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Information sur le cours
Rencontrez l'Ă©quipe enseignante -
Jeu de données du cours 1
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Jeu de données du cours 2
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MODULE A1: INTRODUCTION AUX STATISTIQUES AVEC R ET STATAA1.1 Qu'est-ce que les Statistiques?
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A1.2.1a Introduction Ă Stata
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A1.2.2b: Introduction Ă R
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A1.2.2c: Introduction to SPSS
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A1.3: Statistiques Descriptives
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A1.4: Estimations et Intervalles de Confiance
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A1.5: Tests d'Hypothèses
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A1.6: Transformation de Variables
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Fin du Module A11 Quiz
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MODULE A2: CALCULS DE PUISSANCE STATISTIQUE & DE TAILLE D’ÉCHANTILLONA2.1 Concepts Clés
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A2.2 Calculs de puissance pour une différence de moyennes
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A2.3 Calculs de puissance pour une différence de proportions
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A2.4 Calcul de taille d’échantillon pour les essais randomisés (RCTs)
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A2.5 Calculs de taille d’échantillon pour les études transversales (ou sondages)
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A2.6 Calcul de taille d'Ă©chantillon pour un devis cas-contrĂ´le
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Fin du Module A21 Quiz
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MODULE B1: RÉGRESSION LINÉAIREB1.1 Corrélation et Nuages de Points (scatterplots)
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B1.2 Différences Entre Moyennes (ANOVA à un facteur)
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B1.3 Régression Linéaire Univariée
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B1.4 Régression Linéaire Multivariée
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B1.5 Sélection de Modèles et Tests F
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B1.6 Diagnostics de RĂ©gression
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Fin du Module B11 Quiz
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MODULE B2: COMPARAISONS MULTIPLES & MESURES RÉPÉTÉESB2.1 ANOVA Approfondie— Tests Post-Hoc
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B2.2 Correction pour Comparaisons Multiples
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B2.3 ANOVA Ă deux facteurs (Two-way ANOVA)
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B2.4 Mesures Répétées et Test T Apparié
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B2.5 ANOVA pour Mesures Répétées
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Fin du Module B21 Quiz
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MODULE B3: MÉTHODES NON-PARAMETRICB3.1 Hypothèses des Tests Paramétriques
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B3.2 Test U de Mann-Whitney
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B3.3 Test de Kruskal-Wallis
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B3.4 Test des rangs signés de Wilcoxon
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B3.5 Test de Friedman
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B3.6 Corrélation des Rangs de Spearman
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Fin du Module B31 Quiz
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MODULE C1: DONNÉES BINAIRES & RÉGRESSION LOGISTIQUEC1.1 Introduction à la prévalence, au Risque, aux Cotes (Odds) et aux Taux
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C1.2 Le Test du Chi Carré & le Test de Tendance
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C1.3 Régression Logistique Univariée
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C1.4 Régression Logistique Multivariée
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Fin du Module C11 Quiz
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MODULE C2: DONNÉES DE SURVIEC2.1 Introduction aux Données de Survie
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C2.2 Fonction de Survie de Kaplan-Meier & Test du Log-Rank
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C2.3 RĂ©gression de Cox Ă Risque Proportionnel
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C2.4 RĂ©gression de Poisson
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Fin du Module C21 Quiz
Learning Outcomes
By the end of this section, students will be able to:
- Explain the importance of the parametric assumptions and determine if they have been met
- Explain the basic principles of rank based non-parametric statistical tests
- Describe the use of a range of common non-parametric tests
- Conduct and interpret common non-parametric tests
You can download a copy of the slides here: B3.6 Spearman’s Rank Order Correlation
B3.6 PRACTICAL: R
For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.
This can be conducted using the cor.test command, which has the structure:
cor.test(data, variable 1, variable 2, method=””)
We can specify spearman as the method to conduct a Spearman’s rank test. If the method is not specified, the default is Pearson’s correlation.
Question B3.6: What is the spearman correlation between Weight_end and BCS_end?
Answer
We can use the cor.test command with the structure specified above:
> cor_test(data, Weight_end, BCS_end, method = “spearman”)
This gives the following RStudio output:
We can see that there is a significant (p<0.05) correlation between the body condition score and the weight of the mice at the end of the study and their correlation coefficient is 0.81.
B3.6 PRACTICAL: Stata
For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.
The command in Stata is
spearman [varlist] [if] [in] [, spearman_options]
We can put multiple variables on the command line where it states [varlist].
Question B3.6: What is the spearman correlation between Weight_end and BCS_end?
Answer
Here you would report an rs value of 0.814 and a significant correlation with P<0.001.
B3.6 PRACTICAL: SPSS
For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.
Select
Analyze >> Correlate >> Bivariate
Move the two variables you are interested in into the Test Variables box. Here we are going to look at BCS_end and Weight_end.
If you put more than two variables into the Test Variables box, SPSS will perform the selected test of correction on all possible combinations.
Make sure ‘Spearman’ is selected at the bottom of the box before you press ‘OK’ to run the test.
Answer
Here you would report an rs value of 0.814 and a significant correlation with P<0.001. SPSS automatically conducts all of the correlations both ways and the correlation of each variable against itself.
If this is confusing, you can get rid of this by clicking ‘show only lower triangle’ and then deselecting ‘show diagonal’ when setting up the test. Then your output will look like this.
