Index Mike’s Biostatistics Book

Index to key terms in this eBook; 519 terms, 598 links (last updated 7 December 2023)

R commands used in this book available at List of R commands

Click on term to take you to chapter and section where the word is first presented; subsequent references are noted by chapter (e.g., Ch08.3 refers to Chapter 8.3).

Note you may need to scroll down on the page to view the word: use browser Find (Ctrl+F or Cmd+F).

— A —
Absolute risk
Absolute risk reduction
accuracy; Ch08.3
Age-adjusted rates
Age-specific rates
Akaike Information Criterion (AIC)
allele frequency
alpha
alpha = 5%
alternative hypothesis; Ch08.2; Ch08.4; Ch17.0; Ch17.1
Among Groups Variation
analysis of means
analysis of variance (ANOVA)
ANOVA; Ch12.2
ANOVA table; Ch17.1
Anolis lizard; Ch08.5
antilog
anova()
ANOVA on ranks
Anscombe’s quartet
aov()
ARR
assumptions of linear regression
assumption, independence
assumption, linear
assumptions parametric tests
ave()

— B —
base R
Bayes conditional probability
Bayes factor; Ch08.2
Bayesian; Ch08.2
Bayesian Information Criterion (BIC)
Bessel’s correction
best fit line
bias
binary outcome variables
binomial
Bioconductor
Bioinformatics 
Biostatistics
Bonferroni correction
bootstrapping
box plot

— C —
c(); Ch03.2; Ch03.4; Ch12.2
cause and effect; Ch17.0
causation
cause and effect; Ch17.1
census; Ch03.3
Central Limit Theorem
central tendency
Chance
Chebyshev’s inequality
Chi-square
chisq.text()
coefficient of determination
Coefficient of variation
coefficients
cholera
Chromebook
citation bias
classical frequentist
Classification; Ch07.1
CoCalc
code snippets
coefficient of determination
combine, c()
cloud computing
Colaboratory
collinearity
command-line interface
compiled language
completely randomized experimental design
confidence interval regression line
confounding variable
conda
conditional probability
confidence interval
confounding variable
confidence interval for a sample mean
confusion matrix
constants
contingency table; Ch07.1
convenience sampling
CRD
contingency table; Ch05.1; Ch09.0
Cook’s distance
correlation; Ch17.0; Ch17.1
covariate
covary
CRAN mirror
cranlogs
critical value
critical value; Ch08.2; Ch08.5; Ch08.6

— D —
data ; Ch03.3
data()
data analysis
data cleaning
data exploration
data.frame(); Ch08.5; Ch12.2
data mining
data processing; Ch02.2; Ch03.1
data sets
Data set CO2 Mauna Loa
data set diabetic
Data set GaltonFamilies
data set pipette
data set Rhinella marina body mass
Data scientist
data transformation
data types; Ch03.4
datum
deciles; Ch08.6
degrees of freedom; Ch08.1; Ch08.2; Ch08.3; degrees of freedom
degrees of freedom, one-way ANOVA
degrees of freedom one sample t
dependent
dependent variable
Dependent variables; Ch17.0
descriptive epidemiology
descriptive statistics ; Ch03.0
deviate
Diagnosis
diagnostic test
discrete
distribution free tests
Dotplot()
dplyr()
drop down menus
Dunnett’s test

— E —
eBook
effect size; Ch08.5
empirical rule
epidemiology; Ch02.3
epiR
epiR_descriptive
error matrix
error sums of squares
error variance
Estimate
estimation
Euler’s number
Eugenics
Event; Ch02.3
evidence
exp()
expected values
experimental units
experiment wise error rate
extrapolate

— F —
F statistic
factor levels
factors
Fagan nomogram
family-wise error rate
Fisherian approach
frequentist; Ch08.1
Frequentists’ approach
full model
FUN

— G —
gecko; Ch15.2
general linear model; Ch17.1; Ch18.2; Ch18.1
geomean() MS Excel
geometric mean
geosd()
goodness of fit
gof
Goodness of fit (GOF); Ch09.1
Google Sheets
grand mean
Greek letters
GUI

— H —
haphazard sampling
Hardy-Weinberg
harmean() MS Excel
harmonic mean
Hazard
head()
health disparities
help()
HistData package
histogram; Ch03.3
Holm method
Holm-Bonferroni method
homework

— I —
id number
Incidence; Ch07.2
Incidence rate
independent
independent variable; Ch12.2; Ch17.0
index variable
individual-wise comparison
inference, statistical; Ch02.2; Ch07.0; Ch08.4
inferential statistics
interpreted language
Install R Commander
interquartile range
interpolate
interval; Ch03.4
Interquartile range
IQR
IR

— J —
justify alpha

— K —
Kruskal-Wallis test.
kruskal.test()

— L —
LaTeX; Ch01.1
Law of Large Numbers
learning curve, statistics software
left-skewed
levels
leverage
LibreOffice Calc
Likelihood; Ch08.1; Ch08.2
likelihood function
likelihood ratio test
likelihood value
Likert scale
linear models
linear regression; Ch17.1
lm()
logarithm, base 2
logarithm, base 10
logarithm, natural
log-transform
log()
logistic functions
logistic regression
lower limit

