Exponential And Weibull Model In Home Care

Exponential And Weibull Model In Home Care
As shown in previous sections of this chapter, when analysing data, a preliminary exploration may be made graphically by plotting non–parametric
estimates of H(t) and log H(t) to give an informal check on whether an exponential or weibull model might be adequate. These plots are shown in
Figure~ref{HazCumHaz}. begin{figure}[H] centering includegraphics[width=150mm]{HazCumHaz.jpeg} caption{Hazard and log hazard
functions of time to death for patients with HC and HCCA assessments.} label{HazCumHaz} end{figure} A resulting straight line through the graph
of H(t) would suggest that an exponential distribution would be the favoured choice for these data, while a straight line through the graph of log
H(t)would suggest a weibull... Show more content on Helpwriting.net ...
Predictive trees are an excellent choice for data that have features which interact in a complicated way as the models sub–divide (or partition), the data
space into smaller regions, making the interactions more manageable. This partitioning continues until model can no longer make a better model than
the one previously made for each subset of the data. One extension of the basic tree methodology is the survival tree, which applies recursive
partitioning to censored survival data. The literature presents several types tree models for censored data. These trees are a more flexible
non–parametric option to survival methods such Cox's proportional hazards methods and AFT models with more stringent assumptions. The main
difference in the various predictive trees is the splitting criteria. In an article by Segal, M.R. states that, he replaced the traditional splitting criteria for
regression trees for right–censored data with criterion based on variations of the two sample statistics, in which contrary to common practice at the
time; for which within–node separation was maximised, his algorithm preferred splits that result in large between–node
separation~cite{segal1988regression}. The default criterion in the R package "Rpart", which is maximized in each split, is the Gini coefficient. The
Gini coefficient is a measure of variation in a set of data.
... Get more on HelpWriting.net ...

Distributed Lag Model For Money Supply And Price...
CHAPTER EIGHT DISTRIBUTED LAG MODEL FOR MONEY SUPPLY AND PRICE RELATIONSHIP 8.1 Distributed Lag Model The economic
variable Y is affected by not only the value of X at the same time t but also by its lagged values plus some disturbance term
i.e.X_t,X_(t–1),X_(t–2).....,X_(t–k),Оµ_t.this can be written in the functional form as: гЂ–Y_t=f(XгЂ—_t,X_(t–1),X_(t–2).....,X_(t–k),Оµ_t) In linear
form, Y_t=О±+ОІ_0 X_t+ОІ_1 X_(t–1)+ОІ_2 X_(t–2)+в‹Ї+ОІ_j X_(t–k)+Оµ_t (8.1) Where, ОІ_0 is known as the short run multiplier, or impact
multiplier because it gives the change in the mean value of Y_t following a unit change of X_tin the same time period. If the change of X_t is
maintained at the same level thereafter, then, (ОІ_0+ОІ_1) gives the change in the mean value of Y_t in the next period, (ОІ_0 + ОІ_1+ОІ_2) in the
following period, and so on. These partial sums are called interim or intermediate multiplier. Finally, after k periods, that is =ОІ, therefore в€‘в
–’ОІ_i is
called the long run multiplier or total multiplier, or distributed–lag multiplier. If we define the standardized ОІ_i^* = ОІ_i/(в€‘в
–’ОІ_i ) then it gives
the proportion of the long run, or total, impact felt by a certain period of time. In order for the distributed lag model to make sense, the lag coefficients
must tend to zero as kпѓ п‚Ґ.This is not to say that пЃў2 is smaller than пЃў1; it only means that the impact of X_(t–k)on Y_t must eventually become
small as k gets large. The distributed lag plays

Genetic Cluster Number Of Genetic Clusters
2.5 Number of genetic clusters
To infer genetic cluster number (K) in our sample set, we used two Bayesian approaches based on the clustering method which differed in that they: a)
incorporate or not a null allele model, and b) use a non–spatial or spatial algorithm. We selected this approach because Bayesian models capture genetic
population structure by describing the genetic variation in each population using a separate joint posterior probability distribution over loci. First, we
used STRUCTURE v.2.3.3 (Falush et al., 2003; Pritchard et al., 2000), which does not incorporate a null allele model, but uses a non–spatial model
based on a clustering method and it is able to quantify the individual genome proportion from each inferred population. A previous run had been carried
out to define what ancestry models (i.e. no admixture model and admixture model) and allele frequency models (i.e. correlated and uncorrelated allele
frequency models) fit our dataset. All these previous runs were conducted with locality information prior to improving the detection of structure when
this could be weak (Hubisz et al., 2009). Run parameters of previous simulations included five runs with 50,000 iterations following a burn–in period of
5,000 iterations for K = 1–10 as number of tested clusters. Before choosing models to run our dataset we evaluated Evanno's index О”K (Evanno et al.,
2005), to identify whether different models yielded different K values, implemented in STRUCTURE HARVESTER

Chapter Four : Research Methodology Essay
CHAPTER FOUR: RESEARCH METHODOLOGY In Chapter 4 will be described the methodology which was used. In this chapter, we will explain
the reasons for choosing this methodology and give more details about this study. We will explain and present the methods that help us in this project.
An overview of the method that was used to collect the data will be given. Afterwards, the statistical concepts will be explained thoroughly. 4.1 Data
Collection This was a multicentre, prospective longitudinal cohort study. All eligible people with Dukes A
–C colorectal cancer were approached before
primary surgery from 30 NHS cancer treatment centres across the UK between November 2010 and March 2012. Questionnaires were given
whenever possible before the primary surgery which was the baseline and then after 3, 9, 15 and 24 months. Baseline questionnaires were handed to
the participants by the recruiting clinician or the research nurse and all the other questionnaires were mailed out to participants. [3] 4.2 Statistical
Methods Firstly, we would like to describe the anxiety and depression data at baseline and at 3, 9, 15, 24 months after surgery for colorectal cancer.
Anxiety was measured with STAI–state scale, depression with CES–D scale and the relevant question on the EQ–5Dв„ў assessed anxiety and
depression together. More analytically, we will present in a table (Table 2) mean STAI
–state scores and mean CES–D scores, the numbers and the

Econometrics. a Regression Analysis
Question 1: Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error
of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of
dollars and LOT is measured in acres. Model 1: OLS estimates using the 832 observations 1–832 Dependent variable: price VARIABLE
COEFFICIENT STDERROR T STAT P
–VALUE const 119.575 1.54566 77.362 <0.00001 *** lot 1.38850 0.209083 6.641 <0.00001 *** Mean of
dependent variable = 122.076 Standard deviation of dep. var. = 44.3478... Show more content on Helpwriting.net ...
If this is present it means there is a violation of the constant variance assumption. * The effect of heteroskedasticity on the OLS estimator is that it is
still unbiased. * The effect of heteroskedasticity on the OLS estimator standard errors are that the results in adjusted robust standard errors cause the
homoskedasticity results to be incorrect standard errors. Question 5: As mentioned in class, one commonly employed solution to heteroscedasticity is
to adjust the standard errors for the possible presence of heteroskedasticity, i.e. we compute the heteroskedasticity–robust standard errors, which are also
referred to as heteroskedasticity–consistent standard errors. Rerun the regression in part (2) with the OLS standard errors replaced by
heteroskedasticity–robust standard errors. Comment on the differences between the OLS standard errors in part (2) and the heteroskedasticity–robust
standard errors in this part. * With Homoskadasticity, Part 2 model, with constant variance of error term: Model 2: OLS estimates using the 832
observations 1–832 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P
–VALUE const 34.6160 4.74177 7.300 <0.00001
*** lot 1.71129 0.148643 11.513 <0.00001 *** bdrm 3.39579 1.36729 2.484 0.01320

