The most common names with which X and Y can be referred to are: By admin on January 5, in Data Analysis In the field of research, Regression is a generic term that is used commonly for all the methods that strive towards the quantification of the relationship between two groups of variables.

As your research has indicated that alcohol use is the biggest predictor of child abuse, you would enter that predictor variable into the regression equation first. The classical indications for regression as a tool for future prediction, could be the following: The model that is fitter now could be further used to describe the relationship that exists between the two groups of variables or sometimes to predict new variables.

The incidence of child abuse would be entered as your dependent variable. The denotation of X and Y is possible Regression analysis in thesis not just one term but a plenty of terms. If you have only one Regression analysis in thesis variable and one dependent variable, you would use a bivariate linear regression the straight line that best fits your data on a scatterplot for your analysis.

In a simple regression analysis, all of your Regression analysis in thesis variables are entered together. If we go further to explain the notations of regression, there are two data matrices used which are denoted by X and Y.

This model attempts to explain the relationship between the Y and X variable which further means that it explains the variations that happen in Y variable from the variation that happens in X variable. The big difference between these types of regression analysis is the way the variables are entered into the regression equation when analyzing your data.

Your dissertation hypothesizes that these three variables predict the incidence of child abuse. A correlation indicates the size and direction of any relationship between variables.

However, it is important to note that the link between Y and X can only be achieved when the set of samples is common and values have been collected for both X and Y.

Since your background suggests that socioeconomic status also contributes to child abuse, but not as much as alcohol use, you would enter that predictor variable next. If this is the case, then use a simple regression for the analysis. If the p-value obtained by your analysis is less than this, then your results are significant, and your variable education level is a significant predictor of child abuse, even when your other variables alcohol use and socioeconomic status are accounted for!

If your research did not indicate that any of your independent variables alcohol use, socioeconomic status, education were related to your dependent variable child abusethen there is no clear theory on which your dissertation is based to dictate what order you should enter these variables in the regression equation.

To determine which of these regressions you should use to analyze your data, you must look to the underlying question or theory on which your dissertation or thesis is based. When the researcher feels the need to build up a response surface model from the outcome of the experimental designs No comments yet.

Where is the need to use a Statistical Regression Model? Using your preset alpha level. Your research also has indicated that socioeconomic status is correlated with child abuse, but not as much as alcohol use.

After you enter all your variables and run the analysis, your statistical software package should provide a significance value p-value. It is often insufficient for modelling a property. Based on your research, an order of entry is suggested for your analysis, so you would use a hierarchical regression for your analysis.

If, however, your hypothesis involves prediction such as variables "A", "B", and "C" predict variable "D"then a regression is the statistic you will use in your analysis. To use a hierarchical regression in analysis, you must tell the statistical software what order to put your predictor variables into the regression equation.

If your paper is based on a theory that suggests a particular order in which your predictor variables should be entered, then use a hierarchical regression for the analysis.

Making a Regression Model involves a collection of predictors and the response values for the common samples and then fitting into it a mathematical fundamental which is a part of the collected data.

Types of Regression Analysis There are several types of regression analysis -- simple, hierarchical, and stepwise -- and the one you choose will depend on the variables in your research. When the researcher feels the need to put to use cheap and easy to perform measurements for as a substitute for the costlier alternatives and the time consuming options.

In most statistical software packages, you simply select the type of regression you want to use for your analysis from a drop-down menu.

For an analysis using step-wise regression, the order in which you enter your predictor variables is a statistical decision, not a theory on which your dissertation is based. From your research, you learn that there is a strong correlation between alcohol use and the incidence of child abuse.

This is where multivariate regression comes into picture as it takes into account a lot of predictive variables at the same time and models the property of interest with greater precision.data analysis, dissertation of thesis?

help please? up vote 1 down vote favorite.

I've tried using regression analysis, dependent variable is Gender then independent variable are the overall satisfaction of my respondents in the two fast food. Basically, it's the logistic regression where the dependent variable has multiple ordered.

For an analysis using step-wise regression, the order in which you enter your predictor variables is a statistical decision, not a theory on which your dissertation is based. To determine which of these regressions you should use to analyze your data, you must look to the underlying question or theory on which your dissertation or thesis is based.

In regression analysis, the focus is usually on the overall effects of all the independent variables on the dependent variable, and what each independent variable contributed relative to the contributions from other independent variables that gave rise to the overall outcome.

The overall outcome is usually seen as a unit of a whole event outcome. Statistical Regression Analysis: The Fundamentals By admin on January 5, in Data Analysis In the field of research, Regression is a generic term that is used commonly for all the methods that strive towards the quantification of the relationship between two groups of variables.

Plotts, Timothy, "A Multiple Regression Analysis of Factors Concerning Superintendent Longevity and Continuity Relative to Student Achievement" (). Seton Hall. Linear Regression Analysis on Net Income of an Agrochemical Company in Thailand.!

2!! Abstract: The purpose of this research is to analyze the ABC Company’s data and.

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