Ever since I had my first car, every single one had a name. My current car, which is an 09 Mini Cooper S, has been blessed with the most tacky name of Bean. If you don’t know why, there is no hope for you.
Fellas, don’t be ashamed of it. I’m a 19yo dude who loves his car so much he had to name it.
My brother drives an 05 Toyota Celica (it’s non-molested. Only work that was done was a rear-ring delete) and he named it James P Cellivan or Celly for short. I pray you get the gag.
I have two separate datasets from two different samples that I want to examine together. My issue is that I measured a different dependent variable for each sample, but the experiment, conditions, and stimuli were the same. To provide more context, I am measuring conflict in a memory task, where I manipulate how difficult the memory task is. For the first sample, I measured reaction time in milliseconds, and for the second sample, I measured accuracy, so the only difference between my independent samples is the data collected from the experiment.
Since, both reaction time and accuracy were significantly predicted by how difficult the memory task was, I am assuming that reaction time and accuracy should be correlated somehow.
My question is, since I have two independent samples that have different variables measured but the same factorial design and conditions, can I predict one dependent variable based on the other?
As of now, my approach has been to collapse the averages based on my conditions and try to train a classifier that predicts the one variable based on the experiment, but I’m not too fond of the idea of losing that many data points. Another approach that I thought about is trying to have one of the variables as a prior using Bayes methods. Any thoughts if these approaches are sound or if there is a better way to approach this problem?
i have to be at a dead stop and i have to hold the clutch down for 1-2 seconds before shifting, otherwise i receive a nice a buzz/grind/jolt.
Only found a few examples of people experiencing the same thing online, consensus seems to be a worn 1st gear synchro or (less likely) something else clutch related.
My mechanic changed my transmission fluid and found metal shavings, he suggested that i replace the transmission. I’d sooner sell the car or keep driving her as is than spend $2000 + labor on a $3500-4000 car.
I started modeling a daily series with an ARIMA procedure, but I realized that my series has a strong seasonal component.
When I looked at the ACF with 30 lags, I noticed that there was a peak every 7 days, and that was very cool. Haha
I plotted ACF for 365 days, and at the end of those lags there were a lot of peaks (like talking about Xmax and those holidays).
I wanted to use a SARIMA model, but I don’t understand well what are the differences between (p, d, q) and (P, D, Q). I mean, when should I include a P instead of a p.
In addition, I was discussing with some friends about the seasonality and the trend. They told me that I have to check wheter there is seasonality or if there’s a trend that’s making that peaks happen. Is that correct? How should I do that?
I’d be very grateful with your answers.
Hi guys, first of all – sorry for my english, it is not my native language. I will try my best to explain what I need.
I am looking for some math book as a gift for a student, where whole math will be nice explained. Is there something like that but without Kindergarten and elementary math?
I mean something like Khan Academy but in the book form (i am lazy watchin videos and i learn better with the book) with nice and simple explanation. Something for someone who wants to start or repeat the math again but in the one book.