Exchange Rate Volatility Measure And Relative Price
3.2 Exchange Rate Volatility measure and relative price An important issue in this topic is how to choose the appropriate technique to estimate the
exchange rate volatility. However, wide variety of measures have been discussing in the literature, but there is no right or wrong measure of exchange
rate volatility. Mckenzie (1999) provides a brief over–view of different methods to measure exchange rate volatility, such average absolute difference
between the previous forward and current spot rate, variance of the spot exchange rate around its trend, absolute percentage change of the exchange
rate and the moving average of the standard deviation of the exchange rate. A moving standard deviation of nominal or real exchange rate seems to be
the most commonly used method in the empirical literature. Hence, we will construct the moving average standard deviation of the monthly real
exchange rate volatility with the same spirit as Serenis and Tsounis (2014) and a moving standard deviation of real exchange rate can be expressed as:
Where R_t is logarithm of nominal or real exchange rate and m is the number of periods which can be range from 4 to 12.In this paper, we will use
the moving average of the standard deviation of exchange rate as the measure of exchange rate volatility by using the real exchange rate and the order
m is set to be 12. Koray and Lastrepes (1989) have shown that the moving average of the standard deviation of the exchange rate captures the variation
in the

Assessing Inflation Risk Of General Insurance Industries
ASSESSING INFLATION RISK IN GENERAL INSURANCE INDUSTRIES BY
MWAKAVI JACKSON K KarU/ACS/413/12
This research project is submitted to the School of Pure and Applied Science of the Karatina University in partial fulfillment of the requirement for the
degree of Science in Actuarial Science.
DECLARATION
This project as presented in this report is my original work and has not been presented for any other university award.
Candidate:MWAKAVI... Show more content on Helpwriting.net ...
Last but not least I wish to thank my family for always being there for me throughout the entire course and specifically for the encouragement they
offered me while I was working on my project. May God reward them impressively.
ABSTRACT
Inflation risk is very virtual to the non–life insurance companies as it has immerse impact on the claim reserving. This study focuses on modelling of
the claim inflation risk based on data provided by APA automobile insurance. The aim is to show empirically the impact of inflation on claim reserving
and more so to model the claim inflation which help in predicting such future uncertainty.
I will use multiple linear regression model to fit all drivers of claim inflation to obtain a linear relationship using claim inflation as dependent variable.
I'll also use ARIMA model to forecast the future claim inflation in relative to the past values of claim inflation.
Table of Contents
DECLARATION................................................................................................................i

ACKNOWLEDGEMENT.................................................................................................ii
LIST OF TABLES ............................................................................................................iii
LIST OF

The Importance Of Waiting Time To Service
Waiting times to each service for these data sets are shown in Figure~ref{WaitCAOnly} and Figure~ref{WaitCACombo}, respectively. They seem
longer for patients with larger values of assessment urgency scale; in particular, levels 5 & 6. Level 4, in most cases has the shortest waiting times
to services. Patients with an assigned value of "5" seem to have the longest waiting times. There seems to be two separate distributions for waiting
times to some departments; in which levels 5 & 6 are not given services as quickly as the rest. When a clinician was contacted for expert advice, he
speculated that AUS level 6 could be a group of patients who were excluded from further clinical intervention. I.e. the group of patients whom clinicians
... Show more content on Helpwriting.net ...
It would be expected that higher levels of AUS; being more urgent, would have the least waiting times to services.newline Table~ref{AUSDorA}
also shows that there were more patients who died in levels 5 & 6 than in the other levels. In fact, level 5 recorded the most number of deaths
proportionately. This is also counter expectation as one would expect AUS level 6 to have had the most deaths. begin{table}[H] centering
caption{Number of patients who died in each Assessment urgency level group.} label{AUSDorA} begin{tabular}{|l|l|l|l|l|l|l|l|} hline
Assessment Urgency Scale & 0 & 1 & 2 & 3 & 4 & 5 & 6 hline Alive & 4 & 141 & 28 & 360 & 171 & 100 & 109 hline Dead & 3 & 12 & 6 &
74 & 48 & 158 & 87 hline end{tabular} end{table} From Figure~ref{CAOnly} and Figure~ref{CACombo}, it is not clear whether the patients
with AUS levels 5 & 6 who have long waiting times for the Community service, say, would probably have had shorter waiting times for the Emergency
service. Therefore, pursuing such an investigation seemed prudent. Waiting times were calculated by taking the shortest time to any of Emergency,
Community, Inpatient or outpatient. If the

Definition Of Exponential Distributions
A resulting straight line through the graph of H(t) would suggest that an exponential distribution would be the favoured choice for these data, while a
straight line through the graph of log H(t)would suggest a weibull distribution for the data. Figure~ref{HazCumHaz}, for patients who filled in a
Home Care or both a Home Care and a Contact Assessment, does not support either distributions. Part results for outputs from the exponential and
weibull distributions for these data are shown in Appendix~ref{AppendixB}. These results confirm the evidence from Figure~ref{HazCumHaz} and
show that neither distributions fit the data well. Therefore, formal modelling for the 3525 patients who filled in either a Home Care assessment alone, or
Further, stepwise regression is also known to pick smaller models than desired~footnote{http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch10.pdf}.
The criteria used in the step–wise variable selection process for this study was the Akaike information criterion (AIC). AIC estimates the quality of
each model, relative to each of the other models by estimating the information lost when a model is used to represent the data. In doing so, it deals with
the trade–off between the goodness of fit of the model and the number of parameters used. When using the AIC criterion, the model with the least is the
preferred model. After using the AIC selection process, any insignificant variables which were selected by step–wise regression were manually
removed from the model. There was also a need to consider the practicality of the model. Some highly significant variables resulted in coefficients
that were very small for any practical use. For instance, if a variable resulted in a coefficient of $0.04$ then this coefficient would be $0.04 times$
the baseline hazard. This would consequently increase mortality by a factor of $e^{0.04}$ days; which is $~1.04 times$ baseline number of days.
The resulting increase is very insignificant and could be ignored. Table~ref{LowCoeffs} consists of 63 variables that were removed from

Evaluate The Impact Of Human Capital Disclosure On...
Methodology
The paper ascertained the impact of human capital disclosure on shareholders' value using panel pool, fixed and random models in Nigeria oil and
gas companies from 2004 to 2016. The work uses secondary source of data in an attempt to achieve the set objective of the study and to solve the
problem under study. The secondary data were obtained from the annual financial reports of selected listed oil and gas companies as released by the
Nigerian Stock Exchange over the period 2005 – 2014. Measurement of Variables
The dependent variable; Shareholders' value as used in this study was measured similarly to the one used by Olayiwola, (2016) which have been
widely embraced in the literature as shareholders' value and is measured ... Show more content on Helpwriting.net ...
(2)
Where:
DPS= Dividend Per Share (proxy for shareholders' value)
HCD= Human Capital Disclosure/Costs (In aggregate)
U= error term
HCR .................... +/–
Human capital costs comprises of Salaries and Wages, TrainingCost, Retirement Benefits, Medical/Health and Labour Turnover
The aggregate of the indices for measuring human capital disclosure shall be regressed against the dividend per share of companies to determine the
impact of labour cost accounting information disclosure on the profitability potential of oil and gas companies.
Relationship between dividend per share and shareholders' value of oil and gas companies
Dependent Variable: DPS
Method: Panel Least Squares
Date: 06/22/17 Time: 05:19
Sample: 2004 2016
Periods included: 13
Cross–sections included: 9
Total panel (balanced) observations: 117

VariableCoefficientStd. Errort–StatisticProb.
Salaries & Wages (N '000)0.3335280.1052313.1694810.0020
Training Cost (N '000)0.2427600.0981472.4734360.0149
Retirement benefits (N' 000)0.1716640.1978110.8678150.3874
Pension Provident Fund (N '000)–0.0157870.119144–0.1325080.8948
Medical/Health (N '000)0.1794970.0953141.8832110.0623
Labour Turnover Ratio–0.0142910.075641–0.1889300.8505
C–2.2724800.682633–3.3289940.0012
R–squared0.416917Mean dependent var2.334626
Adjusted R–squared0.385112S.D. dependent var0.611377
S.E. of regression0.479410Akaike info criterion

