So I had a fuel return pipe that developed a crack and some of the fuel (diesel) was spilled around. It was fixed and the shop said they washed the engine to get rid of smell (and I can see engine was indeed washed) but there’s still some smell and that it’ll go asay on its own after some time.
Well it is true, it is weaker and only occasionly I can smell it, but it’s still there and it’s very annoying. So, is there anything I can do myself to help it evaporate (or ehatever it needs to do) faster, so it stops smelling? After the car hs been driven, when you park it and walk around it you can smell it on the passenger’s front side. Less than when it was leaking, no doubt, but still…
I guess as it was a high-pressure leak it got sprayed around the engine bay, maybe on inside of the bumper or any place that is hard to reach so when they were washing the engine I soubt they reached ALL of the spots.
What can I do?
Does someone know an article published by a high impact/reputation journal about analysing a data set using some statistic/data-mining methods? I need it to be a high reputation because I need good references for my thesis.
The idea is to apply methods to predict/classify, for example, a data set using statistics.
Thanks in advance
I’m a college student taking a general maths, and it’s incredibly hard for me to grasp some derivative forms. For example: f(x)= tan2(x) + ln(cos2(x)) The answer is 2(tanx)3, however, it didn’t occur to me to tackle each part individually, and this happens often. I honestly like math very much, and was interested in derivatives after reading “Applied Mathematics: Body and Soul”. This may seem like a stupid question, but how should one study derivatives? Most of the formulae I have memorized, but struggle to apply others. Also I struggle to understand the form of the equation in order to apply the formulae. Should I just practice every possible case? Or is it possible to break down each equation to solve the easier? Any advice would be helpful! Thanks 🙂
I am doing a regression analysis with two dependent binary variables: meeting a clinical threshold for 1) anxiety (A) and 2) depression (D). In each case, the two levels of the variable are: 1) subject meets clinical threshold for… and 2) doesn’t meet threshold.
The aim is to explore what clinical and social factors (e.g. psychological coping strategies) predict whether someone meets the clinical threshold for anxiety or depression. There are 25 of these predictors…
To reduce the number of tests, I have argued for having a primary analysis with just one dependent variable – which would be a combination of anxiety and depression variables (whether someone meets the threshold for either A or D). A chi^2 of the association between A and D binary variables is significant (p<0.001) and I ran a logistic regression with one as the indep and the other as dep – the OR was 8.92, p<0.001. As such, I think it seems reasonable to merge the variables as the social and clinical predictor regression analyses are likely to be very similar for each dependent variable (A and D).
However, a clinician I am working with very strongly wants to preserve some differentiation between anxiety or depression – that is, they want to know what predicts anxiety vs depression vs both vs neither. I am wondering if there is a sensible secondary analysis I can propose to explore this. The sample size is n=250.
TL:DR – I have two binary variables that I want to predict using social and clinical factors, and I would like to do it in a single analysis. What should I use?
Thanks v much in advance!
This is a really helpful community and thank you again to all those who have helped me with questions in the past!