I am currently learning R and I am relatively inexperienced in the field. Hope I can get some advice from you guys!

I am working on a project where I have to estimate the average processing time of different work items (tasks).

I have the following panel data:

My sample size is n=2000 individual workers, and T=10 (each time interval is a four week period)

  • Independent variables: 51 different work items. I have count data for each work item (# of times they are performed by each worker over a four week period)

  • Dependent variable: Total Working Hour of the worker (over a 4 week period)

The goal of my analysis is to find the regression coefficents (which are estimâtes of the average completion time of each work item). I may also include other regressors (other than #of work items) such as experience, age... into my model.

y= Bo + B1*X1 +...+Bk*Xk + e

y: total working hours
X: # of each work items type


Right now, I finished cleaning and processing the data and I performed some exploratory data analysis.

  1. Some work items have a lot of zeros (the work item is only performed once or twice by several workers in the time period).

  2. From VIF, I can see that there are imperfect multicollinearity in the independent variables. Some independent variables have VIF of 5 to 6.


  1. Any advice on how I should specify my model?

I look at boxplots and eliminate outliers of each regressor, I see that some regressors are highly skewed (due to lots of zéros).

I also plot each regressors against the total complétion time to see if there is any linear relation. So do, other looks more like a quadratic relation.

  1. Any way to deal with the multicollinearity aside from eliminating the regressors that have high VIF? This is because I need to estimate the coefficent of each of the work item.

  2. Should I set the intercept to 0? I know for sure that when ALL the regressors are 0 (# of work items are all 0, I should have zero total working hours).

I would also welcome any other advice for this problem. Thanks!

  • $\begingroup$ You should not set the intercept to zero, you could find dead times of a worker, and those are times not related to specific items (meaning: Intercept) $\endgroup$ Apr 15, 2019 at 6:12

1 Answer 1


As you would like to retain all the predictors, you should try implementing ridge regression, which is a regularization technique popularly used for multi-collinearity problems like yours, by means of coefficient shrinkage.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.