X1 X2 X[...] X25 Y Q1_2019 23 65 18 32 1,6 Q2_2019 87 32 23 46 1,2 Q3_2019 34 15 63 78 3,2 Q4_2019 85 45 43 65 3,9 Q1_2020 85 43 78 35 1,1 Q2_2020 37 78 54 78 1,5
- I have a very expensive dataset which shows aggregated survey data. These are probably means. I am trying to get the individual data but at the moment that is all I have.
- The shape of data frame is 5x26
- Y data so far is collected data calculated at the end of each quarter via other means The survey is done at the beginning of the quarter.
- Y is my dependent variable and I would like to derive a polynom to predict the exact number based on future X data or at least the probable trend it will be going in the next quarter once new survey data is available. Up, down, stable would be enough
- I have done correlation analysis (all vs all) and there are strong pairwise correlation between several X and Y
- Y comes as a one digit before the comma and one digit after the comma. Since all other values are 2 digits before the comma I would like to multiply it with 10 to transform it into 2 digits before the comma.Is that ok from math/data science perspective?
- 5 records is not much but there are a lot of features. I would like to do multiple linear regression. Do you think this feasible with this data set? What would be objections and risks doing that?
- Would upsampling the dataset help me with anything here? Or could I just work with the five records?
- With the strange shape of the dataset especially the low number of records do you think that sufficient precision can be reached?
- How could I calculated the maximum possible precision/discriminative power possible with this dataset? (I am looking for strong arguments why they should give me access to the complete dataset)