You generally start with a description of the research question and the data. You identify your dependent variable, and all the independent variables and any control variables used in the analysis. You describe the literature regarding previous studies in this area, and if you are building on or testing any of these studies, you want to say so.
Then, you tell how the data are gathered, what the sample was, and other information to allow researchers to get a handle on whether they think there may be problems in the study that are caused by deficiencies or misunderstandings in the data gathering process.
Next you describe the variables you have selected for your analysis, and what the theory is driving your selection of these variables. I think you put in your hypotheses (don't forget the nulls) at this point.
At this point you want to describe your analytical tools. Now this, of course, depends on what kind of variables you are using (binary, categorical, continuous) for both the dependent and independent variables.
Operationally, you do the analysis. Now, in reality, you might play around a lot with your data, and try various things, to see which gets you the best results. But in the presentation, you mention none of that fumbling around that everyone does. You act as if you knew it all from the beginning.
So, back to the presentation. You've got results. Display them. The form of the dispay must include your betas and their significance. If you like, you can put everything in a table. If you're good a graphics, you can display the data graphically. This is especially useful if you are doing a poster session. It looks cool, and gives you something to talk about. However, true statisticians look down their noses at such baubles. In the real world, if you do statistical analysis, you are going to be talking to ordinary people, as well as statisticians. I suggest you get in the habit of using graphical representation of your results, because people intuitively understand it better. Not a lot of people know what significance test are, nor the significance of them (sorry, couldn't resist -- it can be so dry discussing stats).
Finally, you tell your audience what the results mean. Did they answer your research question? Was your hypothesis proven, or did the null get proven? Well, of course, in the real world, hardly anyone presents papers where the null is proven. It's a shame, because it's just as important to know that black cats don't cause accidents as it would be to know they do. But in your case, since it's a student project, and you may not find anything in your data, you may well be presenting the null hypothesis. Well, it means something, so don't be ashamed.
I don't know if this helps, because I've spoken more generally about the goals of various parts of the paper, rather than telling you which tests and tools to use. Unfortunately, as I hope you understand, I can't tell you, without knowing what your research question and data are. But don't try giving me them, because I'm not gonna do it. One other thing. Perhaps at your school there is a statistical support service, or a data librarian? They can help with this, too.