![](\"https:\/\/editura-unibuc.ro\/wp-content\/uploads\/2025\/04\/goo.png\")
Google Books<\/a><\/strong><\/p>\n
\n
The work is very extensive and will provide the reader with a solid foundation in data analysis, because, as the author says, “you can’t run a marathon if you don’t know how to walk.” The first part is theoretical, in which notions regarding indicators of central tendency (arithmetic mean, median and ranks, mode, amplitude mean, harmonic, geometric, weighted, quadratic mean and other measures of central tendency) and indicators of dispersion (amplitude, variation ratio, quartiles, variance, standard deviation) are treated, also addressing elements related to the standard error of the mean, degrees of freedom, coefficient of variation, use of the mean and standard deviation. It continues with notions about probabilities (permutations, combinations, arrangements, types of probabilities, mutually exclusive events, independent and dependent events), then the issue of samples is addressed, presenting some sampling principles and techniques. A large part of the theoretical notions is allocated to statistical probability distributions, including distributions for discrete variables (Bernoulli, binomial, geometric, negative binomial, Poisson, hypergeometric, uniformly discrete, multinomial and multivariate hypergeometric) and for continuous variables (uniformly continuous, normal, standardized normal, multivariate normal, Student’s t, Chi square and F) and treating, in detail, different procedures for testing the assumption of univariate normality. After a presentation of the role, place and applications of the standardized normal distribution, the author introduces data screening and purification, discussing the detection and treatment of extreme scores, the analysis of missing values \u200b\u200band imputation methods, and the theoretical part concludes with distribution normalization techniques. Regarding the application part, all theoretical notions are addressed in IBM SPSS Statistics, visually and through syntax, more precisely: calculating all indicators of central tendency and dispersion, creating different types of samples,illustration of statistical probability distributions, analysis of the assumption of univariate normality, calculation of z-scores and other standardized scores, as well as detection and treatment of extreme values, treatment of missing values, and normalization of distributions. All applications in IBM SPSS Statistics are repeated using the R language, the reader also benefits from an introduction to the R Markdown language and programming using R, an illustration of obtaining confidence intervals and bootstrapping, the use of “papaja” to automatically write APA 7-compliant articles, and the use of Zotero for automatic bibliography generation and integration with articles written in Markdown.<\/p>\n