The Impact Of Indian Inflation On The Economy Of Nepal Essay
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Nature and Source of Data
The present study is associated with the utilization of secondary data on Money Supply and Price Level for the economy of Nepal. The data of
concerned variables are taken from various issues of Economic Bulletin of Nepal Rastra Bank. Quarterly data on money supply and price level ranging
from 1976Q1 to 2012Q2, a total of 143 periods have been used in the present study. The present study has employed the data sets of money supply and
price level transformed in logarithmic form to minimize the problem of heteroscedasticity.
Besides, the present study utilizes the quarterly data of Indian wholesale price index (WPI) transformed into logarithmic form to examine the impact of
Indian inflation on Nepalese inflation. The WPIs are taken from Reserve Bank of India (RBI).
Likewise, the present study utilizes the annual data of remittance and population growth to find the impact of remittance on inflation of Nepal. While
analyzing the impact of remittance on inflation, the annual data of remittance and inflation as well as population growth have been employed. The
political instability is taken as dummy variable while analyzing the relationship between annual inflation and remittance. The data for remittance are
taken from Economic Survey of Nepal and data associated with population are taken from International Monetary Fund (IMF).
Finally, the impact of anticipated money supply on price level is also analyzed by using

Safe Sex Behavior Paper
(is this necessary?): The results from the McEachan et al. study indicate that the standard TPB model has only a minor chance, between 13.8 and 15.3
percent, of predicating safe sex behavior (McEachan 2011). Based on these types of findings, researches have encouraged the inclusion of additional
variables to the TPB framework that may help to increase predictability (Conner & Armitage, 1998). In research undertaken by Turchik & Gidych, six
variables were added to extend the model, the first three; past behaviour, anticipated affect and moral norms, have consistently shown to increase
predictability when using the TPB, furthermore researchers have argued for their permanent inclusion in the model (Turchik & Gidycz, 2012). The last
three, sexual... Show more content on Helpwriting.net ...
They used the TPB to examine the predictors of having condoms available. Based on past studies of safe sex behavior, Jellema et al substituted a
measure of PBC for self efficacy, also adding descriptive norms which is how people perceive others to be behaving, personal norms and goal
enjoyment (Jellema et al. 2013). In this more specific study, approximately 35% of the variance in having condoms available was explained, making it
substantially more useful in predicting condoms

How Does Foreign Direct Investment Effects on Host...
1.ABSTRACT
The focus of this research is examining the affects of foreign direct investment on economic growth. Then the research reached this question: How
Does Foreign Direct Investment Effects On Host Country's GDP (Economic Growth)? Firstly, research starting with discussing the potential of FDI to
affect host country's economic growth and argues that two important objectes for FDI affects on economic growth, inflows of physical capital and
technology spillovers, and according to research the technology spillovers have the stronger effect to enhance economic growth in the host country.
Using cross section analysis with the range of ten years the empirical part of the paper reached a conclusion that FDI inflows improve economic growth
For many years FDI has allowed lots of externalities such as displacement of general knowledge, level elevation in industrilization, important
technology change in production and distribution, workforce development. Host country's competitiveness increase by FDI's services. Cause when a
country get FDI inflow that means the country attract new capital so its productivity increase. If FDI would be productive then the capital that coming
the country will be stable and long lasting. With the competition brought by FDI, in the host country's companies effected very efficially from this.
They tend to become more productive and they would be more powerful againts abroad competitor. In a country when the companies has strong
productivity, then the growth ratio in that country is higher than others. ( Baracaldo (2005)). With the FDI inflow of country, the employment
generation will increase in that country. When the country's productivity increase, employment ratio will increase and its competitiveness increase, too.
FDI contribute to the spillover of technology and improve knowledge. FDI make them efficient and with this way productivity increase(RamГrez
(2006)). Also, FDI allow bring new goods and services in host country.
Table–1 Inward FDI stocks in high and low growth developing economies High growth | Inward FDI stock 2000, dollars| Low growth economies|
Inward FDI stock per capita in 2002,dollars| China | ...| Suudi Arabia| 1.159| Korea| 918|

Regression analysis of oil price return
Contents
1.0Introduction and Motivation2
2.0Methodology5
2.1. Descriptive Statistics5
2.2 Matrix of pairwise correlation.6
3.0 Model Specification6
3.1 Linear Regression Model.6
3.2 The Regression Specification Error Test8
3.3 Non–linear models9
3.4 Autocorrelation.10
3.5 Heteroskedasticity Test10
4.0 Hypothesis Testing11
5.0 Binary (Dummy) Variables11
6.0 Conclusion13
Reference List13
1.0 Introduction and Motivation
Crude oil is one of the world's most important natural resources. Over the past six decades or so, crude oil – because of the products derived from it,
has become highly indispensable in our everyday lives. Despite being a non–renewable resource, it is still used extensively in power generation. ...
Show more content on Helpwriting.net ...
Through group brainstorming, we came up with a number of variables that theoretically should affect the price of crude oil, and we used Bloomberg
to find data on the same. Our two main criteria for a "good" variable were statistical significance and R2. We conducted a regression analysis as well as
multiple regression analysis to double check the variables we selected on the Bloomberg terminal. Moreover, so as to not omit any good variables, we
broadened our search to the Oil commodity section to find relevant industry reports and prospective variables. Through a process of rough trial and
error, and after eliminating several variables due to problems such as multicollinearity and heteroskedasticity, we finalized the three variables that are
mentioned below.

As crude oil is invoiced in USD, it is of interest to note how fluctuations in the value of the USD affect oil prices. Another of our factors is the price
of natural gas, the closest substitute as a source of energy to oil. Lastly, we seek to establish a relationship between returns in the S&P500 and oil prices.
We used monthly time–series data over a period of ten years beginning from 2005 for the purpose of this study. To avoid issues of non–stationary data,
we used oil price returns and S&P500 returns.
2.0 Methodology
Our y variable is the percentage monthly return on WTI oil spot prices. West Texas Intermediate Cushing crude oil price is typically used as the
reference spot price in the

Foreign Direct Investment Trends Of Kenya
DATA ANALYSIS AND INTERPRETATION 4.1 FOREIGN DIRECT INVESTMENT TRENDS IN KENYA. Kenya has recently experienced a
surge in foreign direct investment (FDI) following a period of substantial declines in FDI inflows near the turn of the century. Net FDI flows to
Kenya have not only been highly volatile but also generally declined in the 1980s and 1990s. Kenya's total FDI as a percentage of GDP rose from
4.21 percent in 1980 to 7.39 percent in 2000 however this declined to 5.17 percent in 2006 and currently FDI as a percentage of GDP is a 7.52
percent (UNCTAD, 2014). The investment wave of the 1980s dwindled in the 1990s as the institutions that had protected both the economy and the
body politic from arbitrary interventions were eroded. The FDI inflows to Kenya since 2008 have considerably improved from $96Million to
$514Million in 2013. In recent years, China has emerged as a key source of FDI in Kenya. Figure 1: FDI Inflows in Eastern Africa Key: Y axis–
Inward FDI Inflows in USD $ millions X axis– Years Kenya's net FDI inflows compared to its East African neighbours however is poor. In 2012,
Tanzania attracted FDIs worth US$ 1.70 billion. Uganda received US$ 1.72 billion in investment, while Kenya drew in US$ 259 million (UNCTAD
2013). Table 1:FDI INFLOWS IN EAST AFRICA. YearsUSD $ millions KenyaUgandaTanzania 2008961 383729 2009115953842 20101781 813544
20113351 229894 20122591 8001205 20135141 8721146 (UNCTAD 2014) 4.2 DATA DESCRIPTION In order

