About doudou66

doudou66 · ‎06-20-2012

Thank you very much for your advice; it is really helpful and enlightening.

doudou66 · ‎06-19-2012

Dear Steve and PG, Thank you very much for your help. I really appreciate it. I do not have specific questions; I am asking this one because someone asked me and I don't know the answer. Referring to "a more complex model that includes interactions" (in Steve's post), if I have enough data, will such a model be my best pick?

doudou66 · ‎06-18-2012

Hello , All Suppose I have a dataset with several variables ( say Var1-Var20);and suppose Var20 is a categorical variable with following values: low, medium, high. I have two choices: Choice 1: build one single model using all variables (Var1-Var20) Choice 2: split the original dataset into three so that dataset1 only contain those observations of which Var20 falls in "low", dataset2 only contain those observations of which Var20 falls in "medium", and dataset3 only contain those observations of which Var20 falls in "high"; and build three models using Var1-Var19 for dataset1, dataset2, and dataset3, respectively. My question: which choice should I go for? If it is choice1, what is the advantage of this method? what dose choice2 miss? Thank you very much for educating me.

doudou66 · ‎06-05-2012

Thank you very much.

doudou66 · ‎06-05-2012

Thank you very much for helping me. If variables are not on the same scale (say, variable A is ~1000, and variable B is ~1), should I do normalization on each original variable (i.e. (original_variable - mean)/standard_deviation ) before doing PCA? or is there any other method?

doudou66 · ‎06-05-2012

Hello, All Suppose my data have two variables A and B. Total_Variance_Original_Data = Var(A) + Var(B) + 2Cov(A,B) Now I do Principle Components Analysis and get two new variables, say PC_1 and PC_2, with eigenvalues λ_1, and λ_2; Total_Variance_PCA_Explained = λ_1 + λ_2 My question is: are theses two variances equal to each other, Total_Variance_Original_Data = Total_Variance_PCA_Explained ?

doudou66 · ‎05-31-2012

Sorry for the late response and thank you very much.

doudou66 · ‎05-25-2012

Hello, All Suppose I have two datasets: dataset_1 and dataset_2. The two datasets have same variables (variables Y and X, and variable Y is binary); however, the observations of two datasets are different. I did logistic regressions on two datasets, respectively; and I got coefficients of X from each regression, say, coefficient_X_dataset_1, and coefficient_X_dataset_2. My question is : how can we know if these two coefficients are significantly different?

doudou66 · ‎05-21-2012

I truly apologize for the late response and thank you all very much for help.

doudou66 · ‎05-18-2012

I know when we build a predictive model, we don't want to use too many predictor variables because of potential overfitting problem. But why "too many" predictor variables will result overfitting? "Curse of dimensionality" is usually seen in data mining; how to understand "Curse of dimensionality" in statistics? Is there anything to do with overfitting? Thank you very much.

doudou66 · ‎05-17-2012

Thank you very much for help.

doudou66 · ‎05-16-2012

Dear All If outlier is identified in the data and is not due to error, what is the common way to do with those outliers? Is Proc Robustreg always the better way to go than simply removing the outliers? Thank you very much for your input.

doudou66 · ‎05-04-2012

Thank you very much.

doudou66 · ‎05-04-2012

Thank you all very much for great help.

doudou66 · ‎05-03-2012

Hello, All Can anyone suggest me an article (or a paper, a book, etc) about variable transformation in model building? thanks a lot.

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Re: one model vs multiple models?

Re: one model vs multiple models?

one model vs multiple models?

Re: Principle Components Analysis clarification needed

Re: Principle Components Analysis clarification needed

Principle Components Analysis clarification needed

Re: how to compare regression results?

how to compare regression results?

Re: how to understand "Curse of dimensionality" in statistics?

how to understand "Curse of dimensionality" in statistics?

Re: one model vs multiple models?

Re: one model vs multiple models?

one model vs multiple models?

Re: Principle Components Analysis clarification needed

Re: Principle Components Analysis clarification needed

Principle Components Analysis clarification needed

Re: how to compare regression results?

how to compare regression results?

Re: how to understand "Curse of dimensionality" in statistics?

how to understand "Curse of dimensionality" in statistics?

Re: Question about outlier

Question about outlier

Re: variable transformation

Re: a question about variable transformation

variable transformation