Hi everyone! This will be my first post of my blog! I am excited to add to this as I make progress in this class. 

Comments

Post a Comment

Popular posts from this blog

Module #7 Assignment

Image
 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...
Read more

Final Project

Image
Final Project The aim of this project is to experience data analysis where you will use the statistical methods  taught in this course to come up with some objective findings. You will collect the data from  your own choice of data repositories and conduct inferential analysis as discussed in the class. The Final Project will be graded by the following criteria: Appropriateness, thoroughness, and accuracy of analysis 60%. Effectiveness of communication 40%. Includes writing, organization, professionalism &  style.
Read more

Module #11 Assignment

Image
 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...
Read more