Which data transformation will give the data scientist the ability to apply a linear regression model?

Explanations:

An exponential transformation would increase the right skewness, as it applies an exponential function to each data point, making the data less suitable for linear regression.

A logarithmic transformation reduces right skewness, making the data more normally distributed, which is ideal for linear regression as it reduces the influence of outliers.

A polynomial transformation increases nonlinearity in the data, which could be useful for non-linear models but is not suitable for achieving a linear regression.

A sinusoidal transformation is typically used for cyclical or periodic data and does not correct skewness, making it unsuitable for this purpose.