The Effects Of Prenatal And Delivery Care On Mothers

The Effects Of Prenatal And Delivery Care On Mothers
I exclude births of twins from this analysis. Further, I keep only mothers who were in the same residence for at
least a year before the birth of the child to ensure the proper matching of the mother with the state–level
expenditure. Table 2 shows the distribution and use of adequate prenatal and delivery care by different
population subgroups. The use of both adequate prenatal and delivery care is higher among mothers whose age at
birth was in the 20–30 age group than among those who were younger and older. It is lowest among those above
30, but they constitute the smallest proportion of mothers in the sample. On the other hand, the use rate for both
services decreases with parity. The mother's education is more relevant in the use of ... Show more content on
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Further, although poor individuals used public providers for prenatal care more than non–poor individuals, their
use rate for delivery at public facilities was almost half that of non–poor individuals. While for the full sample,
approximately 60% had delivery at home, the rate was close to 80% for the poor.
Controlling for these factors, I find a significant positive effect of public expenditure on the use of adequate
prenatal and delivery care. Table 4 shows the coefficients and marginal effects from the logit regressions on the
effect of public expenditure on adequate prenatal and delivery care. The marginal effects for a continuous
variable in a logit model report the average change in the probability of success for a unit change of the variable
among all observations in the sample. For categorical variables, they report the average change in the probability
of success for a particular category compared to the base category. The results show that the doubling of per
capita health expenditure is associated with a significant increase of 0.043 in the probability of both adequate
prenatal and delivery care. The use of such services increases with the mother's age at birth. This finding is
similar to the cross–tabulations, except for the oldest group, which constitutes a smaller proportion of the full
sample. Additionally, the use rates for both services decrease significantly with
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Methodology and Models for Residential Self Selection
METHODOLGY AND MODELS FOR RESIDENTIAL SELF SELECTION: IMPACT ON TRAVEL
BEHAVIOR Introduction: Self–selection in this context refers to "the tendency of people to choose locations
based on their travel abilities, needs and preferences" (Litman, 2005 p. 6). Sources for Residential selection are:
attitudes and social demographic traits. With residential to the latter, an example of self–selection is that low–
income and zero–vehicle households may choose to live in neighborhoods with ample transit service and hence
use transit more. In this case, it is not good transit facilities, but the households' economic constraints that have a
true and direct influence on their choice of transit model. Today urbanization is increasing rapidly with global
opportunities and economic development of the country. So, there are more number of people are migrating from
other areas to Urban. The density of the population increases, but the infrastructure like transportation facilities
have not improved that much. Then there is a cause of Traffic congestion as well as increasing the fuel
consumption. Urban areas are developing very fast and sprawl all over the sub– urban areas. Already central
areas of the cities are overcrowded, So, then the people are moving to suburban areas for living. Then the term
Residential Self selection plays an important role on the travel behavior of the individual. Travel behavior of the
person depends on the so many factors including the income level of the

A Study On Mode Choice Modeling
CHAPTER II– LITERATURE STUDY
2.1 Mode choice
Mode choice is the willingness of a person to prefer one mode of transport over the other when given various
alternatives comprising different attribute values. Here, attributes are the factors which impact the mode choice
such as their socio–economic characteristics, trip characteristics and available transport characteristics.
2.1.1 Theories for mode choice modeling
Mode choice modeling is conventionally done based on discrete choice model; here the discrete set of
alternatives is given and one must be chosen. This model is based on choosing an alternative with maximum
utilization or say choosing a mode which provided the maximum utility to the user. This utility can be found out
with the attributes having an impact on mode choice. The relationship between the attributes and mode choice
gives a value to each attribute which shows the dependency of choosing a particular mode on that attribute. The
utility equations are generally represented as linear equations; the coefficients of each attribute is taken as its
weight representing its relative value or importance in the utility equation and hence, their contribution in mode
choice. Deterministic and Probabilistic Choice Theory: The user chooses a mode as per the utility function but
there can also be some unpredictable behavior which can influence the choice. If the choice of a person in a
modal is taken as rational and he chooses a mode with maximum utility; the model is

Bus Transit And Mass Transit
1 INTRODUCTION
1.1General
In urban areas due to expansion of cities and population growth the major problems which arise are more travel
time and more travel cost. For Developing countries like India, China, Brazil etc. taking affordability and
mobility into account the policy makers induced public transport system for movements like inter–urban or
intra–urban. Various examples are Bus Transit & Mass Transit. Due to bigger city size and fixed routes of these
systems its accessibility varies for different user. These predefined fixed routes do not take into account the
whole catchment area from where the users are using. Among these users there are some users which use daily
bases are known as commuters. Such commuters access/egress the transit system by a variety of modes such as
by walking, bicycle, manual tricycle, motorized 2–3 wheeler, feeder bus services and cars. Each mode has its
utility within certain distance range (Khistey and Sriraj 1996) . Increase in city size has reflected in the utility of
various modes, utility of motorized vehicles have increased whereas the utility of non–motorized have decreased.
From socio–economic point, these include loss of employment, slow elimination of environment friendly modes
like bicycles, manual tricycles and walk and increase in population levels. The mode choice for access and egress
varies for various users but for policy makers it is very important to identify the factors which are influencing
these mode

Example Of Probabilistic Approach
Probabilistic Approach
In addition to univariate analysis and discriminant analysis, researchers also tried to explore probabilistic
prediction of corporate bankruptcy. Zavgren (1985) opined that the models which generate a probability of
failure are more useful than those that produce a dichotomous classification as with multiple discriminant
analysis.
Option to Default Methodology
Merton (1974) applied the option pricing method developed by Black and Scholes (1973) to the valuation of a
leveraged firm and relates the risk of default to the capital structure of the company. According to this model,
"The firm's equity can be seen as a European call option on the firm's assets with a strike price equal to the book
value of the firm's liabilities. The option like property of the firm's equity follows from the absolute priority rule
with respect to which the shareholders can be seen as residual claimants with limited liability. This limited
liability gives the shareholders the right but not the obligation to pay off the debt holders and to take over the
remaining assets of the firm."
Logistic Regression
Martin (1977) used the logit model for bank failure prediction. Later, Ohlson (1980) developed O – Score model
to predict failure for companies using conditional ... Show more content on Helpwriting.net ...
ID3 uses entropy to measure the values of each attribute. After that it derives rules through a repetitive
decomposition process that minimizes the overall entropy. Messier and Hansen (1988) used ID3 to derive
prediction rules from loan default and corporate bankruptcy cases. The loan default training sample contained 32
firms with 16 in each group (default or non–default). In the corporate bankruptcy case, the training sample
contained 8 bankrupt and 15 non–bankrupt firms. For the holdout samples, the rules derived by ID3 correctly
classified the bankrupt/non–bankrupt firms with perfect accuracy and 87.5% accuracy for the loan

