1. J

    Prediction rankings

    I am trying to predict rankings for these players for any season ahead. For example, how could I predict the ranking for player 1 for season #34? ur=unranked for that season
  2. J

    Modeling and predicting pathology from multivariate clinical data

    Hello, I have a clinical data set that consists of 5 clinical measurements on thousands of tissue samples. Furthermore, each sample has a pathology diagnosis that is 1 of 5 possible diagnoses (all different types of tumors). I am interested in predicting which pathologic class future samples...
  3. S

    Probability of a subjective event based on historical subjective data . Forecasting

    Lately i developed an idea of understanding the behavior of kids in their childhood. I was wondering how kids are molded into different adults in no time. So i created a problem statement based on my ideology and hoped to solve using applications of mathematics. Coming from a non engineering and...
  4. D

    vibration signal_prediction

    Hello, I have a vibration data coming from a motor. i am using R program to do the predictive analysis. My vibration data is a white noise (mean and variance are constant). I need to use this vibration data to do the prediction. How do i use this signal for prediction? Do i need to do some...
  5. M

    Best fitting model for one gold standard value ?

    Hello, trying to solve the following statistical problem in jmp: 1. I got a gold standard y (for example: y = 100) 2. I got three data sets x1,x2,x3: (for example x1=92;94;99 / x2=92;94;99;101;103 / x3=92;94;99;101;103;107;100;99;100) please note: the data sets have similar data...
  6. E

    Predicting outcome with multiple groups

    Hi everyone, I'm currently working with 3 different groups (3 disease populations) and I would like to predict which of the three groups they fall into based on a measurement we did. I was thinking that it should be something like multinominal logistic regression but how can I then get a...
  7. E

    Forecasting/Future prediction assistance

    Hi guys, I've got a bit of advertising data here and I'd like to make a model out of it which can predict future events. So I have the amount of money spent, the number of billboards we've got and how many people we think have seen the billboard. We also have the number of walk ins we...
  8. M

    Which is the better prediction model?

    The aim is to predict the breakdown time of a machine as a percentage of scheduled hours for the next day. So my time series looks like this, Break_down_percentage = 7%, 8%, 10%, 6%, 12 % etc. There are 315 data points which can be used to test the different models. I used ets(), arima()...
  9. B

    Brier score calculation: 2 methods should yield same result

    I have a set of 234 predictions of tennis match outcomes and 5 different prediction models. I use the two different methods for calculating the Brier score described, for instance, here. The first method is: BS=\frac{1}N\sum_{t=1}^N(f_{t}-o_{t})^2 Where N is the number of forecasting...
  10. Y

    Prediction from predicted/residual values compare to standard error of the estimate

    Hello. I have to indicate how good prediction is, by looking at the actual, predicted, and residual values, compare to the standard error of the estimate. I understand that the smaller standard error of the estimate is more accurate, and is a better prediction. But when “Residual score...
  11. L

    MLB Stats Model

    Looks like someone created a statistical algorithm to predict and track itself over the MLB season: Anyone know what this guy is using to predict this stuff?
  12. N

    How to differentiate between a global and a local method?

    Hello, I am trying to write a report based on prediction models and my topic requires me to cover one global method(regression for example) and a local method( nearest neighbour for example). I do not quite understand the term global and local in this context. Some help to explain these terms...
  13. L

    NFL Week 6 regression predictions Atlanta 54% NY Jets 79% Pittsburgh 52% Minnesota 73% Buffalo 55% Detroit 84% Denver 75% Houston 55% Miami 52% Seattle 59% Green Bay 80% Baltimore 51% New England 59% Philadelphia 76%
  14. L

    Logistic Regression of NFL Data

    Week 5 picks from a logistic regression model for the NFL:
  15. Y

    Statistical/ML models when observations have different amounts of input

    Let's say we're predicting an employee's performance review score for the following year based on that emplyee's metrics from each previous year of his/her employment. We might have these training observations below. Note that "2014i" means "that employee's set of input values from 2014", which...
  16. S

    Betting Statistics - Calculation of odds accuracy - what am I measuring here?

    Hello folks! Before anything, an important disclaimer: I am an amateur at statistics and I’m trying to learn by my own. Please bear with me if the questions below sounds like utter nonsense to you guys: In an experiment where you have Team A playing a game of any sports against Team B, both...
  17. G

    Error band for calibration correction of an instrument

    I apologize if this is the wrong area to post this. It seems like it could fall into a couple different areas. I have a question about calculating the expected error around a calibration curve for an instrument. I have a calibration curve for a load cell. It was measured at a few different...
  18. H

    Regression analysis(?) for multiple independent variables

    Hello all, Apologies for posting an elementary query, but my stats is very rusty. Not looking for an explicit solution, necessarily, just a pointer in the right direction. (And if I've posted to the wrong sub-forum, I'd be grateful for suggestions.) I have N records. Each contains M real...
  19. B

    How do risk models deal with state changes over time?

    Let's say you are trying to predict if a machine is going to explode (0) or successfully complete its job (1), and you are trying to determine the impact of certain states on the machine. So say the machine has state A, B, C, and D. The machine starts in state A. Then... A-> B, B->C *OR*...
  20. T

    One observation per factor level combination in regression

    Hi... I was wondering if you can perform multiple linear regression with a mix of categorical and quantitative variables, if you only have one observation per combination of the levels of your factors in the regression model? And how do you interpret predictions? Thanks in advance!