Category: Environnement et ressources naturelles

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The E-mail Address es you entered is are not in a valid format. Please re-enter recipient e-mail address es. You may send this item to up to five recipients.Le mot provient du verbe environnerqui signifie action d'entourer. Ayant le sens de base de ce qui entoureil peut prendre le sens de cadre de viede voisinaged' ambiance, ou encore de contexte en linguistique [ 1 ].

Le parc national de Yellowstone deviendra en le premier parc national [ 17 ]. Dans une approche quantitative, on parle de capital naturel. Et ce chiffre est en constante augmentation [ ]. La gestion des ressources naturelles est un enjeu environnemental de premier plan [ ]. Pour de nombreuses autres ressources, des labels existent, certifiant de techniques de gestion durables.

Il existe de nombreuses associations et organisations non gouvernementales actives sur les questions d'environnement. Dans l' hindouismel'environnement a une grande importance. Cet article traite de l'environnement biologique. Pour les autres sens du mot Environnement, voir l'article Environnement homonymie.

Environnement et ressources naturelles

Cambridge: Cambridge University Press. Pachauri et A. Reisinger dir. MalcolmCanran LiuRonald P. Michael Hogan. Meadows, Dennis L. Meadows, Jorgen Randers, and William W. Behrens III. The Limits to Growth. Espaces de noms Article Discussion.Numerous and frequently-updated resource results are available from this WorldCat. Please choose whether or not you want other users to be able to see on your profile that this library is a favorite of yours.

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environnement et ressources naturelles

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Introduction au chapitre 9 - L’exploitation des ressources naturelles mondiales

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Gestion des ressources naturelles et de l’environnement

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And blow-drying her hair at the same time. Her husband had told her it was fine. I told her he was trying to get rid of her.If you are interested in variation differences, then you should test for homogeneity of variances.

Other Related Data Analysis Techniques.

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Although for exploratory data analysis, breakdowns can use more than one independent variable, the statistical procedures in breakdowns assume the existence of a single grouping factor (even if, in fact, the breakdown results from a combination of a number of grouping variables). Thus, those statistics do not reveal or even take into account any possible interactions between grouping variables in the design.

For example, there could be differences between the influence of one independent variable on the dependent variable at different levels of another independent variable (e. You can explore such effects by examining breakdowns "visually," using different orders of independent variables, but the magnitude or significance of such effects cannot be estimated by the breakdown statistics. Post-Hoc Comparisons of Means. Usually, after obtaining a statistically significant F test from the ANOVA, one wants to know which of the means contributed to the effect (i.

One could of course perform a series of simple t-tests to compare all possible pairs of means. However, such a procedure would capitalize on chance. This means that the reported probability levels would actually overestimate the statistical significance of mean differences. Without going into too much detail, suppose you took 20 samples of 10 random numbers each, and computed 20 means.

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Then, take the group (sample) with the highest mean and compare it with that of the lowest mean. The t-test for independent samples will test whether or not those two means are significantly different from each other, provided they were the only two samples taken.

Post-hoc comparison techniques on the other hand specifically take into account the fact that more than two samples were taken. Technically, it determines a linear combination of the dependent variables that best predicts the group membership. Another related type of analysis that cannot be directly performed with breakdowns is comparisons of frequencies of cases (n's) between groups. Specifically, often the n's in individual cells are not equal because the assignment of subjects to those groups typically results not from an experimenter's manipulation, but from subjects' pre-existing dispositions.

If, in spite of the random selection of the entire sample, the n's are unequal, then it may suggest that the independent variables are related. For example, crosstabulating levels of independent variables Age and Education most likely would not create groups of equal n, because education is distributed differently in different age groups.

If you are interested in such comparisons, you can explore specific frequencies in the breakdowns tables, trying different orders of independent variables. However, in order to subject such differences to statistical tests, you should use crosstabulations and frequency tables, Log-Linear Analysis, or Correspondence Analysis (for more advanced analyses on multi-way frequency tables).

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Graphs can often identify effects (both expected and unexpected) in the data more quickly and sometimes "better" than any other data analysis method. Categorized graphs allow you to plot the means, distributions, correlations, etc. The graph below shows a categorized histogram which enables you to quickly evaluate and visualize the shape of the data for each group (group1-female, group2-female, etc.

environnement et ressources naturelles

The categorized scatterplot (in the graph below) shows the differences between patterns of correlations between dependent variables across the groups.

Additionally, if the software has a brushing facility which supports animated brushing, you can select (i. Frequency or one-way tables represent the simplest method for analyzing categorical (nominal) data (refer to Elementary Concepts). They are often used as one of the exploratory procedures to review how different categories of values are distributed in the sample. In practically every research project, a first "look" at the data usually includes frequency tables.

For example, in survey research, frequency tables can show the number of males and females who participated in the survey, the number of respondents from particular ethnic and racial backgrounds, and so on.

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Responses on some labeled attitude measurement scales (e. Customarily, if a data set includes any categorical data, then one of the first steps in the data analysis is to compute a frequency table for those categorical variables.

Purpose and Arrangement of Table.

environnement et ressources naturelles

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