The Relationship Between Central Bank ( Cb ) Roles And...
Methodology Paper What is the relationship between central bank (CB) roles and banking crisis of a country?
The CB can utilize its monetary instruments to bail out the insolvent banks and therefore keep the banking
system still functioning (Khan, Khan and Dewan, 2013). However, the efficiency of utilizing this monetary
instrument depends on the governance of the CB. The governance of CB consists of three essential elements,
independency, accountability and transparency (Amtenbrink, 2004; Dincer and Eichengreen, 2014). The CB role
on influencing the impact of crisis may not happen if there is a political influence from the government, such as
the legislative and the executive. The legislative and executive bodies can intervene the ... Show more content on
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On other situations, the CB may not take the subsequent steps to overcome the crisis. These conditions would not
happen if CB has implemented the accountability and transparency. Considering the aforementioned institutional
issues, the primary research question that the research is designed to answer is: Does the CB governance matter
in reducing the probability of the banking crisis? The review of the literature suggested that there are three
essential elements of CB governance: independence, accountability and transparency. So, the research's first
hypothesis is stated as follows: the more independent a CB, the less probability of a country experiences a
banking crisis. The second hypothesis is the more transparent and accountable CB, the less unstable of banking
sector in a country. Based on the research question and the developed hypothesis, the research will utilize the
quantitative approach. The ultimate goal of quantitative study is to find generalization on large number of case
based on different variables or to examine the causal phenomenon of numerical variables (King, Keohane and
Verba, 1994). The regression analysis will be used throughout the study, it is not sophisticated but it is still
powerful in the social science. Utilizing the rational choice approach as a starting point, the study will first
explore the factors that have determined the banking crisis. Based

Factors Affecting Loan Payment
Tropentag 2009 University of Hamburg, October 6–8, 2009
Conference on International Research on Food Security, Natural Resource Management and Rural Development
==========================================
Factors Affecting on loan Repayment Performance of Farmers in Khorasan–Razavi Province of Iran Mohammad
Reza Kohansal Assistant professor of agricultural economic dep., Ferdowsi University of Mashhad, Iran Hooman
Mansoori Msc student of agricultural economic dep., Ferdowsi University of Mashhad, Iran Abstract This study
investigated the factors influencing on repayment behavior of farmers that received loan from agricultural bank
by using a logit model and a cross sectional data of 175 farmers of Khorasan–Razavi province in 2008. ... Show
more content on Helpwriting.net ...
These authors simulate probabilities of default and default costs on zero–down payment loans and then compare
the results with conventional underwriting standards. They estimate that, if low–income borrowers are enticed by
zero–down payment requirements and if no adjustment for the higher default rates is made, the cost of the
implicit subsidy would amount from $74,000 to $87,000 per million dollars of lending. Quercia et al. (1995)
show that a lower loan–to–value (LTV) ratio at the time of origination (i.e., higher down payment) leads to lower
default rates for rural, low–income borrowers. These authors focus on the 1981 Farmers Home Administration
Section 502 program and show that, while contemporaneous equity value in rural low–income mortgage loans is
not associated with default, crisis events are. Van Order et al. (2000) find, however, that the default behavior of
both low– income and average–income groups is responsive to negative contemporaneous equity, while default
rates and default losses are higher for low–income borrowers. Moreover, the influence on credit risk of
individual and neighborhood income is small for LTV less than 80 percent, but it ranges from 15 up to 50 basis
points for very high LTV ratios. Enticing low–income mortgage borrowers with lower down payment
requirements thus Increases the risk of default. Oladeebo (2008) examined socio–economic factors influencing
loan repayment among small scale farmers in

Statistical Package For Social Sciences Essay
Statistical Package for Social Sciences (SPSS version 20) was used for editing, coding, scoring and tabulation of
collected data. Statistical tools like Mean, Standard Deviation, and Chi–square Test have applied for analysis and
interpretation of data. One–sample Wilcoxon test was carried out to see the significance level to reject or accept
the hypothesis. In addition, Logistic Regression model was used to identify the factors determining the
participation of women in SACCOs with 19 independent variables.
Model specification: Hosemer and Lemshew (1989) pointed out that a logistic regression has got advantage over
others in the analysis of dichotomous outcome variables. Therefore, binary logistic regression model is used to
study the participation decision behavior of sampled women. In this study, the dependent variable "Participation"
has two possible outcomes – probabilities of a woman to be a cooperative member and probability of women to
be non–member. Such variables are characterized as dichotomous and outcomes are coded as 1 and 0
respectively.
Binary logistic regression makes no assumption about the distribution of the independent variables. The
relationship between the predictor and response variables is not a linear function in logistic regression; instead,
the logistic regression function is used, which is the logit transformation of p: ................................................. (1)
Here is the "intercept" and β1, β2, β3, and so on, are the "regression coefficients" of , , and so on

Using Logit And Linear Regression Essay
Using Logit and Linear regression, we attempted to understand factors that influence students to leave their
college or University, their average GPA, and graduating in four years. We tailor our study to first–generation
students, in relation to PoC students. Because much of the differences in college success have been linked to
social class and economic background we have controlled for family income, as well as race, level of cultural
capital and student's use of university services. Our analysis of the dependent variables for student success;
students decision to leave, average GPA and four year graduation, reveal that first–generation status is a
significant factor in all three. Other variables frequently emphasized in education literature on student success
and first–generation status, such as cultural capital and peer support had less of an impact than indicated in
previous research. We analyze each dependent variable separately for independent variables in Wave 2, Wave 3,
and Wave 4. And then look for relational differences in the impact of independent variables both between
dependent variables and over different waves of the study. We present findings in which the relationship our
dependent variables to independent variables are significant to the 0.5 level of confidence. Independent variables
in our study had different effects on our dependent variables, with some positive correlations and some negative.
Finding presented below start with those affecting students

Bankruptcy: Vodafone and Samsung
Table of Contents Executive Summary 2 Background Information 2 Vodafone 2 Samsung 2 Capital Structure
Analysis 2 Vodafone & Samsung Results 3 Liquidity analysis 3 Financial Leverage Ratios 3 Possible
changes in Capital Structure – Vodafone 4 Possible changes in Capital Structure – Samsung 4 Capital Structure
Finance Theories 4 Modigliani and Miller Irrelevancy Theory 4 Pecking Order Theory 4 Trade–off Theory 4
Clientele Effect 5 Traditional View & Shareholders Wealth 5 Vodafone 5 Samsung 5 Bankruptcy Prediction
Models 5 Univariate – Beaver's Failure Ratios 5 Strengths & Weaknesses 5 Rationale 6 Multivariate –
Altman's Z–Score 6 Strengths & Weaknesses 6 Rationale 6 ... Show more content on Helpwriting.net ...
There is also no difference between the 2011 and the 2012 result, suggesting that Vodafone were not interested in
taking a larger proportion of debt INCLUDE SAMSUNG INFO AND COMPARE WITH MARKET
AVERAGES
The debt–to–equity ratio for Vodafone for the past 5 has constantly resulted in a ratio below 0.5. This is a strong
ratio for Vodafone, and permits a strong degree of protection for lenders. Again, the ratios for 2011 and 2012 are
almost identical, suggesting that Vodafone are currently happy with their capital structure. INCLUDE
SAMSUNG INFO AND COMPARE WITH MARKET AVERAGES
Finally, Vodafone has a remarkably good ratio interest coverage ratio at 5.94, although this ratio has been
sporadic over the past 5 years with a high of 23.14 and a low of 2.73. It could be argued that the current ratio, as
well as the ratio from the previous year, are actually too high, indicating that Vodafone is being too careful with
the use of debt, which may mean lower risk, but also means lower returns. INCLUDE SAMSUNG INFO AND
COMPARE WITH MARKET AVERAGES
Overall, Vodafone has extremely good financial leverage ratios, which have all also been strong for the past 5
years, indicating that Vodafone's

