linear-regression

Are there any Linear Regression Function in SQL Server 2005/2008, similar to the the Linear Regression functions in Oracle ? To the best of my knowledge, there is none. Writing one is pretty straightforward, though. The following gives you the constant alpha and slope beta for y = Alpha + Beta * x + epsilon: -- test data (GroupIDs 1, 2 normal regressions, 3, 4 = no variance) WITH some_table(GroupID, x, y) AS ( SELECT 1, 1, 1 UNION SELECT 1, 2, 2 UNION SELECT 1, 3, 1.3 UNION SELECT 1, 4, 3.75 UNION SELECT 1, 5, 2.25 UNION SELECT 2, 95, 85 UNION SELECT 2, 85, 95 UNION SELECT 2, 80, 70 UNION

When we have to predict the value of a categorical (or discrete) outcome we use logistic regression . I believe we use linear regression to also predict the value of an outcome given the input values. Then, what is the difference between the two methodologies? Linear regression output as probabilities It's tempting to use the linear regression output as probabilities but it's a mistake because the output can be negative, and greater than 1 whereas probability can not. As regression might actually produce probabilities that could be less than 0, or even bigger than 1, logistic regression was

This question already has an answer here: Linear Regression and group by in R 10 answers I'm trying to run anova() in R and running into some difficulty. This is what I've done up to now to help shed some light on my question. Here is the str() of my data to this point. str(mhw) 'data.frame': 500 obs. of 5 variables: $ r : int 1 2 3 4 5 6 7 8 9 10 ... $ c : int 1 1 1 1 1 1 1 1 1 1 ... $ grain: num 3.63 4.07 4.51 3.9 3.63 3.16 3.18 3.42 3.97 3.4 ... $ straw: num 6.37 6.24 7.05 6.91 5.93 5.59 5.32 5.52 6.03 5.66 ... $ Quad : Factor w/ 4 levels "NE","NW","SE",..: 2 2 2 2 2 2 2 2 2 2 ... Column r

It is common to want to append the results of predictions to the dataset used to make the predictions, but the statsmodels predict function returns (non-indexed) results of a potentially different length than the dataset on which predictions are based. For example, if the test dataset, test , contains any null entries, then mod_fit = sm.Logit.from_formula('Y ~ A B C', train).fit() press = mod_fit.predict(test) will produce an array that is shorter than the length of test , and cannot be usefully appended with test['preds'] = preds And since the result of predict is not indexed, there is no way

I am trying to do fixed effects linear regression with R. My data looks like dte yr id v1 v2 . . . . . . . . . . . . . . . I then decided to simply do this by making yr a factor and use lm : lm(v1 ~ factor(yr) + v2 - 1, data = df) However, this seems to run out of memory. I have 20 levels in my factor and df is 14 million rows which takes about 2GB to store, I am running this on a machine with 22 GB dedicated to this process. I then decided to try things the old fashioned way: create dummy variables for each of my years t1 to t20 by doing: df$t1 <- 1*(df$yr==1) df$t2 <- 1*(df$yr==2) df$t3 <- 1

This question already has an answer here: Linear Regression and group by in R 10 answers I am running a linear regression on some variables in a data frame. I'd like to be able to subset the linear regressions by a categorical variable, run the linear regression for each categorical variable, and then store the t-stats in a data frame. I'd like to do this without a loop if possible. Here's a sample of what I'm trying to do: a<- c("a","a","a","a","a", "b","b","b","b","b", "c","c","c","c","c") b<- c(0.1,0.2,0.3,0.2,0.3, 0.1,0.2,0.3,0.2,0.3, 0.1,0.2,0.3,0.2,0.3) c<- c(0.2,0.1,0.3,0.2,0.4, 0.2,0.5