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  1. J

    Multiple regression in an event study

    Fixed effects is the way I would go. But if I can offer up some more advise, i'd explore more variables that are measured at a frequency that better lines up with your target e.g. CPI, # of new building permits, etc.
  2. J

    Regression coefficient interpretation

    Your question is a tad vague, but it sounds like you should create another linear regression that will model the average of all other comparable companies. That way you can look at the coefficients of each model and compare that way. If you are trying to see if they're statistically...
  3. J

    which test can i do to satisfy that inferred data is OK

    What exactly are these variables you measured? MC, TVS and VC? It will be tough to approximate and make any conclusions about a variable from a different population that we don't have any samples on.
  4. J

    Regression with no intercept - VIF

    To answer your question, despite you not providing an answer to mine, you would calculate VIF the exact same way as you would with an intercept. VIF is just the ratio of variance in a model with all the predictors to the variance of a the model with just the ith predictor. The interpretation...
  5. J

    Regression with no intercept - VIF

    Why did you remove the intercept? There's almost no good reason for ever doing that. You can introduce immense bias into the model.
  6. J

    Forecast method

    Hi Ron, Thank you for posting on this forum. I struggle with understanding how you are measuring the accuracy of your model as it stands currently. "Reliability" isn't a measure I am familiar with, but you should look into mean absolute percent error, mean absolute error or mean percent error...
  7. J

    Sports data set

    How many observations do you really have though? If you have 10 teams and stats on each of those teams over several seasons, you should have a row for each of the teams each year, correct?
  8. J

    How to interpret Weibull Accelerated Failure Time (AFT)

    What's the distribution you specified? Try changing that
  9. J

    How to interpret Weibull Accelerated Failure Time (AFT)

    Is it displaying an asterisk instead?
  10. J

    How to interpret Weibull Accelerated Failure Time (AFT)

    What software are you using?
  11. J

    How to interpret Weibull Accelerated Failure Time (AFT)

    What was the point of subsetting the data? How did you sample the original dataset?
  12. J

    Which Tests to Run

    If you are simply looking for a positive relationship between two variables (and you are assuming it is a linear relationship) you can use pearson correlation. If both variables are not continuous, then you should look into other correlation measures such as Kendall's tau or Spearman's rho.
  13. J

    Help needed - unsure of what test

    Hi, I think we need clarification when you say "I want to see what happens with a unit increase of fat...". What is the outcome you are trying to measure? What does your sample look like? How was it created? What kind of inferences are you trying to make about a large population? Were these...
  14. J

    What test should i use? THESIS HELP!!

    My apologies, I seem to have provided assumptions for non-repeated measures. The concept of sphericity still holds when assessing variance (of the differences) equality among all combination of groups. When you check normality it should be on the residuals and not your raw (unconditional)...
  15. J

    What test should i use? THESIS HELP!!

    Kruskall-wallis is an alternative to anova, but you can also attempt to make your data normal by transforming it. Box-Cox transformations, log transformation...endless possibilities. But ANOVA also isn't extremely sensitive to deviations from normalcy. If it's not a huge deviation, ANOVA can...
  16. J

    What test should i use? THESIS HELP!!

    A two-way repeated measures ANOVA should be appropriate for this problem, but I would also check to make sure the assumptions of ANOVA are met. To save time, I pulled them from a website. The populations from which the samples were obtained must be normally or approximately normally...
  17. J

    Extreme Gradient Boosting for Survival Analysis?

    I'm wondering if it can handle discrete time, but also is it appropriate for panel data of this nature. It seems that if you threw in results for outcomes that are measured at the end of a quarter, but score observations daily that it wouldn't account for the dependencies in how a sales...
  18. J

    Extreme Gradient Boosting for Survival Analysis?

    A bit of a theoretical question here. A colleague of mine created an XGBoost model to estimate the probability of a sale closing on a software deal. The deal is scored every day and updated when changes in the deal terms exist, etc. However the estimates are extremely unstable day to day. They...