A Strategy For Modeling Salton Sea Basin Future Climate...
Results: A Strategy For Modeling Salton Sea Basin Future Climate Scenarios Based On Relationships Between
Evapotranspiration And Precipitation
A comparison of the averaged weather station datasets versus the historic individual weather station datasets was
made for both precipitation and Eto. The averaged monthly dataset preserved seasonality for both precipitation
and Eto as illustrated in the figures. Notably, for the months of February, March, and April, the average monthly
precipitation volume was lower than the actual weather station historic data. This occurred for two reasons: 1)
the disparity in the number of observations between the two weather station datasets and the average resulting in
the largest number of observations ... Show more content on Helpwriting.net ...
The results of the two test statistics differed at times, i.e., listing two different curves as providing the "best" fit.
In the fore–mentioned situation, a final decision pertaining to the "best" fit was made based on a visual
assessment of the figures.
Precipitation Events and Eto Levels
The importance of using monthly data instead of annual data for the Salton Sea Basin. Further, the two models:
(1) (Logit Y1,..........12) Month = Constant + X1 Prcp, and (2) (Logit Y1,..........12) Month = Constant + X1 Eto,
established that some months were significantly different than others concerning the amounts of precipitation
and Eto, respectively. The relationships between the duration of precipitation events ('CatEvent') on the volume
of precipitation ('PrcpAmt' and 'CatVol'), and also on Eto volumes. A plot of the precipitation and Eto
observations, percentage or percentage of observations of a given magnitude, respectively, by month was also
made.
The negative relationship between precipitation and Eto was tested statistically using two multinomial logistic
regression models and one binomial logistic regression model, respectively: (3) (Logit Y0,...4) PrcpAmt =
Constant + X1 Eto, (4) (Logit Y0,....5) CatEvent = Constant + X1 Eto, and (5) (Logit Y0,1) RainEvent =
Constant + X1 Eto. The

ADL Case Study Summary
In the field of occupational therapy, activities of daily living, more commonly referred to as ADL's, are a major
component for assessing a client's issues and developing a treatment plan. In the study "ADL Differences in
Individuals with Unilateral Hemispheric Strokes", authors Patricia Rexroth, Anne G. Fisher, Brenda K. Merrit
and Jeff Gliner try to gain a better understanding of ADL patterns in unilateral hemispheric stroke victims. Their
purpose in designing this study was due to the inconsistent research findings from other studies in the
relationship between age, gender and ADL ability in people with left cerebrovascular accident [(L) CVA] and
right cerebrovascular accident [(R) CVA]. The authors' hypothesis as stated from the article ... Show more
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The means and standard deviations were used to reach a conclusion of the effect of gender, age and side of
stroke. For example, the data for comparing men's and women's ADL motor ability [Men: Mean (M) = 0.96,
Standard Deviation (SD) = 1.08; Women: M = 0.77, SD = 1.08] indicates that men have significantly higher
ADL motor abilities, while women have higher ADL process abilities (Men: M = 0.74, SD = 0.89; Women: M =
0.81, SD = 0.88). This process was used for analyzing the impact of age and side of CVA

Multivariate Statistical Analysis : Natural And Physical...
Multivariate Statistical Analysis
Statistical methodology designed to obtain information from data sets that include simultaneous measurements
on many variables is called multivariate statistical analysis (1). Multivariate statistics helps to study how the
variables are related to one another, and also how they work in combination to differentiate between the cases on
which the observations are made. Several research disciplines such as biology, medicine, environmental Science,
Psychology, Sociology, Economics, Education, Archaeology, Anthropology have broad applications of
Multivariate statistical analysis (2).
Numerous interesting research questions about natural and physical processes are so complex that they require
multivariate ... Show more content on Helpwriting.net ...
The usual regression method does not work when we are given a binary response variable. In these situations we
should think about using the logistic regression (4).
Considering the following model estimated regression equation (equation 1) It is called as simple Logistic
regression, because there is only one predictor and also the exponential function is never "0" or negative.
There are some other models available for dichotomous and non–dichotomous categorical outcomes. Probit
regression: A model used for binary outcomes, but instead of the logit specification, the probit uses the
cumulative distribution function for a standard normal distribution. Multinomial Logistic Regression: A model
used for outcomes that are nominal, e.g., blood type (A, B, AB, O). Ordinal Logistic Regression: A model used
for outcomes that are ordinal, e.g., Likert scale questionnaire (excellent, very good, good, fair, poor).
Simple logistic regression application involves one dichotomous variable and one independent variable. Multiple
logistic regression application involves when there is a single dichotomous outcome and more than one
independent variables (4) (5).
Applications in Bio–Medical Research:
Logistic regression is one of the widely used tool in fields such as bio–medical research, medical, epidemiology,
social sciences, engineering, ecology, psychology and marketing. For instance logistic regression can be used to

The And Do Not Deal With Time Series Application
cross– sectional and do not deal with time series application. The official model specification is as follows:
Socialhappy= βo
+β1socialmedia+β2goodroomate+β3studyfrisat+β4academichappy+β5alc+β6tv+β7sports+β8outdoors+β9earnings
The main focus of this study was to determine how social media use impacts social happiness. The social
happiness question is phrased as, "How happy are you with your social life at Colby? [with the following
options] Very happy, Mostly happy, Indifferent, Not happy, Miserable" I transformed social happiness into a
binary variable. If a participant selected "Very Happy" or "Mostly Happy" they are considered socially happy at
Colby. One challenge I ran into with this study was the uneven categorical data responses from the survey. The
uneven increment increases made analysis more difficult in that you could no longer describe how a one–unit
increase in Bk affects social happiness. For example the survey asked, "How many hours each day do you
typically spend on social media (Facebook, Snapchat, Twitter, etc.)" The listed responses were None, I don't use
social media, 1 to 2, 2 to 4, 4 to 6, 6 or more. I corrected this particular issue by recoding social media to grow
by two–hour increments. Based upon how I observe my own peers relationship with social media, I predicted a
negative relationship with social happiness. The added dictionary definition of FOMO also gives validity to this
claim.
Other variables also suffered from irregular increments of

Essay about Case Study
1. Table 8.1 shows results of an eight–center clinical trial to compare a drug to placebo for curing an infection. At
each center, subjects were randomly assigned to two groups. Table 8.1 Clinical Trial Data for Problem 1 a.
Perform two different tests (Breslow–Day, and likelihood ratio test) for whether the drug effect on curing an
infection is the same over eight centers. i) Breslow–Day test: The B–D test of homogeneity tests for whether the
drug effect on curing an infection is the same over eight centers gave a chi2 = 8.0 with df=7 and p–value =
0.333. Therefore since the p–value is > 0.05, we fail to reject the null that the drug effect on curing an
infection is the same over the eight centers. The drug effect ... Show more content on Helpwriting.net ...
What is your conclusion with regard to the effect of the drug on curing the infection? What is the odds ratio
using the CMH method? The OR for curing the infection comparing drug with placebo, adjusting for the centers
is 2.175, with a p–value of 0.011 which is less than 0.05 and therefore significant. We can conclude that the drug
is 2.175 times better at curing the infections after adjusting for centers compared to the placebo. Using the CHM
Method: From the CMH method above, the combined OR=2.13, with a p–value of 0.0115 which is significant at
the 0.05 level. Therefore the drug is 2.13 times more effective at curing the infection after adjusting for centers
compared to the placebo d. Perform a new logistic regression to find the odds ratio for curing the infection
without adjusting for the centers. Which measure, adjusted, or unadjusted odds ratio, do you prefer to use when
you investigate the drug effect on curing the infection? From the output above, the OR without adjusting for
centers is 1.5 with a p–value of 0.108 which is not significant at the 0.05 level. Therefore, I will prefer to use the
adjusted measure when investigating the drug effect on curing the infection. e. (Extra Credit) Perform a
likelihood ratio test on whether the treatment is different among the centers, using the deviance measure. data
medtreatment; input center drug treatment count; datalines; 1 1 1 11 1 1 0 25 1 0 1 10 1 0 0 27 2 1 1 16 2 1 0 4

