https://drive.google.com/file/d/1TmU35oIgkgZqGTIhwDiPASjy47qk9nyX/view?usp=sharing

Attached Files:(STartups Data.csv) you can get datset from above link

Many of today's leading companies, including Google, Microsoft, and Facebook, are based on technologies developed within universities. Lisa Fisher is a business school professor who would like analyze university factors that enhance innovation. The accompanying data file shows information that she has collected on 143 universities for a regression where the response variable is the number of startups (Startups), which is used as a measure for innovation. The predicator variables include the university's research expenditure (Research in $ millions), the number of patents issued (Patents), and the age of its technology transfer office (Duration in years). Load the following dataset in R.

Review the Readings/Resources in the unit. Complete the following tasks:

1. Build an lm() model in R to estimate:
   • Startups = ₀ + ₁Research + ₂Patents + ₃Duration +
2. Interpret the coefficients of Research, Patents and Duration.
3. Use the predict() command to predict the number of startups for a university that spent $120 million on research, was issued eight patents, and has had a technology transfer office for 20 years.
4. How much more research expenditure is needed for the university to have an additional predicted startup, with everything else being the same?
5. What is the standard error of the estimate?
6. Find and interpret the coefficient of determination.
7. At the 5% significant level, determine if all predictors variables individually significant in Startups (outcome).
8. Explore the data to determine if the assumptions are violated.