Ps contrasts in R and Xlstat are sum(ai)=0. According to my thoughts and publications I read, it doesn't have any sense … It includes regression (linear, logistic, nonlinear), multivariate data analysis (PCA, DA, CA, MCA, MDS), correlation tests, parametric tests, non parametric tests, ANOVA, ANCOVA and much more. Then, I would like to ask you about the relevance of these beta coefficients for a categorical variable. XLSTAT is a complete analysis and statistics add-in for MS Excel. So, my question is this one : what is the formula to obtain the beta coefficient in a one way Anova ? Essentially it is an extension of the Wilcoxon. Moreover, I conducted the anova on Statistica in order to see if XLStat did any mistake but its outputs for the beta coefficients are the same than in Xlstat … The Kruskal-Wallis H test is a non-parametric test that is used in place of a one-way ANOVA. Given that the first one is the same with R and XLStat, I suppose that Xlstat convert too the categorical variable in numeric variable (which is senseless but this is not the question).
The first beta I obtained with R is the same than in XLstat, but not the second and the third. sd(x) being the standard deviation of the categorical variable (which is automatically transformed as numeric variable with R, in order to calculate sd(x), seems logical ) and sd(y) being the standard deviation of my response variable. Firstly, I was surprised, because I didn’t think we could calculate beta coefficient for a categorical variable and according to the bibliography I read, it doesn’t have any sense.Īnyway, I tried to find these coefficients with R, thanks to the unique formula I found : beta = estimate * sd(x)/sd(y). However, XLstat offers an extra output : the standardized coefficients (called too beta coefficients). I’ve conducted this ANOVA on R and XLStat and the outputs for the F fisher, p-value, coefficient estimations, t-values, std error … are exactly the same. I’ve conducted an one-way ANOVA (my categorical variable is 3 modal (1,2,3) and my response variable is quantitative on scale 1-10). How models work technically A model allows to explain one or several dependent variables using one or several independent variables through mathematical equations that involve parameters. It allows to better understand reality and to do predictions.
ANOVA XLSTAT SOFTWARE
I’m working with the software R and XLStat. 14 Principles of Statistical Modeling Definition A statistical model is a simplified representation of a phenomenon using numbers.
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