Are al of listed below are the assumptions of bivariate regression analysis?

1) The level of measurement must be interval or ratio 2) Sample size of 25 * K 3) K is the number of predictors (in our case k=1) X and Y must both be normally distributed: use histograms, skewness, kurtosis 4) Homoscedasticity (line of best fit) 5) Linearity (If your residuals are normally distributed and homoscedastic, you do not have to worry about linearity) 6) Low multicollinearity: low correlations between two or more predictor variables (for bivariate regression we have one predictor so this should be met)