Capital Structure and Firm Performance: Case Study: Listed...
MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY––– oOo –––
HUб»ІNH ANH KIб»†T
CAPITAL STRUCTURE AND FIRM PERFORMANCE: CASE STUDY: LISTED COMPANIES IN HOCHIMINH STOCK EXCHANGE
MASTER THESIS
Ho Chi Minh City – 2010
MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY––– oOo –––
HUб»ІNH ANH KIб»†T
CAPITAL STRUCTUREAND FIRM PERFORMANCE: CASE STUDY: LISTED COMPANIES IN HOCHIMINHSTOCK EXCHANGE
MAJOR: BUSINESS ADMINISTRATION MAJOR CODE: 60.34.05
MASTER THESIS
INSTRUCTOR : PROFESSOR NGUYб»„N ДђГ”NG PHONG
Ho Chi Minh City – 2010
ACKNOWLEDGEMENT
I would like to express my deepest gratitude to my research Instructor, Professor Nguyen Dong Phong for his intensive support, ... Show more content
on Helpwriting.net ...
1 1.1 BACKGROUND .......................................................................................................................... 1 1.2 RESEARCH PROBLEMS
........................................................................................................... 3 1.3 RESEARCH OBJECTIVES

......................................................................................................... 4 1.4 RESEARCH METHODOLOGY AND SCOPE
.......................................................................... 5 1.5 STRUCTURE OF THE STUDY .................................................................................................. 5
CHAPTER 2: LITERATURE REVIEW .................................................................................... 7 2.1 INTRODUCTION
........................................................................................................................ 7 2.2 CAPITAL STRUCTURE
............................................................................................................. 7 2.3 FIRM PERFORMANCE
............................................................................................................ 11 2.4 HYPOTHESIS AND EMPIRICAL MODEL
............................................................................ 12 2.4.1. Model 1: The Leverage Model

Injury Mortality Theory
Annals of Surgery An Injury Mortality Prediction Based on the Anatomic Injury Scale––Manuscript Draft–– Manuscript Number:
ANNSURG–D–15–02018 Full Title: An Injury Mortality Prediction Based on the Anatomic Injury Scale Article Type: Original Study Keywords:
Abbreviated Injury Scale, injury mortality prediction, injury severity score, logarithm injury severity score, new injury severity score, predictor of
mortality, trauma mortality prediction model, trauma scoring. Manuscript Region of Origin: CHINA Powered by Editorial Manager? and ProduXion
Manager? from Aries Systems Corporation MiniAbstract 2 Mini–Abstract Derive the mortality rate of different AIS predot codes into the modified
coefficient (MC), the IMP, as a new feasible scoring method... Show more content on Helpwriting.net ...
White H. A heteroskedasticity–consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48:817–830. 13.
MacKenzie EJ, Rivara FP, Jurkovich GJ, et al. A national evaluation of the effect of trauma
–center care on mortality. N Engl J Med. 2006;354:366–378.
14. Shafi S, Friese R, Gentilello L. Moving beyond personnel and process: a case for incorporating outcome measures in the trauma center designation
process. Arch Surg. 2008;143:115–119. 15. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. J Trauma. 1987;27:370–378.
16. Salottolo K, Settell A, Uribe P, et al. The impact of the AIS 2005 revision on injury severity scores and clinical outcome measures. Injury.
2009;40:999–1003. 17. Stewart KE, Cowan LD, Thompson DM. Changing to AIS 2005 and agreement of injury severity scores in a trauma registry
with scores based on manual chart review. Injury. 2011;42: 934–939. Table 15 TABLE 1. Patient Demographics Patient CharacteristicsNo. of Patients
(%) Age42 (23–62)* Female407,200 (35.5) Race White, not Hispanic760,141 (66.2) Black163,860 (14.3) Hispanic128,135 (11.1) Asian19,129 (1.7)
Native American or Alaskan Native12,663 (1.1) Other64,431 (5.6) Mechanism of

Examining Genetic and Environmental Effects on Reactive...
Examining Genetic and Environmental Effects on Reactive Versus Proactive Aggression"
Introduction Prior to this study, no other research had studied the genetic and environmental influences on reactive and proactive aggression. The
purpose of this study was to explain how much genes and (shared and non–shared) environmental factors each contribute to aggression, specifically
proactive and reactive. Once a positive correlation between the two types of aggression was determined, a "sub–purpose" was to find out if any
correlation was due to another common factor, such as physical aggression. And, which factors are unique to proactive aggression and which are
unique to reactive aggression. The article defines proactive aggression, or ... Show more content on Helpwriting.net ...
However, there should also be non–overlapping/non–correlated factors contributing as well, as reactive and proactive aggression have different
predictors, associations and 'temperamental and physiological correlates'. With these new developments, the researchers carried out a separate test,
recalculating a correlation factoring in the possible overlap of physical aggression.
Methods
Sample. The participants of this study were 6–year–olds (N= 72.7 months) selected from another current study, the Quebec NewbornTwin longitudinal
Study. They initially enrolled 648 pairs of twins but the final sample size was 172 twins (55monozygotic girls, 48 monozygotic boys, 33 dizygotic
girls, and 36 dizygotic boys) after excluding data from different–sex twins and twins who were in the same class (which might indicate exaggerated
similarities between the twins if rated by the same teacher). The children's reactive and proactive aggression levels were measured using Dodge and
Coie's (1987) assessment, using informant reporting. The informant was the childrens' kindergarten teacher during the spring (enough time for the
teacher to get to know the child) in his/her preferred language (either English or French; translated twice and approved by binglingual judges). The
children's physical aggression was measured using the Preschool Behavior Questionnaire developed by Behar & Stringfield (1974),

The Effect of Savings Rate in Canada
THE EFFECT OF SAVINGS RATE IN CANADA The impact of savings rate in an economic has become a very conflicting issue in research and
among economist all over the world. This may be due to the importance of savings generally to the economic growth and development of any nation.
However, the structure of every economy cannot be generalised by a particular economics' variation because various countries have different social
security and pension schemes, and different tax systems, all of which have an effect on disposable income. In addition, the age of a country's
population, the availability and ease of credit, the overall wealth, and cultural and social factors within a country all affect savings rates within a
particular country. Therefore,... Show more content on Helpwriting.net ...
All variables used in the study have been seasonally adjusted. For the period 1983 to 2010, table 1 below shows that SAV, PCI and DR had
average values of .20366, 35.4638 and 5.4539 respectively and also had corresponding standard deviations of .024869, 6.4639 and 3.8434. SAV,
which had the lowest mean and deviation from mean, also had a coefficient of variation of .094204 while PCI and DR had coefficient of variation
of .14290 and .76027 respectively. The high coefficient of variation of DR implies that there is greater dispersion in the variable than in SAV
which has the least dispersion. Table 1: Statistical Summary Sample period :1983Q1 to 2010Q4 Variable(s)SAVPCIDR Mean.2036635.46385.4639
Standard Deviation.0248696.46393.8434 Coefficient of Variation.094204.14290.76027 As shown in table 2 below, the correlations between the
variables show that both PCI and DR were positively correlated with SAV. While PCI had a higher correlation with a value of .34810, DR had a lower
correlation with a value of .12820. This correlation indicates a predictive positive relationship between the variables. It was also observed that RCPY
and DR were negatively correlated with a value of –.86320. Table 2: Estimated Correlation Matrix of

