2022_23
Educational guide 
Faculty of Chemistry
A A A 
english 
Nutrition and Metabolism (2012)
 Subjects
  STATISTICS APPLIED TO NUTRITION
   Contents
Topic Sub-topic
1. Introduction to data analysis. 1.1. Concept of statistics. Content of Statistics. 1.2. Concept of population, sample, individual and statistical variable. 1.3. Classification of statistical variables. 1.4. Frequency distribution. Graphic representations. 1.5. Grouping data in intervals. 1.6. Position parameters. 1.7. Scattering parameters.
2. Probability distribution models. 2.1. Discrete distributions: Bernoulli, binomial, Poisson, uniform. 2.2. Continuous distributions: uniform, exponential, normal. 2.3. General normal law. Reduced normal law: N(0,1). 2.4. Distributions deducted from normal: chi-squared, Snedecors’ F and T-distribution. 2.5. Convergence to normal law: central limit theorem. 2.6. Examples of some distributions to the normal distribution. 2.7. Use of statistical tables.
3. Confidence intervals. 3.1. Concept of parameter and estimator. Point and interval estimation. 3.2. Properties of estimators: bias, efficiency and consistency. 3.3. Some estimation methods: method of moments and method of maximum likelihood estimation. 3.4. Notion of confidence interval. Confidence coefficient. 3.5. Determination of some confidence intervals for: mean, mean difference, variance, coefficient of variance, proportion and proportion difference.
4. Statistical hypothesis. 4.1. Statistical hypothesis. Types of hypothesis. 4.2. Concept of critical region and region of acceptance. 4.3. Type of errors. Power of a contrast. Level of significance. 4.4. Application of statistical hypothesis for: mean, mean difference, variance, coefficient of variance, proportion and proportion difference.
5. Analysis of variance (ANOVA). 5.1. Generalities on the analysis of variance. 5.2. Design One way ANOVA. 5.3. Design Two way ANOVA without interaction. Randomizing blocks. 5.4. Design Two way ANOVA with interaction. 5.5. SPSS: design ANOVA with two or more factors. 5.6. Analysis of covariance (ANCOVA). 5.7. Repeated measures.
6. Linear regression. 6.1. Relationship between variables. 6.2. Simple regression model simple. 6.3 Simple linear regression parameter estimation by the method of least squares. 6.4. Simple linear regression: measures of goodness of fit. 6.5. Simple linear regression prediction intervals construction. 6.6. Nonlinear regression. 6.7. Multiple linear regressions. 6.8. Contrasts of significance. 6.9. Results with the Excel program. 6.10. Results using SPSS.
7. Multivariate analysis. 7.1. Classification of multivariate analysis methods. 7.2. Factorial analysis. Principal components analysis. 7.3. Non-hierarchical cluster analysis. 7.4. Hierarchical cluster analysis. 7.5. Discriminant analysis.