# How can I use the sufficient statistics (variances, covariances, means) to estimate a linear regression model in R?

My question is simple: is there a function in R which estimates the linear regresion model in a similar fashion as lm, but only using the means, variances, and covariance (correlations), i.e. the sufficient statistics? I am looking for a function to which I can input these statistics (plus sample size) and it returns regression coefficients and tests.

• The estimates are found as described at stats.stackexchange.com/questions/107597/…. With these in hand, any test you want is obtained in a standard fashion. – whuber Aug 4 '14 at 19:40
• Thanks -- supose sample size n varies over cells of the variance-covariance matrix (for example due to item nonresponse and more particular due to the problem described in this question stats.stackexchange.com/questions/110559/…). Should the relevant tests be adapted somehow? – tomka Aug 6 '14 at 12:31
• That's a difficult question to answer--but is best addressed at the other thread you started. – whuber Aug 6 '14 at 13:58
• I figure that in this situation multiple imputation might be another approach to deal with the problem (see my comment on the other thread). – tomka Aug 6 '14 at 14:15