I am reading Introduction to Statistical Learning, and at the end of each chapter there are labs in R. For LDA in the lab we find the coefficients of linear discriminants. So say the predictor has elements x1 and x2, the book then says the observation belongs to class 1 if the sum of the products of the elements and their corresponding coefficient is largest, and the other class if it is low (only 2 classes).
The book says these coeffecients are present in equation:
δk(x) = xT (Σ^−1)μk − 1/2 μT k (Σ^−1)μk + log πk where xT is the transpose of the predictor vector, μk is the mean vector for class K, and Σ is the covariance matrix. However, I don’t see where these coefficients are present.
I am planning pretty long road trip (1800km, about 18 hours) in two or three months. What is a good thing to do to my car before i go, and what to take with me in case of emergencies? I will travel for two days and have a friend with me. Also what is longest road trip you took?
I’ve been trying to get better at driving stick on my new ’15 fiesta st for over a week now and I don’t feel like I’m making progress. I’ve sat in a parking lot for hours and practiced 1st gear starts with no gas and with gas, I’ve been driving every day in a city with uphill lights and stop signs, and my starts are still jerky and bad, my hill starts are worse, my 1st-2nd shifts aren’t great, I’m very slow with downshifts, and I’m still stalling the car every day. The other day I stalled the car like 20 times in traffic. I feel like I’m going to destroy this car, and I get nervous to drive it anywhere now. I haven’t even tried to parallel park, because it feels like my reverse gear launches me into light speed at like 1200rpm.
Does anyone have any advice that I haven’t read before? I’ve watched all of the videos and read all of the articles but the typical “rev it to 1500 and slowly let out the clutch” is obviously not translating here. I’m on the verge of trying to find a way to sell this car and just get another one.
I am working on a project that involves case-control design. Participants with and without a particular intervention (receiving treatment X or control)) are matched on sex, diagnosis and age (+/- 7 years) and self-reported questionnaires are used to collect data related to various health outcomes.
The outcome variables are the count data (ranging from 1 -7) with most having 0, 1 or 2 events (Poisson distribution). Because the data are matched I need to account for the correlated data and I am wondering what an appropriate analysis would be if I am interested in examining a model for both conditional and marginal distributions. I have looked at General Estimation Equations or Generalized Linear Mixed Model but need some guidance on which approach may be most appropriate.
I went to the Goodwood Festival of Speed last month and saw someone wearing a really cool T-shirt, it had a kind of Japanese manga illustration of two JDM cars drifting and lots of smoke coming off the tyres!
I have desperately tried searching for this shirt over the past couple of days but have been unable to find anything remotely like it. It was unique in that the design was printed large on the rear, across the entire width of the shirt.
After considerable searching, the only decent car-themed designs I found have been from Blipshift; but this site is based in the US and I don’t find anything there particularly appealing. Ideally I would like a JDM inspired drifting shirt (as mentioned above) with a large space-filling design, but nevertheless I am open to all styles and types of cars!
Any suggestions most appreciated.
So, my lab mate has a project she needs to run characterizing printing parameters for an experimental ink formula and printer setup. It has four dependent variables, and seven independent variables. She would like to know what the optimal settings are for the four dependent variables.
Samples are time consuming to make.
My current plan is to use response surface methodology. In the first step, we would screen independent variables using a 1/4 fractional factorial DoE and use regression to characterize explanatory variables. We would remove variables from the second round if the p-value and effect size are both insignificant (a hybrid of the backward selection algorithm). I will also consider reducing VIF when choosing variables to remove. Second, we would use a full factorial design to characterize the surface. Alternately, I would use a central composite design, relying on the scarcity of effects principle.
For the fractional factorial, I was considering a 27-2 design (1/4 factorial) with five replicates for a total of 160 samples. If possible, I was wanting to make all five replicates in a single batch, with a total of 32 batches.
In the follow-on full factorial, assuming only three factors survive, we would then test 3 levels, with five replicates. This should mean that we would need to make 27 more batches, again assuming each replicate comes from the same batch.
I am sure there are things I am not considering, and I would love help knowing what they are.
tl;dr: Yes, people should be doing their homework when buying their car. But dealing with the website and showroom employees has shown me that unless you know the questions to ask, new EV buyers are going to have a bad time.
