In charlotte-ngs/asmss2022: Applied Statistical Methods in Animal Science - Spring Semester 2022

knitr::opts_chunk$set(echo = TRUE)

Problem 1: Regression Analysis

The following dataset on body weight and on futher observations on a number of animals is given.

s_ex01_p01_path <- "https://charlotte-ngs.github.io/asmss2022/data/asm_bw_mult_reg.csv"
tbl_ex01_p01_data <- readr::read_csv(file = s_ex01_p01_path)
knitr::kable(tbl_ex01_p01_data,
             booktabs = TRUE,
             longtable = TRUE,
             escape = FALSE)

The data can be read from r s_ex01_p01_path. The additional columns contain data on body condition score (BCS) and height (HEI).

Tasks

• Build a regression model of body weight on the other observations using the dataset given above.
• Set up the matrix $\mathbf{X}$ and the vectors $\mathbf{y}$, $\mathbf{b}$ and $\mathbf{e}$.
• Compute estimate for the regression coefficients in the model defined above.

Your Solution

• Start by building the model which includes the decision of what type of components go into the vector $\mathbf{b}$. Then write down the model in matrix-vector notation.
• Determine all the components of $\mathbf{X}$, $\mathbf{y}$, $\mathbf{b}$ and $\mathbf{e}$.
• Compute the solution for $\hat{\mathbf{b}}

Problem 2

Use the same dataset as in Problem 1 and verify your results using the function lm() in R.

Your Solution

• Read the data into a dataframe
• Use the lm() function which takes a formula to define the model and a dataframe
• Use the function summary() on the result of lm() to show the results

cat('\n---\n\n _Latest Changes: ', format(Sys.time(), '%Y-%m-%d %H:%M:%S'), ' (', Sys.info()['user'], ')_\n', sep = '')

charlotte-ngs/asmss2022 documentation built on June 7, 2022, 1:33 p.m.