LIS4273 Advanced Stats

Module #12 Assignment  The table below represents charges for a student credit card. Month 2012 2013 Jan 31.9 39.4 Feb 27 36.2 March 31.3 40.5 Apr 31 44.6 May 39.4 46.8 Jun 40.7 44.7 Jul 42.3 52.2 Aug 49.5 54 Sep 45 48.8 Oct 50 55.8 Nov 50.9 58.7 Dec 58.5 63.4 a. Construct a time series plot using R. b. Employ E xponential Smoothing Model  as outlined in  Avril Voghlan 's  notes  and report the statistical outcome c.  Provide a discussion on time series and Exponential Smoothing Model results that you obtained .   Post the result on your blog posting and provide details explanation to your findings.

 Module #11 Assignment From our textbook,  Introductory Statistics with R" : pp. 224 Question 12.1 and 12.3 12.1: Set up an additive model for the ashina data, as part of ISwR package. This data contains additive effects on subjects, period and treatment. Compare the results with those with those obtained from t tests.  Hint ashina$subject <- factor(1:16) attach(ashina) act <- data.frame(vas=vas.active, subject, treat=1, period=grp) plac <-data.frame(vas=vas.plac, subject, treat=0, 12.3. Consider the following definitions a <- g1(2, 2, 8) b <- g1(2, 4, 8) x <-- 1:8 y <- c(1:4, 8:5) z <- rnorm (8) Note: The  rnorm()  is a built-in R function that generates a vector of normally distributed random numbers. The rnorm() method takes a sample size as input and generates that many random numbers. Your assignment Generate the model matrices for models z ~ a*b, z ~ a:b, etc. In your blog posting, discuss the implications. Carry out the m...

 Module #10 Assignment  This is from the Multiple Linear Regression chapter 11 of "Introductory Statistics with R", pg. 185-194  I revised this question, so please follow my description only. Conduct ANOVA (analysis of variance) and Regression coefficients to the data from cystfibr : data (" cystfibr ") database. Note that the dataset is part of the ISwR package in R.  You can choose any variable you like. in your report, you need to state the result of Coefficients (intercept) to any variables you like both under ANOVA and multivariate analysis. I am specifically looking at your interpretation of R results.