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. 

 Module #9 Assignment  1. Your data.frame is > assignment_data <- data.frame( Country = c("France","Spain","Germany","Spain","Germany", "France","Spain","France","Germany","France"), age = c(44,27,30,38,40,35,52,48,45,37), salary = c(6000,5000,7000,4000,8000, 5500 ,   4500 ,   6000 ,   7500 ,   5000 ), Purchased=c("No","Yes","No","No","Yes", "Yes","No","Yes","No","Yes")) Generate simple table in R that consists of four columns: Country, age, salary and purchased.

Module #8 Assignment Your assignment: A researcher is interested in the effects of drug against stress reaction. She gives a reaction time test to three different groups of subjects: one group that is under a great deal of stress, one group under a moderate amount of stress, and a third group that is under almost no stress. The subjects of the study were instructed to take the drug test during their next stress episode and to report their stress on a scale of 1 to 10 (10 being most pain). High Stress Moderate Stress Low Stress 10 8 4 9 10 6 8 6 6 9 7 4 10 8 2 8 8 2 Report on drug and stress level by using R. Provide a full summary report on the result of ANOVA testing and what does it mean. More specifically, report  using the following R functions:  Df, Sum, Sq Mean, Sq, F value, Pr(>F)   

 Module #7 Assignment 1. In this assignment's segment, we will use the following regression equation    Y = a + bX +e Where: Y  is the value of the  Dependent variable (Y) , what is being predicted or explained a  or Alpha, a constant; equals the value of Y when the value of X=0 b  or Beta, the coefficient of X; the slope of the regression line; how much Y changes for each one-unit change in X. X  is the value of the Independent variable (X), what is predicting or explaining the value of Y e  is the error term; the error in predicting the value of Y, given the value of X  (it is not displayed in most regression equations). 1.1  The data in this assignment: x <- c ( 16 , 17 , 13 , 18 , 12 , 14 , 19 , 11 , 11 , 10 ) y <- c ( 63 , 81 , 56 , 91 , 47 , 57 , 76 , 72 , 62 , 48 ) 1.1 Define the relationship model between the  predictor  (x) and the  response  (Y) variable: The relationship mode...