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    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; Ch15.2
    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

    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
    constants
    contingency table
    convenience sampling
    CRD
    contingency table; 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; Ch08.6; Ch09.1
    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()
    dropdown menus
    Dunnett’s test

    — E —
    eBook
    effect size; Ch08.5
    empirical rule
    epidemiology; Ch02.3
    epiR
    epiR_descriptive
    error sums of squares
    error variance
    Estimate
    estimation
    Euler’s number
    Eugenics
    Event; Ch07.1
    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; 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; 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
    mad()
    Mann-Whitney test
    magnitude, order of
    margin of error
    Markup
    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; Ch17.1; Ch18; 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
    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; Ch08.3
    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

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