Difference between revisions of "An initial path towards statistical analysis"
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Relevant figures: <br> | Relevant figures: <br> | ||
| − | + | ==Categorical variables== | |
| − | Chi-Square test | + | ===Chi-Square test=== |
R commands: <br> | R commands: <br> | ||
Relevant figures: <br> | Relevant figures: <br> | ||
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Relevant figures: <br> | Relevant figures: <br> | ||
| − | + | ||
| + | [[Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]] | ||
| + | ====[[Poisson GLM|Dependent variable is count data]]==== | ||
R commands: <br> | R commands: <br> | ||
Relevant figures: <br> | Relevant figures: <br> | ||
| − | + | ====[[Binomial GLM|Dependent variable is 0/1 or proportions]]==== | |
R commands: <br> | R commands: <br> | ||
Relevant figures: <br> | Relevant figures: <br> | ||
| − | |||
| − | |||
| − | |||
| − | + | ||
====[[Type III Anova|Type III Anova]]==== | ====[[Type III Anova|Type III Anova]]==== | ||
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Relevant figures: <br> | Relevant figures: <br> | ||
| + | [[Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]] | ||
=====[[Poisson GLM|Dependent variable is count data]]===== | =====[[Poisson GLM|Dependent variable is count data]]===== | ||
R commands: <br> | R commands: <br> | ||
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Relevant figures: <br> | Relevant figures: <br> | ||
| + | Are there random factor variables? | ||
====[[Generalised linear mixed effect models|Random factors]]==== | ====[[Generalised linear mixed effect models|Random factors]]==== | ||
R commands: <br> | R commands: <br> | ||
Revision as of 21:11, 18 January 2021
Start here with your data! This is your first question.
Do you have several continuous variables without clear dependencies? (?)
Yes!
No!
R commands:
Relevant figures:
Contents
Univariate statistics
Does you data contain at least one categorical variable?
Yes, I have at least one categorical variable! (?) (?)
R commands:
Relevant figures:
Categorical and continuous data
Does your data consist of categorical and continuous variables?
R commands:
Relevant figures:
Does your data consist only of categorical variables?
R commands:
Relevant figures:
Categorical variables
Chi-Square test
R commands:
Relevant figures:
Does you categorical dependent variables have 1-2 factor levels?
Does you categorical dependent variables have more than 2 factor levels?
Analysis of Variance
R commands:
Relevant figures:
Dependent variable normally distributed
Type II Anova
R commands:
Relevant figures:
Dependent variable not normally distributed
Dependent variable is count data
R commands:
Relevant figures:
Dependent variable is 0/1 or proportions
R commands:
Relevant figures:
Type III Anova
R commands:
Relevant figures:
Dependent variable not normally distributed
Dependent variable is count data
R commands:
Relevant figures:
Dependent variable is 0/1 or proportions
R commands:
Relevant figures:
Are there random factor variables?
Random factors
R commands:
Relevant figures:
No, I have only continuous variables! (?) (?)
R commands:
Relevant figures:
Multivariate statistics
Yes!
Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?
Does your independent variable contain only 1 or 2 groups?
Does your independent variable contain more than 2 groups?
Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?
t-test
Chi-Square test
Continuous variables
Non dependent relations? Correlations
Clear dependent relations
Regression models
Dependent variable normally distributed
Linear Regression
Dependent variable not normally distributed
Dependent variable is count data
Dependent variable is 0/1 or proportions
Resterampe
[[At least one continuous and one categorical variable|
More than 2 groups
Analysis of Variance
Dependent variable normally distributed
INSERT TYPE II
INSERT RANDOM FACTOR
INSERT LMM
Dependent variable not normally distributed
Dependent variable is count data
Dependent variable is 0/1 or proportions