— M —
machine learning; Ch05.1; Ch07.1; Ch091
mad()
Mann-Whitney test
magnitude, order of
margin of error
Markup
Mathews Correlation Coefficient (MCC)
max()
mean; Ch03.2
mean deviation
mean(); Ch03.2
mean, population
mean, sample
Mean square error
Mean squares
Mean squares among groups
measured; Ch03.4
means, other kinds
measurement units
measurement variable
measures of dispersion
median
median ranks
Mendelian genetics
Microsoft Excel
MDI (Multiple Document Interface)
Mike’s Workbook for Biostatistics
mode
model
model estimates
modeest package
Monte Carlo methods
multicollinearity; Ch17.0; Ch17.1; Ch18.1
multiple comparison problem
multiple comparisons
multiple linear regression
multiplicity problem
multivariate statistics

— N —
names()
namespaces
natural logarithm
netative log p-value (logP)
Negative predictive value
NHST; Ch15.1
NNT
nominal data type; Ch09.1
nonparametric tests
normal probability distribution
normal probability table
Normal Q-Q
normal distribution; Ch08.5; Ch08.6
normal table
normalize scores
NPV
Null hypothesis; Ch08.1; Ch08.2; Ch08.4; Ch08.5; Ch09.1; Ch15.1; Ch15.2
Null Hypothesis Significance Testing; Ch08.3
Number needed to treat

— O —
observations 
Occam’s razor
ODBC
Odds
Odds ratio
one sample t-test
one sample tests
one-tailed test
One way ANOVA
operators
order of magnitude
ordinal data type
Ordinary Least Squares (OLS)
outliers
ordinal outcome variables
outcome variable
Output window

— P —
P-value; Ch08.1; Ch08.2; Ch08.4; Ch09.1; Ch12.2
p-value threshold
p-value, exact
pairwise comparisons; Ch12.2
pandoc
parameter; Ch03.4; Ch08.3; Ch08.6
Parameters, estimating
parametric statistics
parametric test; Ch15.0
partial regression slopes
pch
pchisq()
Per capita rate
percentiles
permutation test
Person-time
phi (φ) coefficient
plot.ly
plot()
plugin 
pnorm()
point characters
point
population
population descriptive statistics
population mean
population standard deviation
Population variance
Positive predictive value
post-hoc tests
Posttest probability
power of the test
pnormGC()
posterior probability
PPV
precision; Ch08.3
Pretest probability
Prevalence
Prevalence rate
prevalence, 95% CI of
prior probability
Probability
probability distribution; Ch08.3
probability value
Prognosis
pseudoreplication
psych package

— Q —
qchisq()
qt(); Ch08.6
qualitative data types
quantile()
quantiles
quantiles, t
quantitative data types
quartiles; Ch08.6

— R —
R; Ch02.2
R Commander; Ch01.1; Ch02.2
R²
R-squared
random error
random normal distribution
random sampling
random variables
range
range()
rank()
ratio
ratio scale data type
raw data
Rcmdr
Rcmdr, Improve experience
Rcmdr: Wilcoxon test
RcmdrMisc
R history
R Markdown
R prompt; Ch02.2
R statistical language
R tutorials
Random
random sampling
regions of the curve
Relative risk
Relative risk reduction
require()
Resampling
residuals
residuals vs. fitted
residuals vs. leverage
residuals vs. predicted
response variable; Ch12.2
right-skewed
risk analysis; Ch07.1
risk difference
robust estimator
Roman letters
round()
RRR
Rstudio

— S —
sample descriptive statistics
sample frequency distribution
sample mean; Ch08.5
sample standard deviation; Ch03.3
sample statistic
sample variables
Sample variance
samples
sampling, convenience
sampling distribution
sampling error
sampling, haphazard
sampling, random
saturated model
scale-location
scan()
script file
Script window; Ch02.2
sd()
SEM; Ch08.5
SDI (Single Document Interface)
Signif[icance] codes
significant figures
Single Factor ANOVA
slope
Snow, John
stack()
stacked worksheet
standard deviation
standard deviation, sample; Ch03.3
standard deviation, population
standard error of the estimate
standard deviation of the geometric mean
Standard error of the mean
standard error of the sample mean
standardize
standard normal probability table
Statistic
Statistical bias ; Ch03.4
statistical inference; Ch08.4
Statistical power
statistical reasoning
statistical thinking
statistical power of the test
statistical significance
statistical significance level
Statistics 
structural collinearity
Student’s t-test
sum of squares
summary()
summary statistics
sums of squares
survival analysis
systematic error

— T —
t distribution; Ch08.6
t quantiles
table()
tablet
T-test
t.test()
tapply()
tails of the distribution
test statistic; Ch08.1; Ch08.2; Ch09.1; Ch12.2
Therapy
tigerstats
tolerance
total variability
trimmed mean; Ch03.3
true value
truncated mean
Tukey’s
Tukey’s range test
two sample tests
two sample Wilcoxon test
two-tailed test; Ch08.1; Ch08.2; Ch08.6; Ch15.2
Type I error rate; Type I error ; Ch12.2
Type I error rate; Ch15.0; Ch15.1
Type II error; Ch08.1

— U —
unbiased estimator; Ch03.4
uncode
unstacked worksheet
unstandardize
upper limit

— V —
variability
variables; Ch08.4
variance, population
variance, sample
VIF

— W —
Weighted arithmetic mean
Welch F-test
Wilcoxon rank sum test
Wilcoxon test statistic (W)
Wilcoxon test, two sample
with()
winsorized mean; Ch03.3
Winsorized variance
Within Group Variation
Workbook for Biostatistics, Mike’s
working directory

— X-Y-Z —
xkcd comic; Ch07.0
XQuartz
Y-intercept; Ch18.1
Z-score ; Ch08.5