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Introduction to statistics

for food scientists.

Main goal

  • To understand and know how to apply statistical methods in food science.

Specific objectives

  • Hands-on data organization and effective summary.

  • Understand how to apply basic concepts like distribution, population, deviation, variance, and others. 

  • Analysis of Hypothesis test for treatments.

  • Know how to design and experiment. 

  • Familiarization with R software for statistical analysis.

Learning method

  • +12 H of online lessons.

  • Examples focused in food science field.

  • +12 H workshop of data analysis with evaluations.

  • Continuous mentoring and follow-up of individual questions. 

Syllabus

  1. Introduction to basic concepts: data, variance, application of statistical analysis, hypothesis test, variables, scales of measurements, population and microbial sample. 

  2. Introduction to R: start a project, data upload, use of basic statistic functions, export results to computer.

  3. Descriptive statistics: organization of data, frequency of observations, measures of central tendency, measures of dispersion, histograms and box-plots.

  4. Theoretical Distributions: concept of probability, binomial distribution and normal distribution.

  5. Samplig distribution: the perfect experiment, sampling considerations to avoid bias, sampling distribution, point estimation, interval estimation, T distribution vs Normal distribution, confidence intervals calculation and interpretation.  

  6. Hypothesis test: statistical test, "p-value", T-test for comparison of means, confidence interval for treatment effectiveness, assumptions for inference.

  7. Analysis of variance (ANOVA): Type-I error rate in multiple comparisons, hypothesis in ANOVA, the sum of squared errors table, inference and interpretation of results. 

  8. The factorial experiment: Two-way ANOVA, p-value adjustment methods, main effects, interaction between treatments, multi-variable ANOVA.  

  9. Correlation and Linear Regression: creation of mathematical models, parameters estimation, mathematical and biological interpretation of results, usefulness of model for prediction.  

  10. Inference for regression: conditions for statistical inference, standard error of model, hypothesis test for parameters, confidence intervals, prediction of mean values and prediction of observed values, verification of statistical assumptions.  

  11. Analysis of frequency: contingency tables, changes in prevalence of control vs treatment groups with Chi-squared test, independence test in multi-treatment tables RxC. 

  12. Introduction to Non-Parametric Statistics: mathematical principles, application to microbiology, Mann-Whitney test for two treatment groups, Kruskal-Wallis test for multiple treatments, interpretation of results and scope of inference. 

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