Analysis for a Traffic Collisions survey data using SPSS v22(SPSS Statistics, Statistical Analysis). professional Statistics experienced persons only bid please

please read the project description before bidding to save your time and mine as well. only professionals are welcomed as this is a critical project and time is very critical. Data and details will be shared after awarding The project would have 3 milestones, we need the following: 1) To find the correlation between all parameters and the significance of each parameter: 2) To run all the statistics checks 3) to write up a report describing the work. in details: I am preparing an analysis of road accidents. Data was collected by a survey. I want someone professional in using SPSS to run some tests on about 35 specified parameters then writes about 1000 words on the analysis - plagiarism free. so the project has car crashes data as F(Y) and the other 34 parameters are F(X). I need the following: • Significant parameters analysis to exclude the non-significant parameters • Correlations analysis I need to use SPSS to create 3 models using : • negative binomial • multiple regression • Poisson • Important note: the regression should be done on 6 predictors each time meaning that for the negative binomial we will start with the first 6 predictors and then on the same model we will try the second 6 predictors and so on and then finally for the same model we will choose only the significant predictors from the previous trials and then run the model one last time , and the same for the Poisson and negative binomial. Then a comparison between the three models with a write up not less than 1000 word about each model • introduction and illustration for each model. • show the significance of each model and how this model is good and what are the bad points of this model. • show the 10% significance predictors and the 5% significance predictors of each model. • shows the equation of each model and when applied on our data what would be the constant values for the coefficients. • lastly, the report from the comparison of the models should state the best fit model for our data. the model after finalizing should be able to predict the number of car crashes based on the parameters and coefficients used. a sample file for the data attached