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I need to interpret a residual table which was fitted by y_.0123 (final residual) against the x_1.023. The table represents residuals produced by each variable. # data pts value of Y Bo B1 B2 B3 212 small small small small small 14 small small large small small 10...

I need help with this question and how to proceed need to rewrite the following regression fitting, can someone assist with this problem. I don't know how to enter subscripts so I indicated sub(): K A e At +K B e Bt , where A and B are given. I need to rewrite K A e^ At +K B e^ Bt in terms of...

I have data for 1970 and 1980 parity ratios, Parity 0 1 2 3 4 5 British 0.879 0.797 0.558 0.484 0.480 0.531 Cubans 0.939 0.920 0.818 0.796 0.789 0.774 Blacks 0.748 0.778 0.787 0.789 0.774 0.759 Parity 0 1 2 3 4 5 British 0.929 0.882 0.646 0.531 0.476 0.496 Cubans 0.961...

Please assist in how do I remove every tenth observation from a data set to use as a test sample and the remaining data as a training sample building a linear regression then a linear regression using AIC: I know how to set up the linear model but how do I remove observations in an equation...

Please help in understanding the differnce in ridge expression and why I get the different results using 0.02 gridge1<-lm.ridge(divorce~.,data=divusa,lambda=0.02) gridge year unemployed femlab marriage birth military -0.0315 -0.1182 0.4317 0.1094 -0.1279 -0.02719...

I have a regression model fit<-lm divorce~unemploy+fem+marriage+birth+military) the data set has 77 observations and 6 variables and interpretted the data. Now, I need to fit a ridge regression to the data. How do I do that in R?

when plotting to examine the leave out one differences in the coefficients: >plot (ginf$coef[,2], ylab="change in expend" and the point is identified and repeated for the other predictors >plot (ginf$coef[,5], ylab="change in salary" how do I determine the value in [], for each of the...

I am trying to do a CI plot as illustrated in book but I get an error..using the values from a previous thread question str(predict(g,g1, se=TRUE)) List of 4 $ fit : Named num 28.1 ..- attr(*, "names")= chr "1" $ se.fit : num 4.97 $ df : int 42 $...

I am trying to predict the amount that a male with average status, income and verbal score would spend along with an appropriate 95% CI. I used my linear model with all my variables and sex is coded as male=0 and female=1 in data set. I think I did something wrong because I get all 47...

I am working on a problem and need to determine the stat. significant variables and provide an interpretation on sex? I got the following results from my lm: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 22.55565 17.19680 1.312 0.1968 sex...

Which results will I use to determine the difference in males and females when all other predictors are held constant. I have 4 predictors (status, income, verbal score and spending). I did a lm as: fit<-lm(spending ~ sex, data=spending) and produce a result Coefficients...