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  1. staassis

    Interpret Linear Discriminant Analysis and visualization

    LDA requires that all the features be scale variables, not categorical or ordinal. Moreover, it requires that all of the features had joint normal distribution.... I know. Moreover, LDA requires that the covariance matrix of features in class A be the same as that in class B. That is what I...
  2. staassis

    Interpret Linear Discriminant Analysis and visualization

    You do not have to scale. However, the variability of the features and their correlations must be the same in each class. If that assumption fails, LDA may misfire. There are other situations in which LDA misfires and is suboptimal to other classification methods... I'm afraid there are...
  3. staassis

    urgent help required - data analysis of time course

    The p-value from the omnibus ANOVA test may be larger or smaller than the p-value for a t-test comparing just two particular groups. What matters is that there is only one ANOVA p-value but many t-tests (multiple testing). And none of those t-tests summarize the whole dynamics in a statistically...
  4. staassis

    Interpretation of second difference of a predictor

    To some extent, the second difference is measuring whether the companies have generated more casfflows than last year or less.
  5. staassis

    urgent help required - data analysis of time course

    Running multiple t-tests would no be right here... This is a set-up for repeated measures ANOVA with two factors: 1) Time: within-subjects factor, 2) Virus Type: between-subjects factor. You should start with the full factorial model (including the interaction of Time and Virus Type) and drop...
  6. staassis

    Interpretation of second difference of a predictor

    Yous said earlier: " using S&P 2nd difference". How is that "the change in absolute changes"? You would need to state clearly what's going on.
  7. staassis

    urgent help required - data analysis of time course

    Need to see the research questions, description of the data + experiment, attempted statistical methods, summary of the results. Just like I mentioned in my previous post... On the other hand, you keep repeating: "I did this little thing. Here's the output.."
  8. staassis

    Interpretation of second difference of a predictor

    The return on the S&P portfolio is indicative of good or bad things happening to GDP, through market sentiment and such.
  9. staassis

    urgent help required - data analysis of time course

    Typically, visitors to this side come with specific questions. They do not say: "Do the whole analysis for me. Please, please." You would need to state what you have done already, what the obstacles were and which research / programming / other questions you have now... Somebody nice here...
  10. staassis

    Need to find data | Repeated-measures/split-plot ANOVA

    Can't you just cook up the data set if you do not care where the data come from? What is the ultimate purpose of the data set? Is it having something to test your code on, or is it something more serious?
  11. staassis

    Logistic Regression vs survival analyses in large cohort studies

    Survival analysis will address all these questions if you study it carefully. This includes participants falling into different categories and some of them allowing intervention with a delay. It is not good to bin the data into cohorts. By applying such a crude discretization you are ignoring a...
  12. staassis

    Hiring statisticians for a long-term trading project

    I guess, it would be quickest if you could send me your e-mail address. For some reason, I was unable to send you private messages on this web-site. Getting the "oops" message.
  13. staassis

    Hiring statisticians for a long-term trading project

    Dear Kaan, I would not go for a full-time job but I would be interested in potential collaborations on the project basis. If such a set-up might be useful at least at the early stages of your trading process, please feel free to check out my page and get in touch. Best.
  14. staassis

    Logistic Regression vs survival analyses in large cohort studies

    If you have recorded the days on which participants died or dropped out of the experiment, then survival analysis is a place to start. SPSS has basic survival analysis functionality. The best implementations of survival analysis are in Stata and R, in my opinion... In the ideal world (if you had...
  15. staassis

    Correct statistical method to use

    Are you asking how this image is likely to progress in time, or are you interested in probability of touching for other images (other people)? Either way you need to record many images / people, not just one.
  16. staassis

    Which Recommendation Algorithm for Sending Offers to Clients?

    The chapter on cluster analysis that I linked is very good. It contains almost no formulas. This is almost as simple an introduction as it gets.... You do have to learn appropriate statistical methods if you want to use statistics in solving your problems.
  17. staassis

    Which Recommendation Algorithm for Sending Offers to Clients?

    In marketing, a classic solution is cluster analysis. However, I hope what you've shown is just an excerpt of data. Any high-tech statistical method requires hundreds of observations, not a dozen... In your case, if you have many characteristics of each client (economy, country, browsing...
  18. staassis

    Time series Residual

    Because a "forecast" is not supposed to use all the data, only the past data. If you have GDP for years 1980-2017, you are allowed to use only years 1980-2000 to predict the GDP for year 2001. On the other hand, any model attempting to explain the overall dynamics will use the whole sample. And...
  19. staassis

    Interpolated Rates vs Initial Rates

    You could run a leave-one-out cross-validation and calculate the relative approximation error. However, such a calculation would be very approximate since it would assume the same approximation precision in different segments of the curve. The assumption would not be very realistic. For example...