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How to visualize relationship between continuous predictor and discrete variable?
There are many options to show the discrete variable on the x-axis, with the continuous variable on the y-axis (e.g., dotplot, violin, boxplot, etc). These options show the distribution of the continuous predictor with a measure of centrality for each group of the discrete variable.
What are the predictor variables for a regession model?
You can make a regession model with three predictor variables. Now you can use age and weight (body weight in kilogram) and HBP (hypertension) as predcitor variables.
Can you make a regression with two predictor variables?
With this plot, you can identify the points and see the regression equation with your mouse. You can make a regression model with two predictor variables. Now you can use age and sex as predictor variables.
When to use mixed effect logistic regression in data analysis?
Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and
How is canonical correlation used in statistical analysis?
Canonical correlation is a multivariate technique used to examine the relationship between two groups of variables. For each set of variables, it creates latent variables and looks at the relationships among the latent variables. It assumes that all variables in the model are interval and normally distributed.
When to use independent samples in statistical analysis?
An independent samples t-test is used when you want to compare the means of a normally distributed interval dependent variable for two independent groups. For example, using the hsb2 data file, say we wish to test whether the mean for write is the same for males and females. t-test groups = female (0 1) /variables = write.
When do you need a nonparametric statistical test?
If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test, which allows you to make comparisons without any assumptions about the data distribution.