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14 May 2024: Download the data from the course webpage:. > path <- "http://www.statslab.cam.ac.uk/rds37/teaching/statistical_modelling/". > ... 2. Binomial responses with multiple trials. Download the Smoking data from the course webpage with. >

14 May 2024: S. A. Bacalladoand Dr. R. Shah. Smoking example continued. Re-download the Smoking data from the course webpage and fit a logistic regression model with covari-ates, age, age squared and ... Poisson regression. Download the English Premiership data from

14 May 2024: The original data areavailable at http://andrewgelman.com/2008/01/21/the_speeddating_1/. Download an editedversion of the data from the course webpage with. > ... Download the data from the course webpage with. > Drinks <- read.table(paste(file_path,

14 May 2024: How was itcarried out? 2. Download the patient-level data, then run the following code in R (you may need to install the readxlpackage first by install.packages("readxl")). ... You may find the following R code useful. table(data[, c("index_ward",

14 May 2024: 2. Download the patient-level data, then run the following code in R (you may need to install the readxlpackage first by install.packages("readxl")). ... You may find the following R code useful. table(data[, c("index_ward", "sw_step", "no_we_exposure")])

14 May 2024: 2. Download the Cambridge Colleges data with. > path <- "http://www.statslab.cam.ac.uk/rds37/teaching/statistical_modelling/". > ... Lookingat a plot of the data, what appears to be the most outlying college?

14 May 2024: S. A. Bacallado and Dr. R. Shah. ANOVA. Download the EssayMarks dataset from my webpage:. > ... How about EssayMarksLM2 andEssayMarksLM4? ANCOVA. Download the Cycling data from the course webpage. >

14 May 2024: Theread.csv function creates data frames from comma-separated value files. Download the students’ brainsize data from my website and store it in the data frame BrainSize. > ... to produce helpful summaries.Try applying it to the the data frame BrainSize

14 May 2024: 2. Download the data for film earnings in 2010. > Movies2010 <- read.csv(paste(file_path, "Movies2010.csv", sep ="")). Compute 95% prediction intervals (see? predict.lm) for each of the earnings of ... Re-member that you will need to transform prediction