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  1. Buckeye

    Engineering Project question

    you could use method of steepest ascent / descent.
  2. Buckeye

    RStudio CSV Import of incorrectly formatted data

    Perhaps install the readxl package and use read_excel But it does seem like the format might be a pain. Afterall, 90% of the time is used data cleaning. You might look into using dplyr to help clean things up.
  3. Buckeye

    Probability/standard deviation

    Can find the z scores of each?
  4. Buckeye

    is regression analysis possible for this dataset?

    I agree. I was speaking from a standpoint of being able to fit the model. If I have 10 observations and 10 predictors I don't have any degrees of freedom to estimate error.
  5. Buckeye

    For your viewing pleasure.

  6. Buckeye

    is regression analysis possible for this dataset?

    I would not do regression. It seems you have almost as many parameters as observations. A good rule of thumb is 10 observations per predictor.
  7. Buckeye

    Create a dummy conditional on two other variables

    How many categories do you have? If you have 3 categories, you need 2 indicator variables. If you have 4 categories, you need 3 indicator variables.
  8. Buckeye

    In statistical learning, is the learning function a random variable or a constant?

    We take a random sample and gather data on it (height, weight, occupation, residence, etc.). These are the x's or inputs to the model. They are fixed because we know what they are. The parameters of the model (the beta coefficients) are also fixed, but unknown. We use statistics to study the...
  9. Buckeye

    Need help with interpretation?

    Well, I can tell you that a contrast is a linear combination of parameters in which the coefficients add to 0, Contrasts are often used in multiple comparisons and hypothesis testing. Suppose I have two groups with means mu1 and mu2, respectively. An example contrast might be H0: (1/3)mu1 =...
  10. Buckeye

    Constructing intervals VS hypothesis testing?

    There is an underlying hypothesis in any confidence interval. If 350 is in the interval, you fail to reject. We never accept the null. If 350 is outside the interval, you reject the null. Confidence intervals are often constructed to supplement a "test". This includes t tests amongst many...
  11. Buckeye

    Help with graph

    Well, maybe a histogram of the tumor size? If it's skewed, you can use median and IQR to describe it. Sometimes you don't need to waste ink with elaborate visualization.
  12. Buckeye

    calculating the effect size

    I think there are multiple ways to think of effect size. I'm not familiar with what you've mentioned here. But, we can get a sense of the effect size by constructing confidence intervals on the coefficients of interest. I suppose R^2 is a measure of effect size as well
  13. Buckeye

    IVs in Regression

    That is called simple linear regression.
  14. Buckeye

    R^2 and r^2 in multivariate regression to exponential function

    In this case, I have no idea how to help. And I have not heard of what you're talking about.
  15. Buckeye

    R^2 and r^2 in multivariate regression to exponential function

    I am not sure what you are asking. But r^2 lower case or capitalize is the same measure in a linear model. It is the proportion of the variance in the response that is explained by the linear relationship with the predictor(s). Thus, r^2 would not be an appropriate measure if the relationship is...
  16. Buckeye

    Thoughts on Multicollinearity

    Thanks for your insights. You all cleared up some confusion!
  17. Buckeye

    Thoughts on Multicollinearity

    Greetings everyone, This might be one response long or several. I would like to have an exhaustive list of issues surrounding multicollinearity. In my studies, it isn't always clear to me when multicollinearity is a problem. I know how to spot the issue (large se, high correlation between...
  18. Buckeye

    Simple Probability

    P(A) + P(B) - P(A and B),
  19. Buckeye

    Observations for logistic regression

    I don't believe there is a rule. But because so few people have it, perhaps it will show up is an insignificant predictor.
  20. Buckeye

    Baseball regression analysis

    I agree with noetsi in some respects. Stepwise regression isn't necessarily bad. But when you build a model the p values are not what they used to be. In other words, you shouldn't do inference once you obtain a model. A pairs plot of all the preds against the response might help get rid of...