# Regression analysis: Log-transformation to meet assumptions?

For my master's thesis I'm exploring the relationship between attitude towards the advertismenent (Aad), brand types (boutiques and high street) and willingness to recommend (willing or not). Therefore, I need to run two regression analyses:

• A bivariate regression (Aad = B0 + B1Brandtype + e)

• A multiple regression (Willingness to recommend = B0 + B1Aad + B2BrandType + B3Aad*BrandType + e

Note that the Aad data are sentiment scores derived from Instagram comments (ranging from -0,9 to 0,9, where the negative values indicate a negative attitude towards the ad). The variables willingness and brand type are dummy coded.

However, when I run the two regressions, none of the assumptions are met... (normality, linearity, homoscedasticity). So my question is: would it make sense to log-transform the variable Aad for the assumptions to be met? However, one problem is that I'm dealing with negative values and so I need to add +100 for instance and then log-transform; however, if I transform those I wouldn't be able to recognize the negative attitude anymore? So then I can only say whether it has a relationship or not, but not which brand types significantly receives more positive attitudes? Right?

• It's unlikely that log transformation would help here, even with extra constant, if only for the reason you give. Plot log x over the range [99.1, 100.9] to see one reason. I think we need information on the data to give more specific advice. How big is your dataset? Can you show a histogram for aad? Apr 6 '20 at 16:38
• Sorry, but as specified, I wanted to see a histogram for aad, namely the original outcome. The residuals don't give any kind of clear signal about whether the original variable should be transformed. Curious: what is this? SPSS? Apr 6 '20 at 17:48