Statistics Help @ Talk Stats Forum - Regression Analysis
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Linear regression, linear models, nonlinear regressionenWed, 01 Jul 2015 20:51:30 GMTvBulletin60http://www.talkstats.com/images/misc/rss.pngStatistics Help @ Talk Stats Forum - Regression Analysis
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Time series regression
http://www.talkstats.com/showthread.php/61429-Time-series-regression?goto=newpost
Mon, 29 Jun 2015 20:51:28 GMTI am trying to learn how to conduct regression with time series data [historically I have used univariate methods that is ESM or ARIMA - multivariate ARIMA simply is too time consuming to consider given the need to prewhiten]. I intend to use ARDL which is time series with autoregressive error with the added ability to do lags of the dependent or independent variable.

A question that I encountered before is if univariate analysis of variable (either dependent or independent) indicates that the variable is non-stationary do you difference it in regression as you would in ARIMA? Secondly, assuming you do difference variables, what do you do if the variables are integrated as of a different order. For example one is I[1] and another I[0]?

Does cointegration, something that is doubtful theoretically with our data, change any of this?
]]>Regression Analysisnoetsihttp://www.talkstats.com/showthread.php/61429-Time-series-regressionHow to Handle Blank Fields in Multiple Linear Regression
http://www.talkstats.com/showthread.php/61418-How-to-Handle-Blank-Fields-in-Multiple-Linear-Regression?goto=newpost
Mon, 29 Jun 2015 13:24:30 GMTI am building a regression with 45 samples across 20+ independent variables. I am randomly selecting subsets of the variables and running many...I am building a regression with 45 samples across 20+ independent variables. I am randomly selecting subsets of the variables and running many combinations of regressions to help avoid multicollinearity. However, my main issue is concerning missing data in my samples. Each of the 45 samples is missing 1 (if not more) value(s) of independent variables. Ideally, I would be able to gather the missing data, however this is not feasible in this specific situation. Instead, I am seeking common alternatives to handling missing data in a regression analysis that will cause the least impact to the strength of my analysis. One idea is to use the median value for each independent variable across the samples, and populating all missing values with this figure. Another idea is to use all other independent variables to 'predict' an appropriate value for each individual variable with missing data, and use this prediction equation to populate the missing fields. What are other common methods for approaching a regression analysis with blank fields in the dataset?

Thank you for any help you can provide!
]]>Regression AnalysisAhmntzhttp://www.talkstats.com/showthread.php/61418-How-to-Handle-Blank-Fields-in-Multiple-Linear-Regression