A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the…

Exponential transformation would increase the skewness of an already right-skewed distribution, moving it further away from a normal distribution suitable for linear regression.

When mode < median < mean, the data distribution is positively or right-skewed. A logarithmic transformation (e.g., natural log or log base 10) is a standard technique to reduce the skewness in right-skewed data, making its distribution more symmetric and closer to normal, which is often a desirable assumption for linear regression models.

Polynomial transformation primarily helps capture non-linear relationships between variables but does not inherently correct for skewness in the distribution of a single variable to make it more Gaussian.

Sinusoidal transformation is used for cyclical or periodic data, which is not indicated by the described skewness of the distribution.