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
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...
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
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
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:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.55565 17.19680 1.312 0.1968
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