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This work aims to study the properties of active components of toothpaste. The hypothesis is that the toothpaste should have a pronounced cleansing, anti-inflammatory, and hemostatic effect on the mouths hard and soft tissues, but significant changes. The color of the tooth enamel will not be revealed after one week. The dependent variable is the enamels color after one week from the start of the experiment regarding the group of participants. The independent variable is the use of particular toothpaste. To study the ability of toothpaste to white teeth within one week, people aged 15 to 60 years take part. They are divided into two groups: the first group consists of persons who used the Toothpaste W; the second one people who used another unknown toothpaste. All groups should be formed from a group of people with identical hygienic and dental statuses. The participants have no medical contraindications and pledged to use only the toothpaste they received and brush their teeth twice a day. The probands did not participate in any other experiments besides this study.
Correlation analysis is a statistical method of studying the relationship between two or more random variables. Its purpose is to calculate the correlation coefficients, taking positive and negative values. Correlation coefficients are relatively easy to calculate; their application does not require specialized mathematical training. The ratios simplicity has led to its widespread use in statistical data analysis. It is imperative to use this method only if the prerequisites for calculating one or another correlation coefficient are met (Pereira et al., 2018). The technique presupposes not only the calculation of the correlation coefficients but also the mandatory verification of their significance, based on the principle of statistical testing hypotheses, the construction of interval estimates of the correlation coefficients.
Reference
Pereira, R. B., Plastino, A., Zadrozny, B., & Merschmann, L. H. C. (2018). Correlation analysis of performance measures for multi-label classification. Information Processing & Management, 54(3), 359-369.
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