# Question 2 create a table called fulldatawithvalue

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Question 2. Create a table called full_data_with_value that’s a copy of full_data , with an extra column called "Value" containing each player’s value (according to our crude measure). Then make a histogram of players’ values. Specify bins that make the histogram informative and don’t forget your units! Remember that hist() takes in an optional third argument that allows you to specify the units! Hint : Informative histograms contain a majority of the data and exclude outliers .
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Now suppose we weren’t able to find out every player’s salary (perhaps it was too costly to interview each player). Instead, we have gathered a simple random sample of 100 players’ salaries. The cell below loads those data. : sample_salary_data = Table . read_table( "sample_salary_data.csv" ) sample_salary_data . show( 3 ) <IPython.core.display.HTML object> Question 3. Make a histogram of the values of the players in sample_salary_data , using the same method for measuring value we used in question 2. Use the same bins, too. Hint: This will take several steps.
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Now let us summarize what we have seen. To guide you, we have written most of the summary already. Question 4. Complete the statements below by setting each relevant variable name to the value that correctly fills the blank. • The plot in question 2 displayed a(n) [ distribution_1 ] distribution of the population of [ player_count_1 ] players. The areas of the bars in the plot sum to [ area_total_1 ]. • The plot in question 3 displayed a(n) [ distribution_2 ] distribution of the sample of [ player_count_2 ] players. The areas of the bars in the plot sum to [ area_total_2 ]. distribution_1 and distribution_2 should be set to one of the following strings: "empirical" or "probability" . player_count_1 , area_total_1 , player_count_2 , and area_total_2 should be set to integers. Hint 1: For a refresher on distribution types, check out Section 10.1
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