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Experienced professional: SPSS,
From 75.01 C$ /h
During this course you will learn:
✓ SPSS, jamovi, jasp
✓ R Studio
✓Stata and xlstat
✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS
✓ Simple and multiple linear regression and Logistic regression
✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers)
✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means)
✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman)
✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters)
___________________________________
✓ My courses are based on exercises with the essentials of the course to remember.
✓ Working method for better understanding.
✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
✓ SPSS, jamovi, jasp
✓ R Studio
✓Stata and xlstat
✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS
✓ Simple and multiple linear regression and Logistic regression
✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers)
✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means)
✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman)
✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters)
___________________________________
✓ My courses are based on exercises with the essentials of the course to remember.
✓ Working method for better understanding.
✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
Location
At student's location :
- Around Paris, France
Online from France
About Me
Statisticien doté de 7 années d’expérience, j’ai développé une solide expertise en analyse de données au travers de nombreux projets d’études diversifiés. J’ai eu le privilège d’accompagner et d’encadrer des étudiants universitaires, des élèves ingénieurs, des chercheurs, et des professionnels de secteurs variés, en leur apportant un soutien personnalisé.
Mon approche a permis à de nombreux étudiants de surmonter leurs défis académiques et de réussir leurs projets avec succès.
Mon approche a permis à de nombreux étudiants de surmonter leurs défis académiques et de réussir leurs projets avec succès.
Education
-Doctorat en statistique et Analyse des données.
-Master en Statistique Appliquée.
-Licence en Mathématiques et Statistique.
-Supervision de projets de fin d'études et encadrement d'étudiants dans la réalisation de leurs travaux de recherche.
-Master en Statistique Appliquée.
-Licence en Mathématiques et Statistique.
-Supervision de projets de fin d'études et encadrement d'étudiants dans la réalisation de leurs travaux de recherche.
Experience / Qualifications
- Enseignement de cours de statistique appliquée, régression linéaire, analyse multivariée, tests d'hypothèses et ANOVA aux étudiants de Licence et Master.
- Supervision de projets de fin d'études et encadrement d'étudiants dans la réalisation de leurs travaux de recherche.
- Développement de nouveaux modules d'apprentissage pour intégrer des outils statistiques modernes tels que R, SPSS, JASP, Jamovi, et RStudio.
- Participation à des projets de recherche collaboratifs dans le cadre de l'université.
- Supervision de projets de fin d'études et encadrement d'étudiants dans la réalisation de leurs travaux de recherche.
- Développement de nouveaux modules d'apprentissage pour intégrer des outils statistiques modernes tels que R, SPSS, JASP, Jamovi, et RStudio.
- Participation à des projets de recherche collaboratifs dans le cadre de l'université.
Age
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
French
Reviews
Availability of a typical week
(GMT -05:00)
New York
Online via webcam
At student's home
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
During this course you will learn:
✓ SPSS
✓ R Studio
✓jamovi/jasp/Stata/xlstat
✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS
✓ Simple and multiple linear regression and Logistic regression
✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers)
✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means)
✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman)
✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters)
___________________________________
✓ My courses are based on exercises with the essentials of the course to remember.
✓ Working method for better understanding.
✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
✓ SPSS
✓ R Studio
✓jamovi/jasp/Stata/xlstat
✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS
✓ Simple and multiple linear regression and Logistic regression
✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers)
✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means)
✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman)
✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters)
___________________________________
✓ My courses are based on exercises with the essentials of the course to remember.
✓ Working method for better understanding.
✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
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