribonucleic acid, or DNA, like proteins, is a linear macromolecule found in all living cells. In contrast to proteins, however, it is build up of
only 4 different types of building blocks, called nucleotides. Nucleotides are composed of a base, being either a purine or pyrimidine group, and a
2'–deoxyribosyl–tri–phosphate. The four types of bases composing the sequence of DNA are purines and pyramidines. The sugar is a 2'–deoxy ribose
which is phosphorylated at its 5' hydroxyl group. Free nucleotides contain either one, two or three phosphates representing the mono–, di–, or
triphosphate form of the nucleotide, the latter being known as dATP, dGTP, dTTP, and dCTP. Single DNA strands are not stable, but associate with a
second strand to

Linear Regression
Linear–Regression Analysis
Introduction
Whitner Autoplex located in Raytown, Missouri, is one of the AutoUSA dealerships. Whitner Autoplex includes Pontiac, GMC, and Buick franchises
as well as a BMW store. Using data found on the AutoUSA website, Team D will use Linear Regression Analysis to determine whether the purchase
price of a vehicle purchased from Whitner Autoplex increases as the age of the consumer purchasing the vehicle increases. The data set provided
information about the purchasing price of 80 domestic and imported automobiles at Whitner Autoplex as well as the age of the consumers purchasing
the vehicles. Team D selected the first 30 of the sampled domestic vehicles to use for this test. The business research... Show more content on
Helpwriting.net ...
The results of the Linear Regression analysis utilized by team D found conclusively that consumer age does not affect purchase price at Whitner
Autoplex. This test is accurate even though the sample sizes are equal because both sample sizes are significant. The results of the Linear Regression
Analysis completed above allow us to reject the null hypothesis and state conclusively that age and purchase price are in no way dependent on one
another. Based on the scatter plot and Excel's fitted linear regression, displayed above, the linear model seems justified. The low R2 of 0.359 says that
Age "explains" only 36 percent of the variation in Purchase Price.
Conclusion
Throughout the last four weeks in Research and Evaluation II, Team D has run various hypothesis tests on the Whitner Autoplex data set provided by
University of Phoenix. The data set provided information about the purchasing price of 88 domestic and imported cars as well as age of the
consumers. In week two, Team D conducted a one–sample hypothesis test comparing the national average purchasing price with that of the Whitner
Autoplex prices and answering the research question: Does the average price of automobiles sold at Whitner Autoplex dealership exceed the national
average sale price of similar automobiles? Once the test was complete, Team D accepted the null hypothesis of: Ho: u < $23,000. In week 3, Team D

Linear Programming
The development of linear programming has been ranked among the most important scientific advances of the mid 20th century. Its impact since the
1950's has been extraordinary. Today it is a standard tool used by some companies (around 56%) of even moderate size. Linear programming uses a
mathematical model to describe the problem of concern. Linear programming involves the planning of activities to obtain an optimal result, i.e., a result
that reaches the specified goal best (according to the mathematical model) among all feasible alternatives.
Linear Programming as seen by various reports by many companies has saved them thousands to even millions of dollars. Since this is true why isn't
everyone using Linear Programming? Maybe the ... Show more content on Helpwriting.net ...
I believe that every company should be using LP and I have hypothesized that linear programming increases both EPS, ROI and by definition Profit.
This research therefore assumes that a company not using LP would have made less money than one that did use LP. The sample is 28 companies
and I felt this would be sufficient as a sample size as statisticians typically recommend a minimum of 20 data points (in this case companies) in order
to test a hypothesis. The companies are located in and near the state of Delaware and also include New Jersey and Pennsylvania.
This research is not exhaustive and therefore will not be final in terms of how it is interpreted but it will assist others who wish to use much larger
samples and over much longer periods of time to refine the results and potentially prove that LP does have a consistent improvement effect on a
company's profits, ROI and EPS measures of performance. Method
I used the survey research method as the basis of my research design. Calls were made to each company that was randomly selected and for whom
their results were collected. Each company was asked the question "Can we speak to your corporate planning and decision support function". If they
were unsure of which department was meant, we would then ask "Can we speak to the key people within your organization who are responsible for

Linear Programming Processes For Optimization

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