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Hints and pseudo code for Exercise 6.6.2 (Fox et al., 2016)

  1. Import the data using pandas , and count the number of reviewers (by summing ReviewerAgreed) for each manuscript (i.e., unique MsID). The column FinalDecision contains 1 for rejection, and 0 for acceptance. Compile a table measuring the probability of rejection given the number of reviewers. Does having more reviewers increase the probability of being rejected?

    Hints: it is convenient to write a function that takes the data and a year as input, and prints the probability of rejection given the number of reviewers for that given year. We can set the function to return the general rejection rate if all is specified instead of the year.

    Pseudocode:

        import pandas
    import numpy as np
        # read the data using pandas
    fox = pandas.read_csv("../data/Fox2015_data.csv")
        use a combination of list and set to extract the unique `MsID`
    
    now go through each manuscript and store
    i) the final decision (reject/accept) in the np.array final_decision
        ii) the number of reviewers in the np.array num_reviewers
        iii) the submission year in the np.array year
    
    def get_prob_rejection(my_year = "all"):
    if my_year == "all":
       do not subset the data
    else:
        subset the data to use only the specified year
    for each number of reviewers:
        compute probability of rejection and produce output
  2. Write a function to repeat the analysis above for each year represented in the database.

    Hints: if you have written the function get_prob_rejection as specified above, this part is more straigthforward to implement.

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