now have said before that the coefficient is way of determining which items matterand that' only true though if your input data is normalized that isif everything' on the same scale of to if it' notthen these coefficients are kind of compensating for the scale of the data that it' seeing if you're not dealing with normalized dataas in this caseit' more useful to look at the standard errors in this casewe can see that the mileage is actually the biggest factor of this particular model could we have figured that out earlierwellwe could have just done little bit of slicing and dicing to figure out that the number of doors doesn' actually influence the price much at all let' run the following little linezhspvqcz eg%ppst nfbo little bit of qboebt syntax there it' pretty cool that you can do it in python in one line of codethat will print out new %bub'sbnf that shows the mean price for the given number of doorsi can see the average two-door car sells for actually more than the average four-door car if anything there' negative correlation between number of doors and pricewhich is little bit surprising this is small datasetthoughso we can' read whole lot of meaning into it of course activity for multivariate regression as an activityplease mess around with the fake input data where you want you can download the data and mess around with the spreadsheet read it from your local hard drive instead of from httpand see what kind of differences you can have maybe you can fabricate dataset that has different behavior and has better model that fits it maybe you can make wiser choice of features to base your model off of sofeel free to mess around with that and let' move on there you have itmultivariate analysis and an example of it running just as important as the concept of multivariate analysiswhich we exploredwas some of the stuff that we did in that python notebook soyou might want to go back there and study exactly what' going on