Collinearity: collections of variables that tend to move together, such as height and weight, are called collinear.This creates some challenges for analysisin that individual t-statistics tend to be less informative.Using the data found on the tab 'Collinear':

1. Filter the data to consider only the first 25 observations then run the followingmodels;repeat the analysis with all the observations and note any differences.You do not need to worry about standard data problems such as heteroscedasticity, etc. You will finish with 6 different regressions.

a. Run a linear regression to explain y in terms of experience and height.Does height appear to explain y?2 marks x2

b. Run a linear regression to explain y in terms of experience and weight.Does weight appeartoexplain y?2 marks x2

c. Run a linear regression to explain y in terms of experience and height and weight.Do height and weight appear to explain y?2 marks x2

2. Do these variables appear to be significant when considered individually?2 marks

3. Consider the results you suggest have found from the work in a. Write a paragraph or two to explain to your manager the patterns you observed with respect to the significance of the t-statistics, why these results occurred, and the strategies for using explanatory variables that exhibit collinearity.3 marks

Sheet - https://docs.google.com/spreadsheets/d/1pA7Hwjx6WDL1qu-NUTBnAr88ZRbIX4PdxXA_9Jf3wPY/edit?usp=sharing