The last assumption the multiple linear regression analysis makes is homoscedasticity. The scatter plot is good way to check whether homoscedasticity (that is the error terms along the regression line are equal) is given. If the data is heteroscedastic the scatter plots look like the following examples:
To build upon and test my hypothesis, it is important to review the state-of-the-art for both the public participation and information technology domains (body of knowledge, current research and approaches, available technology, role of information systems in decision making), through literature review and experimentation with technology. In particular, this review and experimentation provides the foundation for the few assumptions in the formulation of the hypothesis and the choice of methodology, what I called "argued assumptions":
False Assumptions Concerning Thesis Statements 1
Other assumptions are analytical. They are based on facts, but they go a step further in making some sort of statement about those factsinterpreting them, analyzing them, explaining them, judging or ranking or weighing them. An example of an analytical assumption in the sample would be that larger systemic problems ... , such as the exclusion of voices and perspectives of racial minorities and working-class populations from environmental policy-making, caused environmental racism (paragraph 5). This assumption goes beyond measurable or observable data to examine meanings and relationships. As such, it is always more open to debate than factual assumptions whichat least in theorycan be proved or disproved by direct observation.