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Chapter 11 covers inferences involving the mean when σ is not known, one- and two-sample designs, and includes the following specific topics, among others: t-distribution, degrees of freedom, t-test assumptions, one-sample t-test, two-sample t-test for independent groups, two-sample t-test for related groups, paired sample t-tests, effect size, the bootstrap, and power analysis.
Chapter 8 covers theoretical probability models and includes the following specific topics, among others: he binomial probability distribution and the normal probability distribution.
Chapter 20 covers accessing data from public-use sources and includes the following specific topics, among others: good research questions, desirable features of public-use data, and accessing publicly available datasets.
Chapter 16 covers an introduction to multiple regression and includes the following specific topics, among others: confidence intervals, statistical significance of the b weight, fit of the overall regression Eeuation, R and R-squared, adjusted R-squared, semipartial correlation, partial slope, confounding, and statistical control.
Chapter 17 covers two-way interactions in multiple regression and includes the following specific topics, among others: two-way interaction, first-order effects, main effects, interaction effects, model selection, AIC, BIC, and probing interactions.
Chapter 7 covers probability fundamentals and includes the following specific topics, among others: the discrete case, additive rules of probability, complement rule of probability, multiplicative rule of probability, conditional probability, Bayes’ theorem, and the law of large numbers.
We study the local convergence of critical Galton–Watson trees under various conditionings. We give a sufficient condition, which serves to cover all previous known results, for the convergence in distribution of a conditioned Galton–Watson tree to Kesten’s tree. We also propose a new proof to give the limit in distribution of a critical Galton–Watson tree, with finite support, conditioned on having a large width.
Chapter 9 covers the role of sampling in inferential statistics and includes the following specific topics, among others: samples and populations, random samples, simple random sampling, sampling with and without replacement, sampling distributions, the sampling distribution of means, The central limit theorem, estimators and bias.
Chapter 12 covers an introduction to research design and includes the following specific topics, among others: descriptive, relational, and causal research studies, blocking, quasi-experimental designs, threats to internal validity, and threats to external validity.
Current evidence suggests that recent acute respiratory infections and seasonal influenza may precipitate acute myocardial infarction (AMI). This study examined the potential link between recent clinical respiratory illness (CRI) and influenza, and AMI in Bangladesh. Conducted during the 2018 influenza season at a Dhaka tertiary-level cardiovascular (CV) hospital, it included 150 AMI cases and two control groups: 44 hospitalized cardiac patients without AMI and 90 healthy individuals. Participants were matched by gender and age groups. The study focused on self-reported CRI and laboratory-confirmed influenza ascertained via quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) within the preceding week, analyzed using multivariable logistic regression. Results showed that cases reported CRI, significantly more frequently than healthy controls (27.3% vs. 13.3%, adjusted odds ratio (aOR): 2.21; 95% confidence interval (CI): 1.05–4.06), although this was not significantly different from all controls (27.3% vs. 22.4%; aOR: 1.19; 95% CI: 0.65–2.18). Influenza rates were insignificantly higher among cases than controls. The study suggests that recent respiratory illnesses may precede AMI onset among Bangladeshi patients. Infection prevention and control practices, as well as the uptake of the influenza vaccine, may be advocated for patients at high risk of acute CV events.
Chapter 5 covers the relationship between two variables and includes the following specific topics, among others: scatterplots, Pearson product moment correlation coefficient, the Spearman rank correlation coefficient, the point biserial correlation coefficient, the phi coefficient, and visual displays of bivariate relationships.