In data analysis, which two methods should a data scientist use to identify factors affecting product sales?

The correct choice involves conducting regression analysis and focusing on multivariate analysis. Regression analysis is a powerful statistical method used to understand the relationship between one dependent variable (in this case, product sales) and one or more independent variables (factors affecting sales). This approach enables the data scientist to quantify how changes in those factors affect sales, helping to identify which elements are most influential.

Moreover, multivariate analysis allows the examination of multiple variables at once. This is particularly important in understanding complex relationships where various factors may interact with each other. By analyzing multiple variables, data scientists can uncover more nuanced insights and detect patterns that may not be visible when looking at variables in isolation.

The other options do not provide the same level of depth in analysis. For example, focusing on simple random sampling and univariate analysis limits the examination to single variables without accounting for interactions, which can oversimplify the complexity involved in product sales. Additionally, ignoring multicollinearity is misleading, as it can result in unreliable regression coefficients and interpretations if independent variables are highly correlated. Thus, the combination of regression analysis with a multivariate approach is the most robust method to identify factors affecting product sales effectively.