The Determinants Of Women Participation
5.4 Determinants of Women Participation in SACCOs.
Women's equal participation in cooperatives is both a women's right and important for sustainable and people–
centered development. If cooperatives are gender–responsive and inclusive, they can help women to overcome
gender specific constraints to improve their self–confidence, knowledge, leadership skills, income, and access to
agricultural inputs, social networks, and position in value–chains. When women are more economically and
socially empowered, evidence shows that there are direct and positive impact on women's household and
community decision–making power and on access to and control over productive assets. These changes lead to
improved household nutrition, food and income security, broader development outcomes, and a more integrated
production of both food and cash crops (Qui–sumbing, 2003; FAO, 2011; CSA and ICF International, 2012).
The success of cooperative is strongly related to the target group and member participation (Tips, 1986; Yu,
2009). Member's commitment to working together and the sense of trust and mutual respect between the
managers and members are also important factors (Fulton, 2004). Despite the availability of cooperative societies
and efforts of government at all levels, a significant proportion of rural women are either unaware of the
existence of such co–operatives or lacking the basic socio–economic characteristics that form the prerequisite for
participation in such activities (Idrisa et

Analysis Of The Logit Model
The regression results of the logit model in Table 4.42 are depicted by the regression Coefficient, robust standard
errors, Wald statistic and p–value. The logit model generated a chi–square value of 169.95 and p–value of 0.0001
which was statistically significant because the p= value was far less than (0.05). The results indicated that time
schedule supervision had a significant level of 0.001 (–– removed HTML ––) 0.05. This called for the rejection
of the null hypotheses (H01, H02 and H03) while accepting the null hypothesis H04. The alternative (Ha1) was
adopted for the first hypothesis that; there is a significant relationship between time schedule supervision and
financial performance of women funded projects in Nakuru County. In ... Show more content on Helpwriting.net
...
Quality Management was not a significant predictor. Exp(B) value indicated that when the significant predictors
are raised by a unit their odds ratio are raised by exponent value respectively.
For interpretation of Exp (B) the results of values from the regression analysis are taken to account, and if the
value (>1), then the odd of an outcome occurring increases, and if the figure is (< 1), any increase in the
predictor variable leads to a drop in the odd of the outcome occurring (Mbachu, 2012). From the table time
schedule supervision as a predictor, has a value of Exp (B) of 2.843 and implies that when the predictor is raised
by one unit, will increase level of financial performance by 2.843 times. The scope predictor had an Exp (B) of
1.775 which implies that, an increase by one unit in proper scope management results in the increase by 1.775 in
the outcome, in this case the level of project's financial performance. Cost control predictor had an Exp (B) of
2.195 which implies that, an increase by one unit in proper cost control results in the increase by 2.195 in the
outcome, in this case the level of project's financial performance. The project quality management predictor had
an Exp (B) of 1.231 which implies that, an increase by one unit in good project quality management results in the
increase by 1.231 in the level of project's financial performance.
There are two hypotheses that were tested in relation to the overall fit of the model:
H0: The model is a

What Is The Role Of Bridge As Anchor Point Model
LITERATURE OVERVIEW This report gives a summary of the role of bridges as anchor points in route choice
modeling. This work expands upon the prospect that people dispense more substantial amounts of significance to
some specific highlights of the route, so–called anchor points. By this report, we contend that the thought of both
route level attributes and anchor points would upgrade the behavioral part of route choice models and
additionally their estimation and forecast capacities would be of great help. Past route choice models have either
overlooked the impacts of anchor points (route based models) or have given a restrictive regard for their
belongings and disregarded the behavioral of anchor–based models. The implications of bridges ... Show more
To underscore the signiﬁcant part of anchor points, they have additionally set a theoretical subjective anchor
based route choice modeling scheme. Furthermore, Kazagli and Bierlaire (2015) contend that drivers portray
their course network by utilizing a small grouping of Mental Representation Items (MRIs) for example, anchor
points or bits of the structure as opposed to utilizing a connection of all representation. The OD study of
Montreal, in which the creators had just access to announced overpass bridge was a depended information
investigated by Habib et al. (2013). They have considered the briefest way of calculation, in light of speed radar,
to create one way for every bridge for every OD combine, containing the decision set of network choices.
METHODOLOGY USED In 1973 Ben–Akiva proposed the Nested Logit (NL) detailing and that turned out to
be predictable with the stochastic utility maximization hypothesis by McFadden in 1978. In 1991 Ben–Akiva and
Bolduc first proposed Logit Kernel, which is a blend of Probit and Logit models. The arbitrary part of its utility
capacity is made out of a Probit–like term, which catches the interdependencies among choices, and an i.i.d.
Gumbel circulated unpredictable part. In 1984 McFadden proposed the interdependencies between choices can

The Dependent Variable, Perception Of Safety On Campus Essay
The dependent variable, perception of safety on campus, was measured by an item on the questionnaire asking
students to indicate their level of satisfaction on feeling safe on campus. Respondents rated their satisfaction on a
scale from 1 (Very satisfied) to 5 (Very dissatisfied). Responses to this item were recoded into three categories.
Very satisfied and satisfied were combined and recoded 0 (Satisfied). Undecided was recoded 1 (Undecided).
Very dissatisfied and dissatisfied together were recoded 2 (Dissatisfied). Recoding was deemed necessary for
easier interpretation of findings (See Table 1). Independent Variables The variables, perception of officers'
fairness and impartiality, fear at night, and violence in residence halls, were operationalized by an item on the
questionnaire asking students to indicate their satisfaction or agreement. Responses to this item ranged from 1
(Very satisfied) to 5 (Very dissatisfied). Similarly, responses were recoded into three categories 1 (Satisfied – a
combination of both satisfied and very satisfied responses), 2 (Undecided) or 3 (Dissatisfied – a combination of
both dissatisfied and very dissatisfied responses). Gender was a dichotomous variable, with male surveyees
coded 1 and female surveyees coded 0. Race or ethnicity was indicated by an item on the questionnaire that
asked respondents to specify which group they considered themselves a member of Caucasian/White, African
American/Black, Hispanic (Mexican, Puerto Rican, etc.),

The Deviation Of Bid Prices
deviation of bid prices (a commonly used measure of dispersion in literature), quality weighted BPD, distribution
weighed BPD, and BPD with legitimate bids as alternative measures to check the robustness of the measurement
of BPD. We also note that measurement error is not a concern for all other variables as our data was directly
pulled from the firm database and the data recorded actual transactions and the buyers' and freelancers' behaviors
in the online labor market.
4.3.2 "Buyer Contracting" Model Specification
Since our dependent variable (buyer contract) is a binary variable, we estimate a logit model with robust standard
errors (Equation 3). is the latent utility a buyer i infers from project j. captures the effect of BPD on the buyer's
contract decisions. is a vector of the average freelancer's characteristics like average experience, mean quality
rating (control variables that affect the DV), and quality dispersion (potential confounding factor that affects both
the DV and BPD). The freelancer's average experience and mean quality rating represents the average quality of
the freelancers that a project attracts. A buyer would have more high–quality freelancers to choose from with a
higher average experience and mean rating, which may facilitate the buyer's contracting decision. Freelancer
quality dispersion is measured as the standard deviation of all freelancers' rating (or experience), which
represents the diversity of choices a buyer has; thus, it is

