statistical model infographic.compressed (2)
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How many dependent
variables does your model have ?
What is the nature of the dependent
variable
Are the explanatory variables continuous?
Multivariate Models
Select Count Data Models
How many levels does your categorical response variable have?
Count
>1
1
Censored
SurvivalRelated
Yes
Yes
No
Yes
No
No
No
>2
2
>1 Factor
Truncated
Yes
Continuous
Categorical
Also known as univariate models
Dependent / responsevariables represent the output or effect
Are the levels of the categorical dependent
variable ordered?
How many factors are consideredin the model?
Which one is it?
TruncatedRegressioin models
Censored Regression Models
Ordered
Multinomial
Binary Choice Model
Factorial Anova
One Way Anova Main Effects
Duration Models
Linear Regression Models
How to choose aSTATISTICAL MODEL
AKA INPUTS
Also known as multiresponse models
In these models, more than 1 dependent variables
are modelled jointly.
These models are used when the dependentvariable represents a discrete count. Thus, it takes integer values greater than or equal to zero. E.G. THE NEGATIVE BINOMIAL OR POISSON MODELS
Is the dependent variable truncated or censored?
The effect of each of the factorial independent variables is considered separately.
This ANOVA is used only when the effect of one factorial explanatory variable is considered.
These models are used when both the dependent and independent variables are continuous.
These models are used to model the time individuals are in a state before a certain event takes place. For certain individuals the event might not have yet taken place when the analysis is done.
Some of the values of the dependent variable beyond a certain threshold are not available. The values of independent variables for these observations are available.
These models consider the effectof interaction between the factorial independent variables along with their individual effects.
These models are used when the dependent variable takes only two values.e.g. LOGIT OR PROBIT MODELS
These models are used when there is no clear ordering among the levels of the response variable.
These models are used when there is a clear ordering among the levels of the response variable.
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The some values of dependent variables are excluded based on some threshold. Values of neither the dependent nor the independent variable are available.
Does the interactionbetween the factorshold importance inyour model?
Yes
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