I’ll come right out and say that I categorize myself as a Tesla fan. No doubt about it, I enjoy what they’re doing and have been considering the move into one for some time now.
Last weekend, I decided to haul myself up to a local (two hours away from home) showroom to take a look at a Model 3 and see if the wife was
- Employees were willfully ignoring that Tesla has hit the $7,500 federal tax credit and aren’t telling customers unless asked
I brought this up to the gentleman that I was dealing with in the showroom and he told me that as long as I ordered that day, I’d be fine.
This isn’t actually the case, you need to take delivery of the vehicle by 12/31/2018 or you’ll receive the halved ($3,750) incentive. Which puts you right at the end of the 2-4 month timeframe for the Model 3, but if the production is delayed (which it has been several times already), then you may not take delivery. After pressing the issue, another employee who got involved confirmed that the delivery couldn’t be guaranteed in time to receive the tax incentive.
- Both employees and website falsely advertise the Pennsylvania EV rebate as being available for the purchase
Here is a screen grab from Tesla’s site and the actual terms of the Pennsylvania rebate. Even if it’s hidden in fine print on Tesla’s site, it’s deceptively listed and employees didn’t seem to be trained on this aspect.
The employees knew I was there to look at a Performance Model 3 (MSRP $64,000) and the website had the configuration. PA caps out the incentive rebate at $50,000. Technically the only Model 3 trim that would qualify for this that you can buy right now is the RWD/Long Range at $49,000 without any other options.
- No financing = no refund of deposit
Honestly, this was the most frustrating part for me, and it was mostly due to the existing community of owners who would rather give me financial advice when I asked questions about the policy and financing. I planned on dropping $12,000 on a downpayment, of course I’m going to ask questions when I’d potentially risk 10% of it on an unknown lol.
I prefer to use a credit union outside of Tesla because of better rates/terms, only to find out that the process really doesn’t line up well with people who are on the fence of getting approved. You first put a deposit ($2,500) on the car and wait to be assigned a VIN as confirmation that you have been allocated a vehicle; then you start the financing process. But this can take weeks or even months.
For example of why this could be concerning to someone; I have a single derogatory medical remark on my credit (<$100 debt) from 2011 that I have tried to pay again and again to no avail because of the collection agency and hospital disagreeing with who would take the payment. Most underwriters wouldn’t skip a beat on that, but in order to qualify for certain rates, the number is needed by some creditors. One of my credit reports is 804 because it doesn’t have the mark, another is 695 because it’s still present. The APR difference is as much as 2% for some lenders; over the life of the loan, that amounts to nearly a $5,000 discrepancy, or enough for me to delay my purchase.
Let’s say you seek out financing and put your $2,500 deposit down on the Model 3. Two months later, some event happens and you are no longer eligible for financing. Long shot, but whatever. Humor me here.
If you have a VIN assigned, you “may” or “may not get your money back” depending on who you ask. The customer service rep told me over the phone that they “[didn’t] see why not”, the rep at the showroom simply said “I don’t know”, and the internet is filled with horror stories.
If you read the actual sales agreement, you quickly understand that it is, in fact, non-refundable once you have a VIN, something which every Tesla employee I spoke with was unable to concretely say, yet there it is in writing.
Now, I’m not arguing that Tesla should give you your money back. If you can’t uphold your part of the agreement, then that’s that. But making it difficult to plan that far ahead on iffy credit (FWIW: Most lenders I spoke with require a FICO of at least 780 to get advertised rates below 5% on 60-84 months, among other requirements) can easily be frustrating, and the employees being unable to explain the sales agreement is going to hurt somebody.
- So what’s the point?
I just don’t like shady business practices. I do think that everyone should be doing their homework before committing to buy anything, but this entire ordeal left me with a sour taste in my mouth. Transparency is a huge selling point to me, and I don’t tell like there’s enough of it going around.
Imagine that I have predictors A, B, C and D; with predicted value X.
I am interested in how well D predicts X, over and above predictors A, B, C.
Is it enough to run a multiple regression model with all four predictors, and then interpret the coefficient for predictor D? Or, is it better to run a model with and without predictor D, and then compare the two (e.g., with an F-Test and R2 change)?
What are the advantages/incremental pieces of information that either approach provides?