Mexican Non-Citizen Destination: A Case Study
To study the destinations of Mexican non–citizen destination, we can suggest using the Synthetic Control
Method, the methodology proposed by Abadie (2010). This method relies on the probability that a Mexican non–
citizen moves to a different state – and their predictors in order to generate a convex combination of states that
serves as a better control group.
A pooled sample data can be obtained from the ACS (American Community Service), from 2000–2012. This is a
time frame where tough interior immigration enforcement is seen. The ACS contains data on migration status
during the last year, present state of residence and country/state of residence in the last year.
To disentangle the effects of LAWA and SB 1070, we can focus to look at time frames of pre–LAWA, post–
LAWA, pre–SB 1070 and post–SB 1070. ... Show more content on Helpwriting.net ...
We can ignore the states not having passed immigration policies – an outcome much like the next point. We then
identify a convex combination of states from that donor pool that most closely resembles Arizona in terms of
pre– intervention outflows of Mexican non–citizens and a number of predictors. We compare Arizona to states
that exhibited similar labor market trends, including a thriving construction sector (as seen before), prior to
LAWA and SB 1070. These can be Nevada, California, New Mexico and Texas.
The main destinations of non–citizen Mexicans are: California, Texas, New Mexico, Nevada, Utah and
Colorado. Setting up a probability of movement of such non–citizen Mexicans across these 6 destinations, we set
another broad destination: rest of the US. It is important to note here that a multinomial logit assumes that adding
another destination does not affect the relative odds of choosing any of the US destinations. Our estimates should
provide a set of probabilities for the 8 mobility choices as discussed

Criminological And Gang And Non Gang Involvement Across...
Criminological and Gang MembershipApril SmolkowiczGeorgia Gwinnett College Criminological and Gang
MembershipSocial scientists have researched and documented empirical findings of the many one–of–a–kind
influential factors of formative years gang and non–gang involvement across the United States. While it is now
not feasible to predict whether or not a younger man or woman will be a section of a gang, possession of certain
danger elements can extend the probability.Social Leaning Theory Article One The motive of this paper is to
analyze adolescence gang and non–gang attitudes and behaviors with the social learning idea factors and its
possibility of predicting youths self–reporting of gang membership, explicitly with the ethnicity of "Hispanics'
and Anglos, of eight–grade college students in two southwestern cities". The lookup article is empirical that
meets the requirements of the capstone mission with the gaining of understanding on the difficulty of
adolescence gang and non–gang membership attitudes and behaviors. The hypothetical context is primarily
based absolutely on comparing key variables of the social learning theory, advantageous reinforcers, terrible
punishers, and indications of differential definitions.The lookup method of cross–sectional quantitative records is
used, that is then recoded systematically and equipped to be entered into a pc database. This evaluation will serve
as an additional evaluation with the previous longitudinal information that is used

Regression Analysis For A Dependence Method
Regarding the testing of the hypotheses of this research, regression analysis or structural equation modelling
techniques is best suited for a dependence method (Hair et al., 2014). We employed regression analysis to specify
the extent to which the independent variables predicted the dependent variable. The analysis conducted in this
study was therefore intended to test the hypotheses of the study. The regression output provided some measures
which allow assessment of the hypotheses. Following from the hypotheses, Brand Engagement was used as the
dependent variable while the independent variables consisted of Monetary Savings, Exploration, Entertainment,
Recognition, and Social Benefit. Results from the model assessment are presented in the Table VI. Insert table VI
Results from the model assessment indicate strong and significant reliabilities among the constructs used in the
study (F = 87.362, Prob.F–stats < 0.001). This was followed by Exploration (β = 0.102, t = 2.271, P = 0.024 <
0.05), as well as Entertainment (β = 0.081, t = 1.712, P = 0.068 < 0.10). Although Recognition was positively
related to Brand Engagement, it was not statistically significant (β = 0.051, t = 1.084, P = 0.279 > 0.05). It was
however discovered that Monetary savings was inversely related to Brand Engagement (β = –.009, t = –0.194) as
well as statistically not significant in the current study (P = 0.846 > 0.05). In consequence, hypotheses one and
four (H1 and H4) were rejected in our study

The Effects Of Smoking On The United States
Smoking is established as a recognized cause of cancer, lung disease, coronary heart disease, and stroke(US
Public Health Service,1964,Doll R 1976,1994, US Department of Health and Human Services,1989).it is
considered to be the single most important avoidable cause of premature morbidity and mortality in the world.
WHO has estimated that there are about 1100 million smokers worldwide; this represents about one–third of the
global population aged over 15 years (WHO,1997). About 73% of these smokers (700 million males, 100 million
females) are in developing countries; in industrialized countries there are 200 million male smokers and 100
million female smokers. In the over 15–year–old population of developing countries, it is estimated that about
48% of males and 7% of females are smokers. The corresponding figures for industrialized countries are 42% for
males and 24% for females (WHO,1997).
Logistic Regression Model: The logistic regression models has wide ranging applications from determining the
population growth and its determinants to applications for predicting and analyzing qualitative data and in
epidemiological studies. Logistic regression model postulates relationship between the log odds of a disease and
the risk factors. A regression predicts the probability that an observation falls into one of two categories of a
dichotomous dependent variable based on one or more independent variables that can be either continuous or
categorical.
As in univariate logistic

Where Does Turnout Decline Come From?
Where does turnout decline come from?
ANDRE BLAIS, ELISABETH GIDENGIL, NEIL NEVITTE & RICHARD NADEAU
Background
Blais 2000 and Gray & Caul 2000 said that in the most recent democracies the number of people who use their
voice in election is declining. As what the journal claimed, this is also occurred in Canada. The journal reported
that the turnout in small period of elections tends to decline slightly by only three per cent, and this is claimed
important.
Based on that, this research was trying to investigate the causes of the decline using data from the Canada
election conducted from 1968 to 2000. The researchers concentrated in age and education factor as classic socio–
demographic factors that associated with voting. In addition, ... Show more content on Helpwriting.net ...
In the education factor, it is claimed that the fact happened in Canada, the decrease in turnout has occurred most
significantly among the better educated who may have gotten to be more inclined to refrain from time to time.
This result is surprising as we know that people with better education have higher willingness to vote that those
with lower education. The research tries to find why did this happen, and seek any other factors that affects.
Data
Based on the journal, here are some points they used:
Resource : Canadian Election Studies (CES)
Details : Nine years elections 1968
1974
1979 1980
1984
1988 1993
1997
2000 *No election study for 1972 election because it is missing
Data set : 25,000 individuals (about 3,000 per survey)
Dependent variable : Reported vote in the post–election survey
Independent variable : Age and Education
The journal also mentions some problem in the research, they are:
1. Over–estimated voting
Based on Blais & Young 1999 and Granberg & Holmberg 1992, the individuals who are more intrigued by
political issues and more slanted to vote are more inclined to answer surveys, and to some degree (on account of
board studies in which individuals are talked with in the campaign and re–questioned after the decision) in light
of the fact that taking part in a race study makes individuals more slanted to vote.
2. Some misreporting due to social desirability
Based on Brady et al (1995:292), social desirability

