industry, thereby businesses can get high profit margins in the long-term. 3.4. Model and methodology Previous studies often used cross-sectional data or time-series data to build model. In this study, panel data is used, which allows for the use of panel data regression model. The analysis based on cross-sectional data model can only reflect the relationship between the variables in one year and is susceptible to accidental factor. Moreover, we can only analyze one unit over several time periods from the use of time series data model. In this paper, I decided to combine above two methods by using panel data regression model, reflecting both firm-specific effects and time dummies(Ozkan, 2001). This method improves the efficiency of econometric estimates(Dessí and Robertson, 2003) and leads to a more comprehensive analysis of the relationship between the capital structure of the industry and enterprise value. From the point of view of convenience in research, I decide to establish the multiple linear regression model under assumption of linear relationship between capital structure and enterprise value. Then, I use software EVIEWS7.0 to do unit root test for checking the stationarity of each variable. Before the regression, correlation test between variables should be done in the model to eliminate the multicollinearity effect. Based on the results of F and Hausman test, a fixed effects model is selected from three basic estimation techniques for panel data, including…
A0130036Y Method 1: Box Plot The question asks if Road Handling is part and parcel of Practicality, i.e. whether road handling is an essential component influencing/affecting another data group, practicality. Thus, road handling is taken to be the independent variable, x, and is ranked from the lowest to the highest score. The data is split into 5 groups, Group 1 and 5 containing the lowest-ranked and highest-ranked road handling scores, respectively. Group 1 2 3 4 5 Median 4.58 4.585 4.455…
T.T.M Kannan et al. [2014] performed the experiment on AISI 316 Austenite stainless steel to investigate the heat partition, tool wear and tool life. In their investigation they found that CBN cutting inserts has been damaged in moderate cutting velocity and produce good machinability and higher cutting temperature decreases the yield strength of produced white layer. [11] R.Suresh et al. [2014] studied the effect of various cutting parameters in hard turning of AISI H13 steel at 55 HRC with…
Correlation analysis is used to determine the degree of relationship between variables. In correlation it is assumed that the variables mutually influence each other (Sharma, 2005). Baker and Saltes (2005) used a correlation to test capability of ABI for forecasting CS sectors and then used regression analysis’s coefficient of determination (R2) to indicate the proportion of the variance of CS that is explained by ABI. They found that non-residential CS is highly correlated with ABI in lag 5 for…
An Artificial neural network model for risk impacts on cash flow forecast in construction industry Key Words: Risk Factors, Risk Impacts, Model, Artificial Neural Network, Cash Flow Forecast Area of Research Cash flow forecasting is a vital contributing factor in construction industry where lead to the high rate of insolvencies. Risks involved with construction industry play significant role for the variation of forecasted and actual cash flow. Identification of risks and risk assessment are…
Problem: Apparatus 1: what is the relationship between height of release and the initial velocity? Apparatus 2: What is the relationship between length of the ball catcher and the final velocity of the system? Additional question: What is the percentage error of the initial velocity calculated using momentum and kinematics? Percentage error between theoretical initial velocity and experimental initial velocity? How much energy is lost? note: the initial velocity calculated using kinematics…
The Kriging weights w_i1,w_i2,w_i3,…w_ik can be used to estimate the fair value of VA contracts x ⃑_i by the following formula, y ̂_i=∑_(j=1)^k▒〖w_ij∙y_j 〗. The kriging weights can be calculated by the following linear equations, Where the is a control variable, which is used to make sure that ∑_(j=1)^k▒w_ij =1. V_rs=α+exp(-3/β D(z ⃑_r,z ⃑_s,λ)),r,s=1,2,3,…,k, D_ij=α+exp(-3/β D(x ⃑_i,z ⃑_j,λ)),j=1,2,3,…,k, The D(.) function is the distance function mentioned in the clustering section,α≥0…
Weight Estimation using AHP In this section weight estimation using AHP is provided, where first dependency matrix is created based on saaty’s scale. Referring to matrix A1, every attribute is compared with others, ex. DT (Distance) first compared with itself so value is 1, then DT is compared with TT (Travel Time) as in this case TT is moderately important than DT is value will be 1/3, when DT is compared with PCU (Traffic Volume), in this case DT is moderately important than…
This paper will study the relationship between a country’s perceived amount of corruption, and how that affects the wages of its domestic workers. The premise behind this paper is that a country that is perceived as being dishonest and corrupt would have lower wages. Corruption in government is seemingly always present in our world. This paper will try to determine if there is a correlation between a country’s perceived corruption and the monthly disposable income its workers earn with…
Variance Decomposition Analysis The variance decomposition analysis has applied to quantify the extent up to which the selected indices one influenced by each other. We can also examine the short run dynamic relationship by variance decomposition. While impulse response functions trace the effects of a shock to one endogenous variable in the VAR, variance decomposition separates the variation in an endogenous variable in to the component shocks to the VAR. Thus, the variance decomposition…