Please use R studio and code the following questions:

1. Kit, is a very good boy. When I take Kit on walks, he is always (okay - mostly) well-behaved. When other people walk him, they claim that he barks at other dogs, pulls on the leash, eats bread off the sidewalk, and various other kinds of 'misbehaving'. In general, they don't believe that Kit is half as good as I say he is, which is really a shame.

Being a data-driven dog owner, I have been collecting data on each walk including the number of 'incidents', the length of the walk, the number of other dogs encountered, whether we encountered a mailman, the number of squirrels, the number of fire-hydrants passed, and the general time of day. Using the data provided, help me prove to everyone that Kit is in fact a good boy (or, the very unlikely alternative - that Kit is not as good as I think he is...). - use this data -

https://docs.google.com/spreadsheets/d/1DDZT0QWmFqsNsHYPDY6MXmWKUlhhXdM6N2wb0wjTEX4/edit?usp=sharing

1. Prepare the data for modelling. What steps did you have to take and how?
2. I believe that Kit may be better behaved specifically when I walk him vs. when anyone else walks him. Conduct a test to see if this is the case. Briefly explain the test and your results (i.e., state the null, alternative, the p-value of the result and what it means.)
3. Now conduct a Chow test to determine if there is any difference at all when someone else walks Kit. Briefly explain your results, specifically in light of the test above.
4. Suppose the last 30 observations I'm actually walking Kit with another person (i.e., two of us are walking Kit together). Extend the Chow test logic to test for any difference between the three scenarios (i.e., me alone, someone else alone, me & someone else together). Explain your results

1. You are interviewing for a job at a prestigious consulting firm that focuses on solving business problems using analytics. The following are the questions asked by the recruiting manager - they are based on real client problems and questions. For each question, provide a clear and concise answer that shows you understand the material from both a managerial and statistical perspective.
   1. Your client is proposing a satisfaction survey which should generate about 5000 responses. One of the questions has potential responses 'very dissatisfied', 'dissatisfied', 'no opinion', 'satisfied' and 'very satisfied'. Since there is a natural ordering to these results, he is not sure if he should code the results 1,2,3,4,5 respectively or -2,-1,0,1,2 respectively. Explain and justify your thoughts on how the data should be coded, and any strengths and weaknesses the choice of coding has.
   2. How could you find a multi-dimensional outlier in a linear regression context? What should you address about it?
   3. Your client is worried about the restrictions imposed on a linear model. She suspects some of the relationships have non-linear patterns like decreasing returns to scale or an exponential shape. What would you tell her?