# How to start analysing and modelling data for an academic project, when not a statistician or data scientist

I have collected data for a PhD thesis, and need help understanding how to build a road map to do analytical and statistical analysis. The PhD is not itself in statistics or machine learning, but I would like to understand what are the steps and type of analysis that I have to follow for analysing data for an advanced degree? In general, how should I approach such a problem?

In the data I have collected, there are 623 observations including one continuous dependent variable and 13 independent variables (continuous, categorical, and ordinal) that are defined based on the researcher experience and literature review.

I considered planning to do several regression analysis to predict the dependent variable and study the effective factors (if they are positive, negative, and their magnitude) on it. I've tried multiple linear regression including different transformation on independent variables. On the other hand, I'm not sure if I should study each independent variables through the time and forecast their values in the time horizon?

Here are the steps in my mind so far:

1. Plotting the scatter plots of different independent variables vs dependent variable to define outliers and check if the model is linear also with respect to coefficients

2. Removing the potential outliers

3. Splitting the data into two data sets to build the model and validate it after that.

If the model is linear then:

4. Performing the multiple linear regression

5. Performing the multiple linear regression including different transformations to enhance the model

6. Validating the model

7. Doing the quantile regression

8. Doing supervised learning machine etc.

If the model is not linear, I may instead need to use non-linear statistical techniques.

Any feedback would be highly appreciated. My goal is to build a clear and robust road map for this part of the work.

• I don't think you've said what you are trying to do. Predict the dependent variable? what regression have you tried? – Sean Owen Sep 19 '15 at 7:05
• I explained that I want to predict the dependent variable and study the coefficients also. I mentioned what analysis I have done so far. Thank you. – Amir Sep 19 '15 at 21:23
• . . . unless you are asking about analysis for data collected as part of your PhD (which you are studying now)? In which case I think it may be time to talk things through with your advisor. – Neil Slater Sep 20 '15 at 8:20
• Thank you Neil. I'm doing the analysis for data collected as part of my PhD. I had some graduate statistical analysis courses but they were not as helpful and structured as I expected. On the other hand, my adviser is not well-experienced in this field. Hence, I want to get the experienced advice to build a clear plan for my analysis which must be heavy enough for PhD also. Here are the different steps in my mind so far: 1- Plotting the scatter plots of different independent variables vs dependent variable to define outliers and check if the model is linear also with respect to coefficients – Amir Sep 20 '15 at 12:39
• I would completely avoid removing "potential outliers". You should certainly identify values that seem a bit odd and if there is some clear reason for removing or changing the value (such that from field notes you see that there is a transcription error), only then remove them. But otherwise, that's data which should be kept. Everything else might be just noise. – JimB Sep 21 '15 at 21:24