What Is The Impact Of The Great Recession On Payout Policy
This paper uses panel data analysis to assess the impact of the great recession on payout policy in the USA. It
uses three multivariate linear and nonlinear regression technics to study the relationship between the payout
variable, pre–identified payout determinants and the GR. The following regression model is considered as used
by Denis and Osbov (2008): 〖PV〗_(i,t)=β_(i,0)+β_(i,n) 〖X'〗_(i,t)+ε_(i,t) (1) 〖X'〗_(i,t) is a vector of
explanatory variables and includes all the relevant factors identified in section 3.2. Table 2 in the appendix
provides a summary of these variables, their measures, empirical evidence and expected sign. 〖PV〗_it is the
payout variable analysed as only dividends, only repurchases, both or total payout. The paper ... Show more
Nonlinear Models The second analysis technique uses a probit and logit models with the payout variable taking a
value of 1 if the sample firm uses the respective payout category in year t, and 0 otherwise. This is used by Fama
and French (2001) and DeAngelo et al. (2006) and allows to measure the marginal propensity for the different
payout categories while capturing the behaviour of firms that omit or initiate payout payments. We estimate a
fixed effect logit model where the log likelihood of the probit model and the random effect logit model do not
converge. Using fixed effect logit limits our work because the analysis considers only the explanatory variables
within the subsample of firms that changed their payout policies during the sample period (Renneboog and
Trojanowski, 2005). This excludes 22.1% of the original sample from the estimation procedure. We also use a
multinomial logit estimation procedure to explain the likelihood that a firm decides on a specific payout category
during the GR because we were unable to apply a multinomial probit estimation as used by Renneboog and
Trajanowski (2011). As such, the independence of irrelevant alternatives (IIA) assumption may be violated by
our outcome categories. We test for this violation using Hausman–McFadden test of IIA assumption. The
dependent variable in these models takes four possible

In Addition To Saving And Bequest Channels, The Proposed
In addition to saving and bequest channels, the proposed model in this paper identifies two additional channel of
child labor. These are heterogeneity in child 's ability, which causes heterogeneity in the cost of education, and
differential future expectation of parents depending on their socio–economic background. I attempt to capture
these four channels using a number of variables. For saving, I use income of household head and for bequest, I
use whether the household owns any house. Here income of household head may be under–reported or may have
some types of measurement errors, so, we use several other variables to instrument it. Ownership of a house is
used to capture bequest as housing price is very high almost across the country ... Show more content on
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Secondly, the socio–economic group in which the household head belongs to is also crucial in forming
household head 's expectation. Thus, I use household head 's gender ($Head_female$), Household head 's sector
of employment ($Sector$), religion of household head ($Religion$), and the location of household ($Urban$).
Using these variables I estimate the following models: Where $y$ is child 's working hours per day, $ extbf{X}=
{Urban, Sector, Female, Head_literacy, Religion, House,$ $ Head_income, Head_female, Dropout_ability,
Dropout_cost, Dropout_income}$, $ extbf{Z}={Urban, Head _sector,$ $ Female, Head _literacy, House,
Land, Num _hhchld, Head _literacy, Head_age, Dropout_ability,$ $Dropout_cost, Religion,
Dropout_income, TV, Head_female}$, $Q_{N}(eta) = g_{N}(eta) 'W_{N}g_{N}(eta)$, $g_{N}
(eta)=frac{1}{N} sum_{i=1}^{N}Z 'epsilon$, and $W$ is a $N imes N$ positive semi–definite matrix. In
addition, I also estimate logit and probit model of child labor, where I attempt to estimate the probabilities of
being a child labor with respect to the variables mentioned above. Results of these models are shown in the
following table:
In the above table second column reports the OLS estimates. Dummy $Urban$ has positive coefficient, which
indicates that child labor on an average work 0.4 hour more in

Stage 2 Hypertension Essay
Abstract: Hypertension is one of the leading causes of Ischemic Heart disease. The study aims to find out the risk
of getting Ischemic heart disease due to stage 2 hypertension. Other factors which contribute to the risk of
Ischemic Heart disease are high cholesterol levels, age of the person, smoking status of the person, tumor of
adrenal gland, sleep apnea, diabetes and high stress levels.For this study, the outcome variable is binary(Ischemic
heart disease) and the variable of interest is hypertension. The variables which are controlled include cholesterol
level of the person, smoking status, age and tumor of adrenal gland.The dataset which is from Evans County in
Georgia is a 9 year follow–up study of 609 males at risk of getting a Ischemic Heart disease.71 males in the
dataset had ischemic heart disease and 255 males had stage 2 hypertension. The race of the observations in the
dataset was white and gender being male leaves the scope for further study to include different races and females
which may lead to different outcomes and interpretations. ... Show more content on Helpwriting.net ...
(b)1mg/dl increase in Cholesterol increase the probability of having Ischemic Heart disease by 0.000689.
(c)Smoking increases the probability of having Ischemic Heart disease by 0.05862.
(d)Tumor of adrenal gland increases the probability of having Ischemic Heart disease by 0.2499.
Conclusion: By using the Logit model, we reject the null hypothesis as Stage 2 Hypertension increases the risk
for Ischemic heart disease.
Limitation: Diet, Socioeconomic condition, diabetes and stress levels could have been used as control variables
to fit the model better. Inclusion of variables like race and gender can give interesting results. Sleep apnea may
may cause Omitted Variable Bias(OVB) as it exaggerate s the effect of Hypertension on developing Ischemic
Heart

The Impact Of Retail Industry On The Retail Sector
I. Introduction The retail sector plays a pivotal role in the development of UK economy, contributing £180
billion to GDP in 2014, 11% of the total. This industry employed about 4.4 million people in 2014, 15.8% of the
UK labour, becoming the largest broad industrial group by the quantity and proportion of employees. 539,000
enterprises operating in the retail industry in the UK makes account for 10.3% of all UK businesses. Therefore,
the failure of retail companies can lead to adverse impact on not only shareholders, employees but also the whole
economy. Those are the reasons why the UK government has focused on the development of this industry and
also made an effort to prevent the bankruptcy in the retail sector. Figure 1. % change on the previous year of
economic output in retail sector and whole economy According to Niemira (1996), retail industry tends to be
more vulnerable to the ups and downs of the economy than other industries. To illustrate by Figure 1, in 2007,
the retail sector grew faster than the UK economy. Nevertheless, when the recession began in late 2007, a
quicker and deeper decline than the whole economy could be seen. At the bottom in Q2 2009, output from the
retail industry dropped by 9.3% compared with 2008 while the output from the whole economy decreased by
5.8% in that quarter (Rhodes, 2014) The history witnessed many notable bankruptcy cases in the retail industry
including Montgomery Ward, Macy 's, Zale, Brooks Fashion, Revco, Hooker and Dart

Gender Wage Discrimination in Pakistan
| Gender Wage Discrimination in Pakistan | Evidence from Pakistan 2008/09 and 2010/11 | | | |
Table of Contents Introduction 2 Literature Review 2 Methodology 3 Variables Used – Characteristics of
Workers 5 Results 7 Discussion 7 Bibliography 8 Appendix A 9 Selectivity Bias Logit Regression Results: 9
Introduction
This paper explores the dynamics of gender wage discrimination in Pakistan for two data sets; Labour Force
Survey for the year 2008/09 and 2010/11. We will explore whether or not women are discriminated against, as it
has been suggested for a predominantly Islamic country like Pakistan.
Labour theory addresses many reasons for wage discrimination. For the purposes of this research we will ...
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The signs of education are expected. For the data set of 2008/09, signs for education are positive which does not
support theory. Even after efforts to remove multi–colinearity, they still show positive signs. Most of the
variables in the regression are also insignificant.
However, when we take the data for LFS 2010/11, and correct it for selectivity bias, we get much better results.
Most of the variables are significant as well as show the correct signs. (The same algorithm was applied to both
the data sets, and the same variables have been taken).
Results of Logit models for correcting selectivity bias are attached in Appendix A.
Variables Used – Characteristics of Workers Summary tables from LFS 2010/11 1. Age * Theory suggests that
this is one of the most important determinants of people's decision to work.
2. Marital Status * This variable was taken as a dummy variable in the regression. * It is a significant variable in
the decision to work, especially in developing economies like Pakistan. 3. Province * This is also taken as a
dummy. The Baluchistan province was omitted from this analysis. However, the calculations of the Oaxaca
Blinder method take this omitted variable into account. This is because the method takes the vectors of the
estimated regression equation.
4. Region * Whether a person is from a Rural or urban background has impact on the

