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    Nonlinear regression

    I believe you can use the linear model function lm in R. E.g. if Y = a + bX + cX^2, you can use the below lm(Y ~ X + X^2) Let me know if this works for you.
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    Clarifying the concept of a probability mass function

    Yes, both PMF and Density Functions need to sum up to one for all values of R.V. X. The set equation you have mentioned is also correct and non-conflicting from PMF/PDF definitions. What is your exact question here?
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    How can I enhance my linear regression model?

    If you are trying to measure the boost a particular promotional event would have on sales, then you need to add a dummy variable for each event, exclude one from the model to avoid collinearity and run the regression to see the effect. If you take daily sales data as the Outcome Variable, then...
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    Interpreting coefficients in a multiple regression

    Hi Bentley, First of all, it is difficult to cover and record ALL variables that might influence a particular outcome variable. Usually, and it seems in your case, that the outcome variable is dependent on more than one variable. Therefore, regressing the outcome on IV1 and IV2 alone will give...