Analysis on Inflation Regression Model
Analysis on Inflation Regression Model
Done by: Hassan Kanaan & Fahim Melki
Presented to: Dr. Gretta Saab
Due on: Tuesday, January 25, 2011
Outline: I. Introduction A. Definition of Variables B. Type of Variables II. Background and Literature Review A. Inflation and Unemployment B.
Inflation and Oil Prices C. Inflation and GDP D. Inflation and Money Supply III. Analysis A. SPSS 17 analysis B. E–Views 5 analysis IV. Conclusion
and Recommendation V. Indexes A. SPSS17 results Enter and Stepwise (Index 1) B. E
–Views 5 results Stationarity and Granger Causality (Index 2) C.
Data Collection (Index 3)
The project that the group will be handling is about Inflation and how can these four ... Show more content on Helpwriting.net ...
As cited in their article; Hamilton reached a conclusion that the increase in oil prices Granger–cause the downturn in economic activity. However in
later years: "Hamilton has proposed a more complicated measure of oil price changes: the "net oil price increase." The measure distinguishes between
oil price increases that establish new highs relative to recent experience and increases that simply reverse recent decreases" (2004).
Mahmood Arai, Mats Kinnwall, and Peter Skogman Thoursie wrote a paper on GDP and inflation mainly concerned with the effect of inflation on
GDP. The article titled "Cyclical and causal patterns of inflation and GDP growth" addressed the following problem that there were "No evidence is
found supporting the view that inflation is in general harmful to GDP growth" (2004). In part IV of their article, the authors introduced the results of
their experiment and it shows that "...potential inflation effects might be period specific rather than year specific. Another point of this exercise is that
some individual effects might, in principle, being time invariant during the entire sample period but others

A Report On Engle Granger Cointegration Test
4. Empirical Results In this section, we discuss our findings of Engle–Granger cointegration test which we applied in order to identify whether there is
cointegration relationship between dependent variable – the real non–oil GDP and independent variables – real credit to the private sector and non–oil
sector real effective exchange rate. The steps of the EG approach have been undertaken in order to obtain the long–run model that explains the
relationship between these variables. 4.1. Unit Root Test First of all, variables should be given in log levels in order to alleviate the problem of serial
correlation and the elasticity of the coefficients. The results of ADF unit root test in levels concludes that all three variables– seasonally... Show more
content on Helpwriting.net ...
Table Variable nameADF test (1% critical value =–3.557472, N=56), H0: [has a unit root]Inference t–StatisticProb.*ln_rgdp_noil_sa
–0.2028770.9314I(1) ln_rcred_to_ps –0.8740360.7892I(1) ln_reer_noil –0.5072430.8815I(1) 5% critical value =–3.557472, N=55,
t=0d(ln_rgdp_noil_sa) –11.601100I(0) d(ln_cred_to_ps) –9.0907840I(0) dln_reer_noil) –5.6490220I(0) Sample: 2000Q1:2013Q4 In the Table , d
stands for 1st difference, such that d(ln_rgdp_noil_sa) is the result of the 1st difference ADF unit root test on seasonally adjusted real non–oil GDP and
etc. The graphs below show the trend of the three series through the period from 2000 to 2013 based on level and 1st difference Augmented Dickey
Fuller unit root tests, respectively. Figure Figure ADL and Optimal Lag Selection: From General to Specific After checking for stationarity,
autoregressive distributed lag (ADL) models are estimated and the proper lag length is chosen so as to make the residuals of our model white noise.
As can be seen in the tables on ADLs in Appendix 1, all the model specifications' residuals according to the Jarque–Bera Histogram–Normality tests,
Breusch–Godfrey serial correlation LM tests, and Breusch–Pagan–Godfrey Heteroskedasticity tests are normally distributed, serially uncorrelated and
homoscedastic, respectively. It shows that all residual diagnostic parameters are satisfactory for estimating our model. Therefore, the

Case Study: ARL Bounds Test For Co-Integration
Table 3: ARDL Bounds Test for Co
–integration
Co–integration tests Bound testing for co–integrationDiagnostic tests
ModelsFStatisticsLagR2DW test 7.3806***2,2,2,2,1,10.999941.9644 5.4298**2,0,2,0,1,10.995031.9805 5.4930**0,0,2,1,2,20.984912.1880
6.5027***2,2,2,2,1,10.999941.9592 8.0358***1,1,2,0,1,10.997322.0646 4.2303*1,2,0,0,0,00.965782.2186 Critical value
Significance levelLower bounds (0)Upper bounds (1)
1% level4.0305.598
5% level2.9224.268
10% level2.4583.647
The critical value according to Narayan (2005) (Case III: Unrestricted intercept and on trend) No trend, K = 5, (***), (**), (*) denotes Significant at
1%, 5% and 10% respectively.
Table 3, represents the long–run co–integration test ... Show more content on Helpwriting.net ...
Table 5 shows the estimated ARDL error correction approach. The results illustrate most of the variables in this model as either statistically significant
or not significant at any level with an expected sign. Specifically, food production (dLFD) and annual population growth rate (dLPOP) are positive and
significant at 1% and 5% level of significant respectively. For instance, improvement in the in food production and annual population growth rate in the
short–run are related to improvement in Cereal Production. As can be seen from the results. Food production has an immediate impact on cereal
production in Nigeria. So, with this analysis, it can be stated that food production can foster growth of the cereal production and that its effects seem to
be quite lasting over time, although the magnitude is rather small. As a consequence, population growth displays a prolonged impact on the agricultural
productivity in the short–run. However, this finding agrees with the Malthusiantheory which states that population increase at a faster rate it stimulates
urgent demand for food and increases output. To be exact, improvement of food production by 1% leads to increase in Cereal production by 10.07%.
This findings consistent with the finding by Battisti&

Descriptive And Bivariate Analyses Were Conducted
Descriptive and bivariate analyses were conducted in SPSS. These correlations were then followed by a multivariate analyses in SPSS and
GeoDa. In GeoDa, a standard OLS model estimation was conducted in order to determine whether a spatial lag or error model was necessary. A
spatial error model was selected based on the statistical significance of the Lagrange Multiplier and Robust Lagrange Multiplier, and these are the
results presented hereafter. Data was screened in SPSS for any violation of assumptions prior to analysis, and the assumptions were met. No
multicollinearity was found in the SPSS or GeoDa models. Although a multicollinearity condition number of 60.83 was produced in GeoDa, which
is above the acceptable mark of 30 as an indicator of multicollinearity; however, such values are typical in trend models (Anselin, 2005). Results
Table 2 shows the SPSS bivariate results among all the variables in the present study. All variables exhibited a statistically significance with one
another at p < .001. As can be seen, all property characteristics resulted with positive and statistically significant relationships to home sales values,
with the exception of age of the sold home. Thus, sold homes with AC units and fireplaces, and larger acreage, basement square footage, and building
square footage are related with greater home sale values; whereas, sold homes that are older are related with lower home sale values. As expected,
greater rates of Blacks, Hispanics,

Estimating Exchange Rate Volatility With Garch Models Essay
Student Number: 159006900
Estimating Exchange Rate Volatility with GARCH Models
Master of Business Analysis and Finance
2016
Department of Economics, University of Leicester
159006900
1 Introduction
?The increased importance being attached to exchange rates is a result of the globalisation of modern business, the continuing growth in world trade
relative to national economies, the trend towards economic integration and the rapid pace of change in the technology of money transfer.? (Copeland,
Laurence S. 2014). In 21th July 2005, Chinese authority announced that the exchange rate system changed, from the dollar peg to the floating basket
peg system. Recently, since the volatility in the forex market is growing, which makes there is increase concern about forecasting of exchange rate
movements. (Schnabl, 2008).
It is well–known that currencies exchange rates is difficult to forecast accurately. There are a lot of models can be used to get relatively accurate
estimating result of volatility. Using those models to research currency exchange rate volatility is necessary. A timely and accurate exchange rate
estimating can provide valuable information in various fields, such as, economy, finance and polity, which is helpful to get more effective portfolio
allocation, better foreign exchange rate investment result, more accurate assets pricing and more efficient politics administration.
In international finance filed, there are many studies focus on modelling the exchanged

