I have a model that outputs 0 or 1 for interest/not-interest in a job. I'm doing an A/B/C test comparing two models (treatment groups) and none (control group). ANOVA for hypothesis testing and t-test with Bonferroni correction for posthoc testing is my plan. But both tests assume normality. Can we have normality for 0 and 1? If so, how? If not, what's the best test (including posthoc)?

I have a problem where the target variable Y (continuous, values: 0-1) is controlled by large number of variables. These variables can be grouped by the nature of the data: Group 1 - x1, x2, x3, x4 Group 2 - x5, x6, x7 Group 3 - x8, x9, x10, x12 After modeling Y~X, I would like to disaggregate the impact of these groups. Example, I want to have a plot like this famous Hawkins and Sutton plot of climate change …

I would like to export tables for the following result for a repeated measure anova: Here the function which ANOVA test has been implemented fAddANOVA = function(data) data %>% ezANOVA(dv = .(value), wid = .(ID), within = .(COND)) %>% as_tibble() And here the commands to explore ANOVA statistics aov_stats <- df_join %>% group_by(signals) %>% mutate(ANOVA = map(data, ~fAddANOVA(.x))) %>% dplyr::select(., -data) %>% unnest(ANOVA) > aov_stats # A tibble: 12 x 4 # Groups: signals [12] signals ANOVA$Effect $DFn $DFd $F …

I would like to run one-way ANOVA test on my data. I saw that one of several assumptions for one-way ANOVA is that there needs to be homogeneity of variances. I have run the test for different data-sets. I find sometimes my p-values are larger than 0.05 and for some datasets it is smaller. As I understand, if the p-value is smaller than 0.05, then I can reject the null hypothesis and tell that the variances are not equal (and …

I selected features using ANOVA (because I have Numerical data as input and Categorical data as target): anova = SelectKBest(score_func=f_classif, k='all') anova.fit(X_train, y_train.values.argmax(1)) # y_train.values.argmax(1) because I already one-hot-encoded the target. When I plot the score, it show me the figure in image : plt.xlabel("Number of features selected") plt.ylabel("Score (nb of correct classifications)") plt.plot(range(len(anova.scores_)), anova1.scores_) plt.show() What does the interpretation of this figure ? why there is some interruption in the plot ?

I am trying to run kruskawallis test on multiple columns of my data for that i wrote an function var=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'] def kruskawallis_test(column): k_test=train.loc[:,[column,'SalePrice']] x=pd.pivot_table(k_test,index=k_test.index, values='SalePrice',columns=column) for i in range(x.shape[1]): var[i]=x.iloc[:,i] var[i]=var[i][~var[i].isnull()].tolist() H, pval = mstats.kruskalwallis(var[0],var[1],var[2],var[3]) return pval the problem i am facing is every column have a different number of groups so var[0],var[1],var[2],var[3] will not be correct for every column. mstats.kruskalwallis() take input vector which contain values of each group to be compared from a particular column.(as per my knowledge). …

I have seen researchers using pearson's correlation coefficient to find out the relevant features -- to keep the features that have a high correlation value with the target. The implication is that the correlated features contribute more information in finding out the target in classification problems. Whereas, we remove the features which are redundant and have very negligible correlation value. Q1) Should highly correlated features with the target variable be included or removed from classification problems ? Is there a …

I am doing an analytic exploratory analysis. If the target is a continuous variable and the attributes are all categorical (discrete values), in order to know if exist any influence on the target from the each attribute I am doing the ANOVA-test like this: fvalue, pvalue = stats.f_oneway(df[y], df[x]) pvalue < 0.5 If that condition is true, there is a dependency between variables. For all variables I get true dependency with ANOVA, but the values of the correlation are between …

I've been working on examining statistical relationships between variable: Pearsons, Spearman's for continuous variables Kendall's Tau, Cramer's V for ordinal/nominal variables. I know there's many more ways. Recently I read about ANOVA and hypothesis testing. It seems similar to measuring correlation and association. In fact, I can't tell if it is just another way of doing the same thing, or if it is something entirely different. Most explanations of ANOVA seem a bit more complicated than most explanations of correlation …

I have a data set with categorical and continuous/ordinal explanatory variables and continuous target variable. I tried to filter features using one-way ANOVA for categorical variables and using Spearman's correlation coefficient for continuous/ordinal variables.I am using p-value to filter. I then also used mutual information regression to select features.The results from both the techniques do not match. Can someone please explain what is the discrepancy and what should be used when ?

So I used python to run multi-factorial ANOVA analysis on a data set. I first used a ols.fit() and then the anova_lm function. I realized for the variables I am analyzing their degree of freedom is 1. Does that mean only 1 value out of my data is extracted and used for calculation? Why is the residual df so high? import pandas as pd from statsmodels.multivariate.manova import MANOVA import statsmodels.api as sm from statsmodels.formula.api import ols from statsmodels.stats.anova import anova_lm …