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regression - Why could centering independent variables change the main . . . I want to see how muscle strength, affects bone mass and I want to take into account gender to see if it affects differently in girls and boys The idea is that the higher the muscle strength the higher the bone mass I therefore have: Dependent variable: Bone mass Independent variables: Sex, muscle strength, interaction_SEX_MUSCLEstrength
Interpreting a significant 2-way interaction with post-hoc tests Pooling across the remaining group was correct You don't need anymore tests Take choice 1 Your interaction means the effect of difficulty (hard - easy) in controls is different from the effect of difficulty in clinical Since what it means is exactly what you want to know a post hoc is completely unnecessary You might want to see Gelman and Stern (2006) for a lesson in why the path you
Trying to understand the fitted vs residual plot? [duplicate] A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line This suggests that the assumption that the relationship is linear is reasonable The res
Interpretation of Shapiro-Wilk test - Cross Validated Considering that you are pretty new to statistics, I suspect that you are thinking about this because these are residuals of an estimate of a mean and you want to know whether the assumption of normality is valid for confidence estimates using a t t -distribution t t -tests are quite robust to violations of this assumption, the data look vaguely normal in Henry's q-q plot, and the Shapiro
How to interpret interaction between a dummy and a continuous variables . . . For model 3, mother’s education is significantly and positively associated with the test scores of both boys and girls I was wondering whether my interpretations are correct I am also not sure how to interpret β3 β 3, the coefficient of the interaction term, which is insignificant for all the 3 models I look forward to your suggestions
How to analyse a Likert-type scale with a very small sample size (n=15)? In broad terms, chi-square tests are possible, but the sample size of 15 is likely to bite in one or two ways (1) your expected frequencies may be small (2) you need strong effects to establish significant differences Also, the chi-square tests take no account of the Likert scale Those are reasons why many people would prefer Mann-Whitney-Wilcoxon or ordinal logit here, although as said your
Variable slopes in a fixed effects model - Cross Validated But modelling boys vs girls as fixed effects won't allow for different slopes where as random effects will But boy and girl are fixed effects so a random effect model seems inappropriate
Interpretation of regression coefficients with multiple categorical . . . The specialty of your example, though, is that your design has missing cells Cell "girls x boyonly school" is empty, likewise cell "boys x girlonly school" So I recommend you to obtain the vector of predicted values and check yourself, which differences the coefficients represent
interpretation - Intercepts (reference) in linear mixed effect model . . . When a categorical variable has only two levels, changing the default will simply flip the sign (e g if boys are 5 inches taller than girls, then girls are 5 inches shorter than boys) but when there are more levels the changes are not as intuitively obvious