Analysis Of The Association Of Obesity And Overweight With...
Multivariate analysis is tested using logistic regression analysis to investigate the association of
obesity/overweight with frequent mental distress (FMD) and other covariates such as age, sex, race, employment
status, regular exercise, cigarette smoking, general health, physical health, unable to do activities, depressive
disorder and diabetes. Each of the independent variables was separately used to predict FMD. This was followed
by a multivariable model, where all the variables were simultaneously introduced. Finally, separate multivariable
analyses were performed after stratifying for the significant covariates and interaction variables to get
parsimonious model. All the independent variables were binary or categorical with two or three categories.
Full Model Logistic Interpretation: The logistic regression of full model indicates that after controlling for
covariates, such as age, sex, race, employment status, regular exercise, cigarette smoking, general health,
physical health, unable to do activities, depressive disorder and diabetes the odds of obesity among participants
having FMD is 0.99 times the odds of obesity among those who do not have FMD interval (95%CI: 0.43,
2.29).Race, regular exercise, cigarette smoking, physical activities, unable to do activities, depressive disorder
are not significantly associated with BMI. Covariates such age, sex, employment status, general health and
diabetes are statistically significant in the model with p0.005 or p0.05, thus

The “Lg10” Function Under The “Transform/Compute Variable”
the Lg10 function under the Transform/Compute Variable pull down menu feature of IBM's Statistical
Package for Social Sciences (SPSS). Once the variables were transformed, we ran a correlation analysis between
the operationalized independent variables, moderating variables, control variables and the dependent variable to
check for collinearity and to begin to identify and evaluate non–causal associations and strengths of relationships
between variables. We also applied Variance Inflation Factors (VIF) to detect any overlap or similarity between
the independent or explanatory variables. The VIF results showed that multi–collinearity was not an issue. See
Table 6 for the correlation and VIF results. Hypothesis Tests To execute the ... Show more content on
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An odds ratio (Exp(B)) greater than 1.0 indicates predictor variable is more likely to be resistant (1), whereas an
odds ratio less than 1.0 indicates the predictor variable is less likely to be resistant. Results The Pearson
Coefficient correlations summarized in Table 6 show six statistically significant non–causal associations between
variables. In particular, there is a correlation of –.172 (significant at the 0.05 level) between Firm Performance
Resistance and the independent variable Terrorism Exposure; this is counter to the direction of effect suggested
in Hypothesis 1(a). For a more robust test of the relationships, we conducted a series of stepped logit regressions,
the first of which are summarized in Table 8, Analyses 1 and 2. Analysis 1 looks at the relationship between
Terrorism Exposure and Firm Performance Resistance, with Time Since Last Attack as a moderating variable.
Analysis 2 looks at the relationship between Terrorism Exposure and Firm Performance Resistance, with
Business Continuity Plan as a moderating variable. The overall percentage shown in the model summary
(Overall %) is the predicted percentage of firms that would have the outcome of resistance (coded as 1) to the
focal terrorist attack – meaning that the stock price did not drop by 0.5% or more. Finally, the Nagelkerke R
squared and the statistical significance (Model

The Effects Of Different Types Of Carbon Taxes On The...
Introduction
This paper examines the effects of different types of carbon taxes on the market share of different electricity
generation technologies, particularly between renewable and fossil fuel generators. The Global Change
Assessment Model (GCAM) was used for this analysis because it includes detailed representations of the
technologies in the electricity sector. Given a constraint on emissions or a certain carbon tax, GCAM will
determine the least cost methods of supplying energy in different sectors. We found that the share of low–carbon
generation will increase as the carbon tax increases and the share of renewables generally increases with the
implementation of a carbon tax. Additionally, the timing of renewable market share ... Show more content on
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GCAM is a dynamic–recursive model that utilizes a solution algorithm that is based on a market equilibrium. It
consists of three core modules: a) Energy, b) Agriculture and land–use, and c) Model for Assessment of
Greenhouse–gas Induced Climate Change (MAGICC). Regional population and labor productivity growth
assumptions drive the energy and land–use systems employing numerous technology options (JGCRI, n.d.). The
GDP is a simple labor–productivity model with an energy–GDP feedback elasticity (JGCRI, 2014).
Technology change is modeled using an autonomous energy efficiency index (AEEI) parameter.
The choice of generation mix of technologies in GCAM is based on a logit model, which includes the relative
cost, logit exponents, and share weights of each technology. The logit approach controls the distribution of
market share between technologies or fuels in response to price changes as represented by equation (1).
(1) Share(i) =swi .Ci–iswi .Ci– where Ciis the cost of the ith technology, swiis the share–weight of ith
technology and is the logit exponent. A high valued logit exponent means that technology's market shares are
more responsive to changes in costs. The share weights are calibrated parameters based on the base

Effect of Audit Opinion on Stock Prices
EuroEconomica
Issue 2(31)/2012 ISSN: 1582–8859 Effects of Audit Opinion on Stock Prices: The case of Croatia and Slovenia
Arbër H. HOTI Teaching Assistant
Faculty of Economics, University of Prishtina, arber_hoti@hotmail.com
Hysen Ismajli Associate Profesor
Faculty of Economics, University of Prishtina, hysen_rismajli@yahoo.com
Skender Ahmeti Associate Profesor
Faculty of Economics, University of Prishtina, skender.ahmeti@yahoo.com
Arben Dërmaku Assistant Profesor
Faculty of Economics, University of Prishtina, adermaku@hotmail.com
Abstract
The aim of this paper is to determine the effects of stock prices following the announcement of audited financial
reports of Slovenian and Croatian public interest entities. Our ... Show more content on Helpwriting.net ...
The fact that only one out of every two executives of Croatia and Slovenia listed companies has a theoretical
knowledge of IFRS which implies that auditing firms have been involved at least in training programs to Croatia
and Slovenian listed companies in the transition period to IFRS. This study is justified in a different context than
other studies. It is justified in the framework of IFRS and, in particular, three years after their adoption. IFRS
were effective since 2005 and the date of audit opinions are dated in 2007.The contribution of this study is that it
provides evidence from the Zagreb Stock Exchange and Slovenia Stock Exchange listed companies is
characterized by a stakeholder (debfholder) orientation which stands in a transition. Note that the debt to equity
ratio of Zagreb Stock Exchange and Slovenia Stock Exchange listed companies in a time horizon of four and a
half decades stands on average to 1.24 (minimum) in 2004 and 3.068 (maximum) in 1984 (with outliers
excluded). The structure of the paper is presented as follows: Section 2 discusses the review of the literature.
Section 3 describes the research design. Section 4 presents the empirical analysis and results. Section 5
concludes with suggestions for further future research.
2
2. Literature review Audit reports supplement the accounting information drawn from the financial statements.
They provide a means of

