how to understand the relationship between different predictors

[#1]

From a large car data set, a company would like to have a tool to predict prices for used cars. Therefore they first need to understand the relationship between different predictors (independent variables) and the used cars price.

For the following questions we assume that the price is determined by a number of predictors contained in the dataset.

1. Investigate which continuous predictors have an influence on the price.
2. Investigate which ordinal predictors have an influence on the price.
3. Investigate which nominal predictors have an influence on the price.

How do I best analyse this 

[#2]

Is this a textbook question?

[#3]

Hi @BaggingSpaces55 ,

Unfortunately your question is not very clear. But as far as I understand, if you want to find the relations between car price and those variables that define the car price. I believe you can use a regression analysis to determine that stastical significance and also correlation of those predictors on used car price. If you have a set of data, try using fit model option in JMP. And since you mentioned these factors or variables are independent of each other, you can skip the interaction effects in the model. 

I hope this answer can help you. May be adding specific details can help you to get the right solution.

[#4]

Hi @BaggingSpaces55,

Given the lack of context and precision, I will only offer a suggestion to help you get started.
With your dataset and questions, a good starting point for a "data mining" mindset could be to use the "Predictor Screening" platform, in order to identify the relative importance of predictors on the price, no matter their type/format.

If you need to asses for each factor type their relative importance, you can run the analysis for each factor type separately.

I hope this complementary answer may help you.

If you need further info or guidance, please provide more context.