The closer the data aims to making a straight line, the higher the correlation between the two variables, or the stronger the relationship(MSTE,n.d) The scatter plot above does not have a straight line formation, so that showing that there is not a strong relationship between the two variables of GPA and final. An example of a null hypothesis for the variables used in this data collection would be, “Does GPA predicts final exam scores? An alternative hypothesis would be that GPA scores do determine the exam scores.
Central tendency is the extent to which data values conjoin around a specific value or central value (Levine, Stephan, Krehbiel, & Berenson, 2008). The mean is a balance point in a set of data (Levine, et al., 2008). In order to calculate the mean, you must add together all the values and then divide that sum by the amount of values present in the data set (Levine, et al., 2008). One extreme value can alter the mean greatly, when this happens the mean my not be the best measure of central tendency (Levine, et al., 2008). The median is another measure of central tendency that measures the half waypoint in a data set (Levine, et al., 2008).
The proposed method is a combination of the non-optimal and optimal solutions. At first, the design space is reduced by using heuristic approach. Next, the best solution form the reduced search space is extracted. The pseudo-code of the proposed algorithm is shown in Figure 1. Function SelectPaths(U, NSPC) 1:Find the correlations between the paths 2: Prune the paths 3: Generate correlation Matrix 4: Sort the items in the Matrix 5: Prune the Matrix 6: Write ILP formulation 7:Solve ILP Figure 1- The pseudo-code of the proposed selection method In the first step, the correlation between each two paths of the U, is calculated.
7) Table 51 Here we are testing independence between two Attributes: A: Satisfaction of bonus and incentives B: Types of bank Hypothesis: H0 : Satisfaction of bonus and incentives is independent of types of bank. H1: Satisfaction of bonus and incentives is not independent of types of bank. Result: P-value of χ_cal^2≅ 0.00000. Conclusion: As P-value less than α we reject H0. Hence, Satisfaction of bonus and incentives is not independent of types of bank.
Please identify (1) what they are, (2) where to find them on SPSS, and (3) how you know if you have met each of the assumptions. Correlation: Linearity- Assumption that there is a linear relationship between you predictor variable and the outcome variable that you are testing. You are able to check for this in SPSS by generating scatterplots and inserting the line of best fit. If you have a consistent alignment of points in a linear fashion going along with the line of best fit you meet the assumption. If your points are all over and not in a linear fashion, linearity is not met.
As financial variables are characterized for non linear dependence, the ARCH model captures that dependence because it allows for heteroskedasticity, precisely, it depends only on the latest lag. However, when the sample is considerably large, the ARCH model has not the ability to capture that dependence because it will need so many lags that the estimation turns too much complex. Instead, later on, Bollerslev (1986) introduced the GARCH model. The main advantage is that it is able to capture the memory of the volatility, that parameter is the beta allowing for large samples. The main issue is that is defined with symmetric response in volatility, in other words, it is only taking into account the magnitude of the shocks, rather than the sign, meaning that for the same magnitude, negative and positive shocks will have the same effect.
Another reason for choosing the FE model12 is that it can solve the endogeneity problem through using the FE-IV model; the variable GDP per capita-used as a proxy of income-could be an endogenous variable. An endogenous variables are variables that correlated with the error term (ε௧ ), while the variables that uncorrelated with the error term are called exogenous variables. The description of these terms explains that an endogenous variable is determined within the model itself while an exogenous variable is determined outside the model. To understand the endogeneity, we will use the classic regression equations that show the relationship between prices and wages: Price = ߚ0 + ߚ1Wage + ε௧ ………………………… (11) Wage = ߚ0 + ߚ1Price + u௧ ……………………... (12) From equation (11) and (12) we can see that prices can affect wages and also wages can affect prices, in this case we can say that both wages and prices are interdependent variables and to run our regression we can‟t use the OLS technique because using OLS will give us biased estimates because of endogeneity13. From equation (11), the variable Wage is an endogenous variable and to solve the endogeneity we need an instrument that correlated with Wage but not correlated with the error term.
On the other hand null hypothesis is a statement of equality between sets of given variables and an equal sign is being used between the variables. Null hypothesis is a no effect or a no difference hypothesis. Whereas alternative hypothesis describes the population parameters that the sample data if the predicted relationship prevails, on the other hand null hypothesis describes the same if the predicted relationship does not prevail. Another difference between null hypothesis and alternative hypothesis is that, alternative hypothesis refers to sample while null hypothesis refers to a population and hence alternative hypothesis can be tested directly
Tabachnick and Fidell (1996) suggest the value of skewness and kurtosis is equal to zero if the distribution of a variable is normal. Chou and Bentler (1995) emphases the absolute values of univariate skewness indices greater than 3 can be described as extremely skewed. Meanwhile, a threshold value of kurtosis greater than 10 can be considered problematic and value greater than 20 can be considered as having serious problems (Hoyle, 1995; Kline, 1998).