# F-statistic: 4.741 on 3 and 34 DF, p-value: 0.007215Ĭonsider the research question, do nestling bank swallows, which live in underground burrows, also alter how they breathe? Data set babybirds contains volume of air breathed per minute for each of 6 nestling bank swallows Vent, percentage of oxygen in the air O2, and percentage of carbon dioxide in the air CO2 for n=120 nestling bank swallows. # Residual standard error: 19.79 on 34 degrees of freedom The ANOVA F-test (P=0.0072) suggests that the model containing Brain, Height and Weight is more useful in predicting PIQ than not taking into account the three predictors. This will come in handy for model selection. R2=.2949, so 29.49% of the variation in intelligence is reduced by taking into account brain size, height and weight. Run a regression PIQ ~ Brain + Height + Weight. PIQ appears to be correlated with Brain, but not so much with Height or Weight. # Body fat, triceps thickness, thigh circumference, and midarm circumference for sample of n=20 individuals.īodyfat <- read.table(url, sep = ' ', header = TRUE, fileEncoding = 'UTF-16LE')ĥ.1 Example on IQ and Physical CharacteristicsĬonsider the research question, is brain size and body size predictive of intelligence? Data set iqsize contains performance IQ scores PIQ, brain size from MRI scans Size (count/10,000), height Height (inches), and weight weight (pounds) for n=38 college students.Ī common way of investigating the relationships among all of the variables is by way of a “scatter plot matrix.” Height and Weight appear to be correlated, and so does Brain with Height and with Weight. #summarise(hospital_infct, n=n()) # bodyfat is a tab-delimited data file. Hospital_infct <- read.table(url, sep = ' ', header = TRUE, fileEncoding = 'UTF-16LE')

# Infection risk, lenghth of hospital stay, average patient age, and number of x-rays at n=113 hospitals. #summarise(stat_females, n=n()) # hospital_infct is a tab-delimited data file. Stat_females <- read.table(url, sep = ' ', header = TRUE) # Height (inches), mother's height, and fathers height for n=214 female college statistics students. #summarise(pastry, n=n()) # stat_females is a tab-delimited data file. Pastry <- read.table(url, sep = ' ', header = TRUE, fileEncoding = 'UTF-16LE') # Taster rating, moisture level, and sweetness level for sample of n=16 pasteries. #summarise(babybirds, n=n()) # pastry is a tab-delimited data file.

# Venillation, O2, and CO2 for sample of n=120 nestling bank swallows.īabybirds <- read.table(url, sep = ' ', header = TRUE) #summarise(iqsize, n=n()) # babybirds is a tab-delimited data file. Iqsize <- read.table(url, sep = ' ', header = TRUE) # Performance IQ scores, brain size, height, and weight of n=38 college students. # combine # iqsize is a tab-delimited data file.

# Attaching package: 'gridExtra' # The following object is masked from 'package:dplyr': # Attaching package: 'car' # The following object is masked from 'package:dplyr':

# intersect, setdiff, setequal, union library(alr3) # Warning: package 'alr3' was built under R version 3.4.3 # Loading required package: car # Warning: package 'car' was built under R version 3.4.3 # # filter, lag # The following objects are masked from 'package:base': # Attaching package: 'dplyr' # The following objects are masked from 'package:stats': Data Management for this Module library(readr)

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