Demand Estimation Has Been At The Heart Of Many Studies
Literature Review Demand estimation has been at the heart of many studies that focus on questions regarding
market power, merger and acquisition, research and innovation and valuation of new brands in differentiated–
products industries. Under the framework of demand estimation, Bresnahan (1987) constructs equilibrium
models of oligopoly under product differentiation and studies the competition and collusion in the 1995 price
war in the American automobile industry. Gasmi, Laffont and Vuong (1992) study the collusive behavior on
price and advertising in the U.S. soft–drink market， which is dominated by Coca–Cola and Pepsi–Cola, after
obtaining a full information maximum likelihood estimation of demand functions. Hausman, Leonard and Zona
... Show more content on Helpwriting.net ...
Firstly, there would be a large number of parameters to be estimated. For example, in a log–log demand system,
where logarithms of quantities are linear functions of logarithms of all prices, without additional restrictions, we
would have 10,000 parameters if there were 100 differentiated products in the market. The second concern
introduced when doing demand estimation for differentiated product is the heterogeneity in consumer tastes.
Dixit and Stiglizt (1997) suggest a representative consumer approach to deal with this. They assume that
consumer preferences are of the right form so that an average consumer exists; there exists a demand system that
lends itself to the level of differentiation observed in the marketplace. However, the required assumptions are
strong and empirically false for many applications (Nevo, 2000). The above two problems are addressed by logit
demand model (McFadden, 1973). The logit model solves dimensionality problem by projecting the products
onto a space of characteristics; in this way, the dimension of the attribute space instead of the square of the
number of products becomes the relevant size. However, due to the restrictive way of modeling heterogeneity
and the property of independence from irrelevant alternatives (IIA property) in the logit model, substitution
pattern between products is driven completely by

Comparing Logit And Probit Models Essay
In Logit and Probit models, a dependent variable takes only two values that represent the occurrence of an event
(yes or no) or a choice between two alternatives. In the current case, to model choice status of each individual
WTP for improved solid waste management respondents differed in age, educational attainment, income, sex and
other observable characteristics. These are denoted by S. In the dichotomous choice method, individuals are
assumed to have utility function, U, income (I), and a set of conditioning factors (S): U(I; S)
Each respondent was confronted with a specified bid value, BID, which she or he may contribute towards the
management initiative. It is assumed that the individual would accept a suggested BID to maximize his or her
utility under the following condition and reject it otherwise (Hanemann, 1984):
U(1, I –BID; S)+ ε1 ≥U(0, I; S) + ε0
In the current case, ε1 and ε0 are considered as independently distributed random variables with zero means.
Therefore, the probability that a household would decide to pay for the proposed improved solid waste
management for Delhi is the probability that the conditional indirect utility function for proposed intervention is
greater than the conditional indirect utility function for the status quo. The dependent variable is dichotomous
and equals 1, if the ith household is willing to pay a bid and 0, otherwise. The general form of the estimation can
be written as:
〖

The Internal Influential Factors That Affect College...
Analysis on the internal influential factors that affect college students ' financial investment tendency 1.
Introduction: Investment can be used to refer to any mechanism used for the purpose of generating future
income. In an economic sense, an investment is the purchase of non–monetary asset that are not consumed today
but are used in the future to create wealth, such as education investment. In finance, an investment is a monetary
asset purchased with the idea that the asset will provide income in the future or will be sold at a higher price for a
profit. College students is a representative group in current society. To the vast majority of them, college
enrollment may be the first time to experience financial independence without parent 's supervision. With more
disposable money from either parents or part–time job, money management appears to be very important for
college students. Among Chinese consumers, college students represent the biggest segment of the total Internet
users (CNNIC, 2013). However, limited research has been conducted to better understand the financial behavior
(e.g. savings and indebtedness) and tendencies of this special group, since the concern over the role of young
consumers is relatively new. Those who are about to enter into society will add new blood with great investment
potential to financial market. Understanding this group's financial behavior and tendency are important for
marketers to develop strategies to target big consumer

Analysis Of Rice Farming
The Proportion of Credit Structure Purposes Based on Cost Structure and Determinant Factors of Credit Demand
by Farmers on Rice Farming Typology of Tidal Land
Abstract: Government policies to increase production and farmers' income at the same time with the ultimate
goal of poverty reduction; one of them through distribution credit to strengthen farmers. Such efforts
implemented by providing capital assistance which known as program of Food Security and Energy Credit. This
research determines the proportion of credit purposes in the capital structure of farmers on farming, especially
rice and determine the factors that influence the demand for agricultural credit by farmers, especially rice
farming. The research was conducted in two ... Show more content on Helpwriting.net ...
Keywords: credit; cost structure; credit demand and Tidal swampland
Key Words: (11Bold)
INTRODUCTION:
The factor of capital to finance still plays an important role in the development of farming systems in tidal
swamp land, South Kalimantan. This capital can be obtained from internal farmer, result of the accumulation of
income after consumption and can also be obtained from the external form of credit. Previous research findings
on swamp land farming belonging to the sub–optimal land; among others by Makki et al (2008) and also
Akudugu (2012) showed that the factors of financing and capital becomes one of the factors that determine the
success of farming.
Base on previous studies, there is important to follow up because different with general characteristics that have
so far in the implementation of agricultural businesses. Purposes of financing and capital in order to optimize
farming in tidal swamp land just on the phases of certain activities and appropriate characteristics, then there is a
difference between the purpose and use of capital between the tidal land farming compared to farming on the
typology of rainfedor dry land.
Currently, access to farming credit relatively easy and credit schemes provided by the government has also been
relatively large. But the main problem faced in terms of capital sources is actually the viability of a given credit
scheme. Moreover, when the aspect of sustainability is focused on

Forecasting Model Of Forecasting Models
Forecasting is often defined as the estimation of the value of a variable (or set of variables) at some future point
in time (Goodier, 2010). It can be applied to a number of different situations when there is uncertainty about the
future and the data collected can aid in decisions that need to be made (Armstrong, 2001). In relation to
healthcare, forecasting models have been used to aid their sector's departments to plan staff rota schedules,
ensuring that a sufficient amount of senior staff are available at any given time throughout the day, week, month
and year. As explained previously, a fundamental factor that causes overcrowding is a limited supply of resources
to treat patients, leading to a longer time spent in an Emergency ... Show more content on Helpwriting.net ...
These models can be characterised as consisting of a time trend, a seasonal factor, a cyclical element and an error
term (Kennedy, 2008.) Unlike casual or economic forecasting, where it is assumed there is a historical
relationship between a dependent and an independent variable will be consistent in the future, time series models
assume the historical components of the model will repeat itself. Research has been undertaken to develop a
generalised forecasting model that uses a method that can accurately predict future the attendees and resources
needed at Emergency Departments.
1.3.3 Long Range Forecasting for Future Attendees An early attempt to predict attendees was conducted by
Milner (1988) who's study on a single Emergency Department within the UK attempted support to healthcare
planning by forecasting annual first, return and total attendances at EDs for Trent districts and the whole of the
Trent region. The data of annual first, return and total attendances were collected over a training period of 10
years and evaluated over a period of 1 year using an Autoregressive Integrated Moving Average (ARIMA)
method for modelling which falls into time series model category. This method for forecasting this type of data
has been supported by other researchers, who state that ARIMA forecasting techniques should be considered for
a time series that's contains a trend or seasonal or non–stationary data. The results

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