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My Ph D thesis deals with the statistical problem of analyzing the relationship between a response variable and one or more explanatory variables when these quantities are only imprecisely observed.
The test will be used to determine the significance of the predictor and beta coefficients will be used to determine the magnitude and direction of the relationship.
For statistically significant models, for every one unit increase in the predictor, the dependent variable will increase or decrease by the number of unstandardized beta coefficients.
The total sample also was divided randomly into two groups, and cross-group analyses resulted in simple correlations of .56 and .29.
Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process.
The assumptions of a linear regression —linearity and homoscedasticity—will be assessed.
Regression Models Dissertation
Linearity assumes a straight line relationship between the predictor variables and the criterion variable and homoscedasticity assumes that scores are normally distributed about the regression line. Data analysis plan: Linear Regression [WWW Document].
The basic form of regression models includes unknown parameters (β), independent variables (X), and the dependent variable (Y).
Regression model, basically, specifies the relation of dependent variable (Y) to a function combination of independent variables (X) and unknown parameters (β) Regression equation can be used to predict the values of ‘y’, if the value of ‘x’ is given, and both ‘y’ and ‘x’ are the two sets of measures of a sample size of ‘n’.
Linear regression analysis is based on the following set of assumptions: 1. There is a linear relationship between dependent and independent variables. There is no correlation between two or more independent variables. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline.
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