A professional memo and using R

Return to the venture capital setting discussed in week 4 (using the large dataset: company confirm.csv U ). Your company has founded a corporate venture capital department and made investments in a number of small companies. Now a debate is raging within the corporate VC department on what advice to give those startups. In particular, the question is whether the startup should focus on hiring: a) engineers who develop products, b) sales/marketing people to procure customers, or c) business strategists to identify market segments with the "right" amount of competition? Instructions: Your boss believes your company should do some corporate venture capital (VC) investing, taking financial stakes in startups that have a good chance of becoming public (have an IPO) or being acquired by another firm. Either outcome (known in VC as a liquidity event) promises great financial returns. The question is: how does your corporate VC department identify promising startups? Your boss has a theory that, all else equal, having a "good name" matters. He is a little vague on what constitutes a "good name", but maybe there is something to this idea. To get you started, he has instructed his assistant to read the newspapers and put together a spreadsheet that will allow you to do a pilot study. The table "company explore U " contains the following variables for 193 US-based startups first receiving venture capital from 2006 to 2015: Companyname = company name string Companymetroregion = regional location of company Companyindustrymajorgroup = major industry of company yearfirstVC = year of first VC investment VCfirms = number of VC firms investing in the company Product_n = # of product types produced Competitor_n = # of known competitors Customer_n = # of existing customers Companyexit = I if firm had an IPO or was acquired Do the following: Run and report 4 or more regression models with Companyexit as the dependent variable o for model I, use product_n (associated with engineers), customer_n (associated with marketers/sales people), and competitor_n (associated with business strategists) as independent variables o for models 2-n: explore different modeling permutations, such as changing control variables, transforming your independent variables of interests to binaries or logs, using subsamples, etc. Taking your analysis at face value, should startups focus on hiring engineers, sales/marketing people, or business strategists? Explain why that recommendation makes sense. Digging further, explain why someone else might make a different recommendation based on exactly the same analysis Submission Format Guidelines: Treat this assignment as if you are writing a professional memo. The report must consist of prose (PDF or MS Word format) and contain neatly formatted tables and figures. You may use bullet points to summarize facts and findings but must fully explain those points. You may not exceed one page of prose and one page of tables or figures. Submit your code as a separate appendix. companymetroregion companyindustrymajorgroup yearfirstVC VCfirms companyexit product_n competitor_n Customer_n name_lengtt AZname Companyname 1 Wr Grace The Lea Twin Cities 2 Topps Company II New York Metro 3 Wf Taylor Co lnc Other US 4 utc Holdings lnc Other US 5 Stant Corp Great Lakes 6 KYOCERA Senco Great Lakes 7 Seaboard Folding Boston 8 Publishing Busine! New York Metro 9 LifeCore Biomedic Twin Cities 10 Boomerang Media New York Metro II Copper-weld Birne Other US 12 Hyatt Hotels Corp. Chicago Non-Hgh-Technology Computer Related Non Fl-Technology Computer Related Non-Hig Technology N on High-Technology Non-Hiqh-Technology Computer Related Medical/Health/Life Science Non-High-Technology Nan -High-Technology N on-High-Technology 13 Gateway Inc Orange County_ Non-High-TechnoIogy 14 Energy Focus lnc Great Lakes 15 Chesapeake Energ Other US 16 US FOODS HOLD Chicago 17 OPKO Health lnc Other US 18 Media Source lnc Great Lakes 19 Triumph Group Inc Philadelphia 20 Caesars Entertain' Other US 21 CommScope lnc other US 22 Archstone Denver Semiconductors/Other Elect Non-Hqh-Technology Nan-Hig Biotechnology Non-Hiqh-Technoloqy Nan-High-Technology Nan-High-Technology Communications and Media Nan -High-Technology 2015 2014 2007 2010 2010 2007 2014 2011 2007 2007 2014 2012 2011 2012 2010 2010 6 0 0 0 0 23 Carramerica Realt Washington Metroplex Non-High-Technology 24 MiTAC Digital consan Jose 25 Jupiter Holding I C Los Angeles 26 Cobra Electronics Chicago 27 Katahdin Industrie Boston 2B prime Group Realt Chicago 29 National City Corp Great Lakes 30 Expert Global SOIL Philadelphia 31 Strategic HealthcaLos Angeles 32 DURA Automotive Great Lakes 33 Consumer Portfoli Orange County Communications and Media Nan-High-Technology Communications and Media Non h-TechnoIogy Nan High-Technology N on-High-Technology Nan-High-Technolog Computer Related N on-High-Technology N on-High-Technology 0 0 10 0 4 8 3 0 16 0 7 15 3 0 35 0 0 0 14 0 34 Watergate Hotel Washington Metroplex Non-High-Technology 35 AirTran Holdings Other US N on-High-Technology 28 17 16 16 10 24 25 27 25 19 27 24 16 22 22 15 16 17 33 13 23 18 22 22 25 24 25 27 35 27 31 15 20 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE explore FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE