modeling

  1. V

    model a sample

    Please help to simulate a sample. simulate a sample of independent observations of a volume 10 having a chi distribution of a square with 6 degrees of freedom. it's desirable to make it using Python but all solutions are welcome. Please help:):):)
  2. S

    A statistic question.

    Provider Charge(Total) Fraud %Fraud Fraud*%Fraud A §§§§§§§1000§§§§§§§600§§§§§0.6§§§§§§360 B §§§§§§§100§§§§§§§§70§§§§§§0.7§§§§§§49 C §§§§§§§10§§§§§§§§§8§§§§§§§0.8§§§§§§6.4 D §§§§§§§1§§§§§§§§§§1§§§§§§§1.0§§§§§§1 Please ignore "§" above. Which provider should be given alert? I am...
  3. R

    Conceptual queries in modeling

    Hi all, I am from engineering background. I would require your help in certain conceptual questions in modeling. Your help would be greatly appreciated! Following are my few questions... (Pointers on these questions would help me , else you may direct me to any useful resources.) 1)If a...
  4. H

    Linear Regression: Disagreement in confounding with stratification

    Hi everyone, I am performing a simple linear regression testing whether an the results from an aptitude test for our sales force is associated with their profitability (binary variable profitable or not). The score variable is a percentile. I performed a simple logistic regression...
  5. L

    Logistic Regression of NFL Data

    Week 5 picks from a logistic regression model for the NFL: http://trevorbischoff.com/nfl-2015-week-5-predictions/
  6. M

    Structural equation modeling - predictor measured in different group

    Dear all, I am kind of stuk on the following problem: what if you would like to create a model, with several predictors. only one important predictor is measured in a different sample. The characteristics of this sample are similar. for example, you would prepare a model to describe a...
  7. S

    Multiple Inter-Correlated Binomial Events with only One Event Possible for Success

    Hi you’ll, I have a thought problem that might not have an easy/correct answer. It looks to be a Bayesian at first glance – and it might be – but because I am using some complicated machine-learning algorithms, and because of the nature of the problem, the water are muddy for me. I am just...
  8. R

    [Minitab] DOE Model Output vs. Actual Data

    I am running multiple 3 factor DOEs and each one of my model outputs fits very well, all above an 80% fit. The problem I am having is that when I plug our recorded values into my model for parameters x,y and z my model outputs a significantly different/unfeasible value when compared the the...
  9. L

    Modeling heteroskedasticity for dummies

    Hello there! Im trying to model heteroskedasticity with known form using MATLAB. The only source I found was a uni-variate example, but Im working with multiple-variate models. http://www.econometricsbysimulation.com/2012/11/modeling-heteroskedasticity.html My questions are the...
  10. C

    Dealing with linear dependent variables

    I have a large dataset with many subject each with responses from a consecutive year going back 10 years (ie 100,000 persons per year (not necessarily 10 data points per person as they may not have been part of the study in prior years) dating back 10 years). I have data on each specific...
  11. E

    Cross-estimating the independent variables to exclude outliers

    I am working on a dataset where some of the independent observations have measurement issues (i.e. mixed feet and metric). I'm considering cross-estimating all the variables to exclude outliers before building the actual model. In other words, a method for defining and excluding outliers and...
  12. C

    Class Distribution, Boosted Models (gbm) Probability Scores

    All, Problem: I need help to better understand the probability scores that come from the result of a decision tree model. Specifically, I'm using the gbm package from R to create Generalized Boosted Regression Models, but the results I see are common across various ensemble classification...
  13. I

    Is it appropriate to include intermediate outcomes in a predictive model?

    It is quite clear that one should not control for post-treatment variables / intermediate outcomes when the goal is causal inference, but I wasn't sure if the same advice should hold when one's goal is to build a model for prediction. Context for my question: I'm trying to build a model that...
  14. K

    Probability of Breakdown

    I am considering using regression (either Cox or Logistic) analysis to estimate the probability of breakdown of vehicles in a fleet. I have some evidence showing that likelihood of breakdown is influenced by the following variables: sex (of owner) age (of owner) age (of vehicle)...