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Introduction to statistics
for food scientists.
Main goal
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To understand and know how to apply statistical methods in food science.
Specific objectives
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Hands-on data organization and effective summary.
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Understand how to apply basic concepts like distribution, population, deviation, variance, and others.
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Analysis of Hypothesis test for treatments.
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Know how to design and experiment.
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Familiarization with R software for statistical analysis.
Learning method
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+12 H of online lessons.
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Examples focused in food science field.
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+12 H workshop of data analysis with evaluations.
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Continuous mentoring and follow-up of individual questions.
Syllabus
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Introduction to basic concepts: data, variance, application of statistical analysis, hypothesis test, variables, scales of measurements, population and microbial sample.
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Introduction to R: start a project, data upload, use of basic statistic functions, export results to computer.
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Descriptive statistics: organization of data, frequency of observations, measures of central tendency, measures of dispersion, histograms and box-plots.
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Theoretical Distributions: concept of probability, binomial distribution and normal distribution.
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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.
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Hypothesis test: statistical test, "p-value", T-test for comparison of means, confidence interval for treatment effectiveness, assumptions for inference.
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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.
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The factorial experiment: Two-way ANOVA, p-value adjustment methods, main effects, interaction between treatments, multi-variable ANOVA.
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Correlation and Linear Regression: creation of mathematical models, parameters estimation, mathematical and biological interpretation of results, usefulness of model for prediction.
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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.
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Analysis of frequency: contingency tables, changes in prevalence of control vs treatment groups with Chi-squared test, independence test in multi-treatment tables RxC.
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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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