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 1. Using Capital IQ1 , download data for at least 50 US firms for the period 2010-2019. Present a table of summary statistics for all the variables used in the project (including the components of Tobin’s Q). Make sure to include: mean, standard deviation, min, max, 25% percentile, 50% percentile, 75% percentile, number of firms, number of firm-year observations. Note that it could be the case that a particular firm has data available for 10 years, whilst another firm has data available for, for example, only 5 years. As long as you have a minimum of 5 observations per firm this is ok. Label this table: Table1: Summary Statistics2  and include a legend at the end of the table that defines each variable; e.g. Price denotes Day Close Price; Equity denotes Total Common Equity, etc. 

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2. Firm size is measured as log(Total Assets). Performance is measured with Tobin’s Q (total assets plus market value of equity less book value of equity divided by total assets; where market value of equity equals price per share times the total number of shares outstanding)3 . Choose the largest and the smallest firm for which you have 10 years of data. Is average performance statistically different between these two firms? Answer yes or no and show two different ways in which you could reach this conclusion. Make sure to show all the details of your tests and present a table for each test. Label these tables: Table2A: Differences in Performance_1 and Table2B: Differences in Performance_2 (50 words max)

 3. You will run a cross-sectional regression. Therefore, compute time averages for every variable for each firm. You will end up with 50 cross-sections. Run a simple regression analysis to assess whether larger firms are associated with better performance. Note that you should run this regression using 50 observations. Label this table: Table 3: Simple-regression [2 marks] 4. Discuss whether the coefficient on size is statistically significant at the 5% level and interpret the coefficient (50 words max).

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