The Foreign Exchange Rate On International Trade And Cross...
1 Introduction
The foreign exchange rate is the rate when domestic currency (for example, Chinese yuan) is used to exchange foreign currency (for example, us
dollar). Volatility of exchange rate has been Kamble and Honrao (2014) defined as ?the risk associated with unexpected movements in theexchange rate
.? The volatility of exchange rate has great impacts on international trade and cross–country investment. The increased importance being attached to
exchange rate is a result of the globalisation of modern business, the continuing growth in world trade relative to national economies, the trend towards
economic integration and the rapid pace of change in the technology of money transfer. (Copeland, Laurence S. 2014). According to the announcement
of People?s Bank of China at 21th July 2005, Chinese exchange rate policies changed from the dollar pegged float to the floating basket peg system.
Recently, since the volatility in the forex market is growing, which makes there are increase concern about the forecasting of exchange rate movements.
(Schnabl, 2008).
It is well–known that currency exchange rates are difficult to forecast accurately. There are a lot of models can be used for get relatively accurate
estimating result of volatility. Using those models to research currency exchange rate volatility is necessary. A timely and accurate exchange rate
estimating can provide valuable information in various fields, such as, economy, finance and polity, which is interest

Revenue vs. Education in the U.S. and the United Kingdom
4.0. Analysis and Results
In this chapter, statistical results of the revenue vs. education in The USA and in The UK will be comparatively illustrated. The time period chosen
lies between 2008 and 2013 (immediately after the effects of the financial crisis started to appear, and up until today); firstly, data will be presented via
bar charts and statistical information, and will continue with a regression for each country which will illustrate the qualitative parameters of the chosen
model, and will establish the amount of influence between the "education level" and "annual income" series. The fixed model (or the Ordinary Least
Square approach) is the most suitable model for our datasets, according to the result of the Hausman test.
4.1. ... Show more content on Helpwriting.net ...
If we are to look again at the bar charts from the beginning of this chapter, we can also observe the fact that the increase of the wage, in time, is very
slow, and in the case of 2008–2009, a visible decrease had even been registered.
4.1.3. OLS Regression results
Dependent Variable: MEDIAN_ANNUAL_WAGE
Method: Least Squares
Date: 02/25/14 Time: 12:32
Sample: 1 30
Included observations: 29
VariableCoefficientStd. Errort–StatisticProb.
C (1)25545.74***4151.7136.1530610.0000
C (2)16162.59***1666.4419.6988670.0000
R–squared0.776985Mean dependent var58985.59
Adjusted R–squared0.768725S.D. dependent var25899.07
S.E. of regression12455.14Akaike info criterion21.76413

Sum squared resid4.19E+09Schwarz criterion21.85842
Log likelihood–313.5798F–statistic94.06801
Durbin–Watson stat1.147248Prob(F–statistic)0.000000
Figure 5: OLS regression results for The USA dataset
The regression results show that the R–square value is 0.776, meaning that the model could be explained by the independent value in a proportion of
77%. This is a significant value, which reveals that 77 percent of the overall factors that influence the income are related to the education level of an
individual. The p–value of the model (or probability value) is less than 0.05, which means that the model is statistically significant; the t–statistics also
reveals a significant value of 9.69,

Impact of Foreign Aid on Poverty and Economic Development...
CHAPTER ONE INTRODUCTION
This project focuses on the poverty profile in Nigeria, the foreign aids given to the nation to help alleviate poverty and how it affects the economic
development of Nigeria. According to the World Bank website, "poverty is hunger. It is lack of shelter. Poverty is being sick and not being able to see
a doctor. It is not being able to go to school, not knowing how to read, and not being able to speak properly.Poverty is not having a job, and is fear for
the future, and living one day at a time. It is losing a child to illness brought about by unclean water. And lastly, it is powerlessness, lack of
representation and freedom."
Poverty is the inability to achieve a certain minimum standard of living. It is ... Show more content on Helpwriting.net ...
By 1996, it was very obvious that urban poverty had become an increasing problem in Nigeria. For example, the number of people in poverty increased
from 27% in 1980 to 46% in 1985. it declined slightly to 42% in 1992, and increased very sharply to 67% in 1996. In 1999, estimates showed that over
70% of Nigerians lived in poverty. The government then declared in November 1999 that the 470 billion naira budget for the year 2000 was "to
relieve poverty."
By 1996, Nigeria had become the 13th poorest country in the world and occupied the 142nd rank on the human development index (HDI) scale.
(World Bank, 1996)
With the reforms, the real growth became positive but there was still a question whether the reform alleviated poverty; how far poverty was reduced.
Foreign aid is the economic help provided to communities of countries due to the occurrence of a humanitarian crisis or for the achievement of a
socioeconomic objective. There are two types of aids:
Humanitarian aid is the immediate assistance given to individuals, organizations or government for emergency relief caused by war or natural disasters.
Development aid is help given by developed countries to support economic or social development in developing countries so as to create long term
sustainable economic growth.
The sources of foreign aids include bilateral and multilateral aids. Bilateral aid is given by the government of one country directly to another.
Multilateral aid is aid from an international

A Criticism On A Migratory Animal : Breeding Season...
A criticism on "Unravelling the annual cycle in a migratory animal: breeding–season habitat loss drives population declines of monarch butterfly"
Introduction
The hemispheric migration of wildlife is in widespread decline specifically in the Monarch butterflies (Danaus plexippus). This species is known for
its long–distance migration from its non–breeding location in Mexico to the breeding sites encompassing the south, central, and north regions of eastern
North America. The main context provided in the introduction is that for predicting future population viability, it is crucial we understand the impact of
environmental and anthropogenic threats on the vital rates of the species at different times and locations in its annual cycle. Specifically, there could be
different environmental threats at the non–breeding and breeding sites and how they could influence the population could vary depending on how
sensitive certain insect stages are to these disturbances. Thus, understanding the spatiotemporal effects are crucial for modeling the dynamics of
migratory species and for guiding conservation efforts.
In addition to presenting the context for their research, the authors clearly outlined the issues that they will address. In the ecological context, the main
issue involves how environmental factors at different times could affect the species' abundance. Previous research agrees that anthropogenic effects
such as urbanization and changes in land–use (i.e. agricultural

The Effect Of Sale Price Of A House By Lots Size Of...
The purpose of this report is defining the effect on the sale price of a house by lot size of property and the number of bedrooms. Firstly, basing on
a data which contains 450 observations, then we will show the chart's relationship of the number 3 factors which are bedrooms with the sale price
of houses, the number of bedrooms with lot size, and lot size with the houses' sale price. Secondly, the model of the sale price of houses will be
given and explain how we get that model. Finally, it will give answers of some question which are does the number of bedrooms have a positive
impact on the average market price of houses with a fixed set of other characteristics? And how much? How much does the average price of a
house increase if the lot size is increased by 1 square foot, with all other characteristics held constant? As the information of question we know the
sale price of a house is depended on two factors that is the number of bedroom and lot size. There are the chart of relationship chart between the
number of bedrooms and the house's price: We can see that is positive relationship of the house price with number of bedrooms and the house price
with lot size are positive, then when the number of bedrooms or lot size are increasing then the house's price also increase. We can predict the
coefficient of the number of bedrooms and lot size in house price model that is positive. The model that shows the relationship of house's price and the
number of bedrooms and lot

