Search Results

Many statistics textbooks state that adding more terms into a linear model always reduces the sum of squares and in turn increases the r-squared value. This has led to the use of the adjusted r-square …

Let say I've ran this linear regression: lm_mtcars <- lm(mpg ~ wt + vs, mtcars) I can use anova() to see the amount of variance in the dependent variable accounted for by the two predictors: anova( … (Intr) wt wt -0.846 vs -0.272 0.006 The variance accounted for by each fixed effect now drops because the random intercept for cyl is now accounting for some of the variance in mpg: anova …

I tried this but it threw an error: anova(mtcars_mixed_effects, mtcars_lm) (please disregard the fact that cyl only has three groups, I'm just using one of R's built in datasets to make question reprodicible …

I have carried out this ANOVA: summary(aov(drat ~ cyl, mtcars)) Df Sum Sq Mean Sq F value Pr(>F) cyl 1 4.342 4.342 28.81 8.24e-06 *** Residuals 30 4.521 0.151 …

This R code outputs the eta squared from an ANOVA: y <- c(rnorm(30, 3), rnorm(30, 4), rnorm(30, 5)) x <- sort(rep(paste("treatment", 1:3), 30)) xy <- data.frame(x,y) xyaov <- aov(y ~ x, xy) …

I'm trying to get an intuitive understanding of why the sum of squares between groups needs to be multiplied by the number of observations within each group. Using the iris dataset in R as an example, …

I've calculated the following ANOVA table for a linear regression in R: anova(lm(mpg ~ drat, mtcars)) Analysis of Variance Table Response: mpg Df Sum Sq Mean Sq F value Pr(>F) drat 1 522.48 522.48 …

Instead, an ANOVA is advised. But if 21 t-tests were performed using these data and because there are only 7 groups, some of the same data would be used in each test. …

I've calculated an ANOVA in R using the aov() function, then calculated it semi-manually using my own code. … create fake data set.seed(50) x = data.frame(length=rnorm(9), site = c(rep('a', 3), rep('b', 3), rep('c', 3)), stringsAsFactors=F) # ANOVA …

The first model is a 2 factor ANOVA: summary(aov(angle ~ temperature + recipe, cake)) Df Sum Sq Mean Sq F value Pr(>F) temperature 5 2100 420.1 6.918 4.37e-06 *** recipe … 1.315 Correlation of Fixed Effects: (Intr) recipB recipeB -0.438 recipeC -0.438 0.500 Is someone able to provide a summary of what the mixed effect model has done differently to the ANOVA …

Should a type of mixed effects model be applied, or would a two-way ANOVA be more appropriate? If a mixed effects model is needed, then what type? …

I am considering carrying out an ANOVA to test to see if the date of the maximum count is different among sites. … Example R code: summary(aov(day.number ~ site, data=df)) Is an ANOVA appropriate here? Is there a better way of looking at differences in day number of maximum count among sites? …