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I'm not from the data science area, and I need to perform some statistical analysis on my data, but I don't know which analytical tools I should use.

Number of variables - 6
Independent Variables 3 - (one is scalar (e.g., 2,4,6,8,10) and the other two are nominal (e.g., A,B,C and Lote 1, Lote 2, Lote 3, Lote 4, Lote 5)
Dependent Variables - 3 (all scalar variables and dependent on all three independent variables, they are basically performance metrics)

One question I would like to answer is how the dependent variables behave when the scalar independent variable increases, taking into account the nominal variables. The picture below shows a sample of my data.

Sample Data

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You could start with plotting the relation between any relevant pair of variables, typically with a simple scatter plot and possibly using color to represent a third variable.

Pearson correlation coefficient is a simple but useful measure of association between two variables. By calculating it between two variables on the whole data and then on subsets based on specific conditions (e.g. third column is A) you can observe the effect of the condition, i.e. see if it increases or decreases the correlation level.

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  • $\begingroup$ Thanks for the advice. I'm doing it in SPSS atm, and it seems to work well to understand the relations. I need to understand some results concepts now (e.g., Sig (2-tailed)).. $\endgroup$ – Torsten W. Aug 23 '20 at 15:29
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You can start with RapidMiner tool, which provides rich graphical interface to load and analyze structured data and unstructured data like text, images, and media. It can also transform unstructured data into structured. Here is link for the tool !

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