Multicollinearity can lead to skewed or confusing results if they appear in your project when you attempt to find the most dependable variable from amongst your various statistics. Learning about this concept can help you understand how it happens and how it can affect your own work in ...
When multicollinearity exists, traditional regression models may yield inconsistent or unreliable results. Ridge Regression addresses this issue by adding a regularization term to the objective function, which penalizes large coefficient values. This penalty encourages the model to distribute the impact of...
Define multicollinearity in the following terms: a. In which type of regression is it likely to occur? b. What is the negative impact of multicollinearity in a regression? c. Which method is used to determine if it exists? d. If multicollinearity is fo...
It has been established that “perceived brand origin strongly affects brand attitudes, and this happens regardless of the perceptions’ objective accuracy” (Magnusson, Westjohn, & Zdravkovic, 2011, p. 454). Brand origin impacts brand credibility (Samiee, Katsikeas, & Hult, 2021). Recently, pos...
Monitoring questions will continue to change over time, and it is just not efficient to start with a clean slate every time this happens. It is a fine line between developing long-time programs in a way that allows future shifts in focus, add-ons, or changes in methodology and at the ...
As it happens with all studies, this study also has its limitations. We would love to extend our sample before 2007 and include more gas and oil indices; however, data availability was an issue, especially for futures. This study could be extended to include frontier countries in the rest ...
If loose this from your sight, you may end up choosing functions that accommodate nicely to the data you have, but not to all the data that exists (and that could exist). This is called "overfitting". Do we prefer to fit the training data with a relatively “complex” function, ...