What type of statistical anylasis should I do and why? I want to run an anaylsis
ID: 3312404 • Letter: W
Question
What type of statistical anylasis should I do and why? I want to run an anaylsis in R using data my class has collected. I want to see if age effects parasite number. Age is typically continouos; however, our data only distingusises between adults and juvinels, therefore I believe it would the be considered categorical. Then pasiste burden is numerical, so it is ccontinuous.
I know I shouldn't do a linear regression because thats for 2 continous varibles... what type of anayalsis is correct and can you explain why?
Also is it correct that if my hypothesis is that adults will have a higer parasite burden than juviniles would my dependent varible will be age and independent varible be # of parasites.
Explanation / Answer
What type of statistical anylasis should I do and why?
Answer:
There is a wide range of statistical tests. The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general,
Type of Test
Use
Correlational
These tests look for an association between variables
Pearson correlation
Spearman correlation
Tests for the strength of the association between two ordinal variables (does not rely on the assumption of normally distributed data)
Chi-square
Tests for the strength of the association between two categorical variables
Paired T-test
Independent T-test
Tests for the difference between two independent variables
ANOVA
Tests the difference between group means after any other variance in the outcome variable is accounted for
Regression: assess if change in one variable predicts change in another variable
Simple regression
Multiple regression
Tests how change in the combination of two or more predictor variables predict the level of change in the outcome variable
Non-parametric: used when the data does not meet assumptions required for parametric tests
Wilcoxon rank-sum test
Wilcoxon sign-rank test
Tests for the difference between two related variables—takes into account the magnitude and direction of difference
Sign test
Tests if two related variables are different—ignores the magnitude of change, only takes into account direction
Hope this will be helpful. Thanks and god Bless you :-)
Type of Test
Use
Correlational
These tests look for an association between variables
Pearson correlation
Tests for the strength of the association between two continuous variablesSpearman correlation
Tests for the strength of the association between two ordinal variables (does not rely on the assumption of normally distributed data)
Chi-square
Tests for the strength of the association between two categorical variables
Comparison of Means: look for the difference between the means of variablesPaired T-test
Tests for the difference between two related variablesIndependent T-test
Tests for the difference between two independent variables
ANOVA
Tests the difference between group means after any other variance in the outcome variable is accounted for
Regression: assess if change in one variable predicts change in another variable
Simple regression
Tests how change in the predictor variable predicts the level of change in the outcome variableMultiple regression
Tests how change in the combination of two or more predictor variables predict the level of change in the outcome variable
Non-parametric: used when the data does not meet assumptions required for parametric tests
Wilcoxon rank-sum test
Tests for the difference between two independent variables—takes into account magnitude and direction of differenceWilcoxon sign-rank test
Tests for the difference between two related variables—takes into account the magnitude and direction of difference
Sign test
Tests if two related variables are different—ignores the magnitude of change, only takes into account direction
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