Using Linear Derivatives To Manage Risk

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
... Get more on HelpWriting.net ...

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 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

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 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 ––) , ≤ and ≥ 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 ≥
and less than or equal to ≤ 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

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

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

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 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

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 ∑ i∈Nm (y i − 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

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  β0  β1x  ε
Linear component
Random Error term, or residual
Random Error component

Linear Regression Assumptions
Error values (ε) 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  β0  β1x  ε εi (continued)
Slope = β1 Random Error for this x value
Predicted Value of y for xi Intercept = β0
xi
x
Estimated Regression Model
The sample regression line provides an estimate of the

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

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

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 to forecasting sales for year two.
Immediately, we

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.

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 more content on Helpwriting.net ...
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 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

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

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:
 Conservation: Changing policy and/or processes of the MTR system so that it requires less energy to operate.
 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

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 Helpwriting.net ...
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 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.
1 Introduction 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

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 more content on Helpwriting.net ...
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

Linear Regression Model
Econ 122B Problem Set 2 Name(Print)______________________
Due in class Feb 6 UCI ID_____________________________ Multiple
Choice 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 self‐ 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.
‐12
b.
‐84
c.
‐168
d. ‐24
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 four‐point 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

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 ... Show more content on Helpwriting.net ...
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 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 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 observed input/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 Färe et al. [41] and Boisso
et al. [42], MPI is defined as:
Rearranging the terms in formula (4), following Färe 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

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 more content on Helpwriting.net ...
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

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 £0.01 per/km.

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 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

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
Chapter 4
Multiple Linear Regression
Section 4.1
The Model and Assumptions
Objectives
Participants will:  understand the elements of the model  understand the major assumptions of doing a regression analysis  learn how
to verify the assumptions  understand a median split
3
The Model y   o  1x1  ...   p x p   or in Matrix Notation
Dependent Variable nx1 Unknown Parameters (p+1) x 1
Y  X 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
              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 : 3  0 | all other  '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

Removing more than one variable at a time is a

Linear Technologies Essay
Case 1 Linear Technologies
Group 15
2006120001 김현섭
2006120124 이 진
2007120155 김진현
2007120262 김효선
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

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

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 Musée 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

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

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.
Work Justification 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.
Work Justification 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.
Work Justification 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

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. A constraint 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

Using Linear Derivatives To Manage Risk

Recommended

Recommended

More Related Content

Similar to Using Linear Derivatives To Manage Risk

Similar to Using Linear Derivatives To Manage Risk (19)

More from Winstina Kennedy

More from Winstina Kennedy (20)

Recently uploaded

Recently uploaded (20)

Using Linear Derivatives To Manage Risk