Reflection Analysis
Evaluating the integral in Equation (37) using the gamma function
в€«_0^в€
ћв–’гЂ–1/x^(k–w) eгЂ—^(–(П…+i)Оё/x) dx=О“(k–w–1)/[(П…+i)Оё]^(k–w–1)
Finally, collecting all of the above evaluations and doing the necessary simplifications, the Renyi entropy of the Logarithmic–inverse Lindley
distribution can be expressed as
ОҐ_П… [f(x)]= гЂ–П… lnвЃЎ(((ОІ–1) Оё^2)/(lnвЃЎОІ (1+Оё)
))lnгЂ—вЃЎгЂ–[в€‘_(j=0)^в€
ћв–’в€‘_(i=0)^в€
ћв–’в€‘_(k=0)^в€
ћв–’в€‘_(w=0)^в€
ћв–’(в– (П…+j–1@j)) (в– (j@i))(в– (i@k)) гЂ–(в– (П…@w))
(1–ОІ)гЂ—^j гЂ–(–1)^i (Оё/((1+Оё) ))гЂ—^k ] О“(k–w–1)/[(П…+i)Оё]^(k–w–1) гЂ—/(1–П…) (38)
6. Estimation of the Parameters
In this section we introduce the method of likelihood to estimate the parameters involved and use them to create confidence intervals for the unknown
parameters.
Let x_1,...,x_n be a ... Show more content on Helpwriting.net ...
By using (Eq.42), approximately 100(1–О±)% confidence intervals for Оё and ОІ can be determined as Оё М‚В±Z_(О±/2) в€
љ(V М‚_11 ) ОІ
М‚В±Z_(О±/2) в€
љ(V М‚_22 )
where Z_(О±/2) is the upper О±–th percentile of the standand normal distribution.
7. Data Analysis
The Logarithmic inverse Lindley distribution was applied to two sets of data taken from the literature with the objective of evaluating its fit relative to
other distributions already present in the literature.
The maximum likelihood method was used to estimate the parameters. For the comparison of the models, the values of –logвЃЎL, the Akaike
information criterion (AIC), and the Bayesian information criterion (BIC), defined respectively by –2 logвЃЎL+2q and –2 logвЃЎL+q
logвЃЎгЂ–(n)гЂ—, where q is the number of estimated parameters and n is the sample size, were taken into account. The most appropriate model

corresponds to that which obtains the lowest value for –logвЃЎL, AIC and BIC
The statistics of the Kolmogorov–Smirnov test (KS), the Anderson–Darling test (AD), and the Cramer–von Mises test (CVM) are also presented, as
well as their respective p–values. These tests observe the differences between the assumed cumulative distribution function and the empirical
cumulative distribution function from the data to verify the fit of the distributions (p–value> 0.05).
The first data set to be studied was used by Nadarajah et al.

An Outage Occurrence? Based On The Expected Effect Of...
Modeling Overview The models developed for this research focused on addressing the following question: What is the likelihood of an outage
occurrence? Based on the expected effect of weather, demographics, and socioeconomic features on outages, some hypotheses to be tested are as
follows: Hypothesis #1 – Weather has an influence on power outages. Snow, rain, wind and temperature have an impactful effect on underground
cables, overhead lines, or complete electrical infrastructure that can cause electrical grids to fail. Weather also effects consumer demand, which drives
changes on the electrical grid. Hypothesis #2 – Income changes will increase/decrease energy consumption and power outages. Changes in income
have an effect on the commercial and residential developments in the area. The changes affect the max load capabilities of the electrical grid. Income
changes have a hierarchy of purchases. For example, if a group has a change of income that now allows air conditioner purchases, the electrical load
will change quickly. Hypothesis #3 – Ethnicity of a population has an influence on power outages. Certain ethnicities have generalized electrical usage.
Some groups do not use air conditioners, but others leave the windows open into the winter. That type of demographic features may be impactful
explanatory variables to help predict outages. The objective of this Capstone is to utilize data mining procedures and the business analytica framework
learned throughout

Variable Selection Via Penalized Likelihood Plays An...
Variable selection via penalized likelihood plays an important role in statistics and machine learning. In this paper, we first review some classical
methods like AIC, BIC, Mallow's Cp, then discuss some regularization methods including Lasso, adaptive Lasso, elastic–net, adaptive elastic–net and
group Lasso. We also consider the application of regularization methods in generalized linear model, Cox's model and time series analysis. At the
end, we give suggestions for future work. 1 Classical Method The problem of variable selection is always the center of statistical research. Some
classical methods like best subset selection, forward selection and backward elimination are proposed to handing massive data set. These methods
are basically based on the idea of grid search. For example, the best subset selection searches the optimal model by all possible combination of the
predictors. The forward selection find the best model by adding one predictor at each time, while the backward elimination finds the best model by
removing one predictor at each time. Although these methods are powerful and accurate, they also cost a lot. For instance, suppose our model has 10
predictors, if we run the best subset selection to get the optimal model, we need to compare 1024 combinations of the predictors. It is unrealistic to
perform such method in our real life because it time consuming. As a result, more efficient methods to solve the problem of variable selection are
urgently desired. In

Modeling Of Forecasting Inflation On Nepal Essay
CHAPTER FOUR
MODELING OF FORECASTING INFLATION IN NEPAL
4.1Introduction
Inflation is a burning economic problem in the developing countries like Nepal that brings adverse effects like loss of purchasing power of national
currency, leading to the aggravation of social conditions and living standards. This also leads to uncertainty making domestic and foreign investors
reluctant to invest in the economy. Additionally,inflation broadens the country's terms of trade causing domestic goods and services more expensive in
the market. That is why; the monetary authority of every economy should have the objective of maintaining stable price.
Inflation forecasting plays a central role in monetary policy formulation. Recent international empirical evidence suggests that with the decline in
inflation of recent years, a fairly widespread phenomenon, the combined dynamics of this variable and its potential predictors, such as money or
different measures of the output gap, has changed, and inflation has become more unpredictable. Univariate models tend to show a better forecasting
capacity than those based on various inflation theories, such as the Phillips curve. Traditionally, in industrialized countries the Phillips curve has played
a predominant role in inflation forecasting, and according to Stock and Watson (1999), Atkenson and Ohanian (2001) and Canova, (2002), it would
seem to perform better in terms of forecasting error than other alternative models. In recent years there have

What Predictive Modeling Situations Would The Aic...
3 In what predictive modelling situations would the AIC statistic be the most appropriate choice, and why?
Akaike (1973) adopted the Kullback–Leibler definition of information, I(f;g) , as a natural measure of discrepancy, or asymmetrical distance, between
a "true" model, f(y), and a proposed model, g(y|ОІ), where ОІ is a vector of parameters. Based on large–sample theory, Akaike derived an estimator for
of the I(f;g) general form гЂ–AICгЂ—_m = –2 Ln (L_m ) + 2 гЂ–.kгЂ—_m where Lm is the sample log–likelihood for the mth of M alternative
models and km is the number of independent parameters estimated for the mth model. The term, , may be viewed as a penalty for
over–parameterization. A min(AIC) strategy is used for selecting among two or more competing models. In a general sense, the model for which
AICm is smallest represents the "best" approximation to the true model. That is, it is the model with the smallest expected loss of information when
MLE's replace true parametric values in the model. In practice, the model satisfying the min(AIC) criterion may or may not be (and probably is not)
the "true" model since there is no way of knowing whether the "true" model is included among those being compared. Thus, for example, in
comparing four hierarchic linear regression models, AIC is computed for each model and the min(AIC) criterion is applied to select the single "best"
model.
The choices for the selection criterion have several model fit statistics that are useful for model

