The Effect Of Bank Consolidation On The Performance Of Banks In Nigeria
equation which the data would express the relationship between variables. The simple linear regression analysis is used to analyze the stated hypothesis.
In hypothesis one, the functional relationship was postulated between (capital base) consolidations (X) and (performance) liquidity ratio of Nigerian banks (Y).
The relationship in hypothesis two is between (capital base) consolidation (X) and (performance) loan to deposit ratio (Y).
To express the model of simple linear regression in equation form is:
Y = a + bx
Where Y = dependent variable
a = intercept parameter (where the regression surface crosses the y axis)
b = slope of the regression line (it is the rate of change in Y with respect to X)
x = Independent variable
3.3 DATA ANALYSIS TECHNIQUE
In this study, parametric tests will be used. The statistical technique that will be used is the coefficient of correlation, which is often referred to as the Pearson Product Moment Correlation Coefficient; the coefficient of correlation will be calculated using the following formula:
r = n() – ()
The correlation coefficient tells us the nature of the relationship between the dependent and independent variable.
It was originated by Karl Pearson about 1900; the coefficient of correlation describes the strength of the relationship between two sets of valuables. It can assume any value from -1.00 to +1.00 inclusive. A correlation of -1.00 to +1.00 indicates perfect correlation.
If there is absolutely no relationship between the two sets of valuables Pearson r will be zero. A coefficient of correlation r close to zero shows that the relationship is quite weak.
Correlation coefficient can be defined as an often used statistics that not only provides a measure of how random variables are associated in a sample, but also has properties that closely relate it to straight its regression. It could also be defined as a statistical technique that determines the strength of linear relationship between two variables.
Furthermore, it can be stated as a measure of strength of the linear relationship between two sets of variables.
The coefficient of correlation does not tell us if the relationship is indeed significant. To do this the researcher uses the +- test to test if the relationship is significant. The formula for +- test is:
t = with n – 2 degrees of freedom.
We will use the two tailed test at 0.05 level of significance. The 0.05 level of significance for rejection of null hypothesis, means a researcher is willing to accept the probability that chances are less than five in one hundred, that the observed difference is due to sampling error. Therefore, the probability of committing a type one error is less than 0.05.
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ESEOGHENE IGBERAHARHA. department of finance and banking, university of port harcourt, choba Rivers state.