Essay On Wifa
In the second part of the thesis, the geometry of the antennas of each WiFi access point is changed to URA in order to extend the searching area into
2–D search, the well–known subspace MUSIC algorithm is used for the examination of the received spatial information, and then it estimates each
spatial spectrum in which the Azimuth Angle of Arrival (AOA) and Elevation Angle of Arrival (EOA) of all the paths at each URA WiFi access
point is located. After that, because our system is considered under very low SNR, a set of spectra at some APs might be influenced, so, a
fine–grained fusion algorithm has been added, it computes the minimum errors between each location in a known grid dimension and the estimated
AOA and EOA at every URA array,... Show more content on Helpwriting.net ...
A hence about the novel GBSA–MDL source number estimation algorithm and our contribution in this thesis was set in the first part of the introduction.
In the second part, the wireless based indoor localization which are RSS, TOA, DOA, and TDOA techniques have been introduced and the grammatical
failure of these methods for indoor localization under the effect of the high existence of multipath signals. Also, we gave a brief explanation about data
fusion multiple sensor techniques and several related works have been introduced. We motivated our novel indoor positioning algorithm by showing
the significant addition of the multiple sensor data fusion with the recently mentioned wireless based indoor techniques and the serious need of the
2–D array geometry in the next 5G. At the end, our contribution in this dissertation has been included.
Chapter 2: Literature Review: In this part, several basic concepts are introduced. We start our chapter by explaining the meaning of optimization and its
two main categories which are local optimization and global optimization, also the advantages of using the last mentioned category compared to the
first one is mentioned. Accordingly, the most know newly invented global optimization algorithms based on nature behaviors like the GA, PSO and
GBSA are introduced. In addition of that, the galaxy based search algorithm is studied well and its

The Concept Of Co-Integration Analysis
Methodology Any co–integration of these variables has been established in relation to the development of GDP using the Engle–Granger
co–integration test. These tests were applied to selected statistical data from the years 2000 to 2015. The data are quarterly and adjusted for periodic
disturbances. Granger and Engle (1991) made developments in the field of co–integration, which links long run elements of a pair or of a group of
series. It can then be used to discuss some types of steadiness and to present them into time–series models in impartially unquestionable ways. In light
of this report's objective, the concept of co–integration is used to examine how the loans provided by banks to private non–financial sector and M3
affected GDP... Show more content on Helpwriting.net ...
Checking whether it is stationary or non–stationary will be carried out by finding the p values (the level of significance is set at 0.05), which then
shows whether the null hypothesis is rejected or accepted with 95% probability. For this test, this is expressed as follows: H0: the tested series has a
unit root (non–stationary) H1: the tested series a unit root does not exist (Stationary) Since non–stationarity can be presumed for the series examined,
the option left here is to remove it by differencing the individual analyzed series. However, researches have proven that this process will result in the
loss of important information on long–term relationships between the elements of time series. For the examination of unsteady relationships between
series, the EG test was hence used, which can analyze the co–integration of non–stationary time series using the given hypotheses: H0: Test series are
not co–integrated H1: Test series are co–integrated Decisions on the relationship between time series are based on p values defined by the EG test. If
the null hypothesis (p> 0.05) is not rejected, the time series will be identified as non– co–integrated thus, for series with no long–term relationship is
irrelevant since they have been developed over the long term independently. Otherwise in cases where p <0.05 the time series will be identified as
co–integrated; i.e. for

Corporate Tax, Cost of Debt, Cost of Equity and Capital...
Corporate Tax, Cost of Debt, Cost of Equity and Capital Structure: A case study of REITs and conventional real estate firms in the UK
University of Groningen
Faculty of Economics and Business
BSc International Business
January 2013
Table of contents
1. Introduction4
2. REITs7
3. Literature Review9 3.1 Capital Structure Irrelevance9 3.2 Present Models10
4. Data and Methodology12 4.1 Regression12
5. Findings and Discussion16
6. Conclusion20
7. Appendix21
8. Bibliography30
Abstract
In January 2007 the UK adopted the globally successful real estate investment trust (REIT) regime, allowing real estate firms to adopt the REIT status
with the benefit of immediate exemption from ... Show more content on Helpwriting.net ...
Furthermore, I expect that REITs use relatively less debt for financing, because of the relatively higher cost of debt.
Already in 1958, Modigliani and Miller have pointed the discussion of capital structure towards the cost of debt and equity. According to their first

proposition, in a world of no corporate taxes and with perfect markets, financial leverage has no effect on a firm's value. In their second proposition,
they state that the cost of equity equals a linear function defined by the required return on assets and the cost of debt (Modigliani and Miller, 1958).
As negative aspects of debt, e.g. personal tax loss and bankruptcy costs however do exist in reality, Miller (1977) elaborates that leverage will either
have no or a negative effect on the firm's value, hence untaxed firms should favor equity.
Nevertheless, firms have used leverage even before corporate taxes have been introduced (Maris and Elayan, 1990). This implies the existence of some
market imperfections, which benefit the use of debt financing, thus enable a trade–off of the cost and benefits of debt resulting in an optimal capital
structure, where marginal cost equal marginal benefits.
In general, the majority of existing research is set up by taking the security issuance choice as the dependent variable and then tests empirically for
determinants based on data from one type of companies. It needs to be taken into consideration that security issue decision and capital

Nt1310 Unit 2 Case Study Betas
Looking at the betas (Exhibit1) we can clearly define two different segments in the sample. Segment1 is a "steady" segment, the customers in this group
are not much affected by both actions and external factors, and moreover, they have a positive base. Therefore, they are probably willing to continue
their relationship even without any solicitations from the company. On the other side, we find Segment2. The customers in this segment are much
more unstable as both the action and the external betas are higher values than in Segment1. Also, this segment shows a negative base, that, combined
with the strong negative external beta coefficient, means the company has to activate some kind of solicitations in order to maintain the customer,
otherwise she is going to be predictably lost.
Anyway, the members in the second segment are a minority as we can see from the parameter q1, which ... Show more content on Helpwriting.net ...
Is difficult to read properly the Likelihood Ratio Index and the Akaike Information Criterion without a comparison, but LRI is a value between 0
and 1 and the higher is better, in our model it is just over zero; AIC has no an upper limit but it is the lower the better, in our model it is over five
hundred. Though, the most significant indicator is the third, the Hit Ratio. The value itself is not that bad, it is over sixty percent, but the big concern
is that the Hit Ratio of the model is exactly the same as the proportion of retention in the model. Hence, we would reach the same accuracy in the
result just assuming that all the members of our sample are going to be retained. It could be due to the combined effects of having a high percentage
(q1) of customers in segment1 which is, as we saw in the betas analysis, the group that naturally tend to be retained. Anyway, all the three indicators
clearly show the ineffectiveness of our

Factor Affecting Performance of Stock Market
Abstract
This study examines the effects of foreign direct investment, market capitalization and adjusted on stock market using time series data from 1991 to
2011. A result shows that there is a significant relationship between foreign direct investment and stock market, as well as there is also a significant
relationship between adjusted saving and stock market but there is insignificant relationship between market capitalization and stock market. Foreign
direct investment, Market capitalization and Adjusted saving explains 90% of variation in the stock market. It is recommended that the government
can encourage FDI in Pakistan to increase its savings by taking various steps provide incentives and save foreign investors interest in a ... Show more
content on Helpwriting.net ...
The results have shown positive statistically strong relationship between FDI and market capitalization thus reflecting the complementary role of FDI
in the stock market development of Pakistan. Raza et al. (2012) investigated the role of foreign direct investment in developing host country's stock
markets and to examine whether they are related or not. The results disclosed a positive impact of foreign direct investment along with other
explanatory variables in developing Stock markets of Pakistan.
Adam and Tweneboah (2008) analyzed the impact of Foreign Direct Investment (FDI) on stock market development in Ghana. Market capitalization,
FDI, stock market development and exchange rate variable are considered and found long–run relationship between FDI and stock market development
in Ghana. Raza and Jawaid (2012) investigated the effects of foreign capital inflows and economic growth on stock market capitalization in 18 Asian
countries by using the panel data from the period of 2000–2010 and found that foreign direct investment has significant negative and economic growth
has significant positive relationship with the stock market capitalization, whereas, the results of workers' remittances is found insignificant in long run.
However, no causal relationship is found in between workers' remittances and stock market capitalization. They suggested that investor should not
idealize the inflow of workers' remittances to

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