This gretl data file contains 800 observations, where each observation corresponds to a single credit card transaction. For each observation, the dataset contains the following variables:

isfraud <-- Indicates whether the transaction was fraudulent

numdayopen <-- The number of days that the credit card account has been open

value <-- The value of the transaction in dollars

tranid <-- An internal transaction id used by the bank

userid <-- An internal user id attached to the account that is used by the bank

last4dig <-- The last 4 digits of the credit card used in the transaction

dayind <-- An integer indicating the day of the week. 1 for Monday, 2 for Tuesday, and so on, up to 7 for Sunday.

bankid <-- An integer used by the regulator to identify banks. There are 6 banks in the industry, hence it takes values 1 to 6.

international <-- An indicator of whether the transaction was domestic or international

You have been provided with the dataset by a regulatory body, and asked to construct a model for predicting the probability that a given transaction is fraudulent. The regulator would like you to write a report that addresses the following questions:

(a) Which variables (or transformations of variables) affect the probability that a transaction is fraudulent in a statistically significant sense? Provide evidence of statistical significance in your report.

(b) Choose some representative values for the explanatory variables in your model, and use those values to provide some estimates of the probability that a transaction is fraudulent. Given your representative values, provide an estimate of the change in probability that a transaction is fraudulent for an international transaction versus a domestic transaction.

(c) Is there any evidence that some banks are more likely to faciliate fraudulent transactions than other banks?

The regulator has also indicated that they are interested in using your model for future work. Consequently it is VERY important to the regulator that your report explains why you have chosen to use the model that you have, and includes any tests that demonstrate the validity of your model.

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You should provide answers to these questions using econometric modelling techniques that you have learnt in ECON2032 this semester.You will not earn additional credit for demonstrating techniques learned outside ECON2032. Remember, we are trying to assess your understanding of the content taught specifically in ECON2032. This means that answers generated by ChatGPT, are not only a clear instance of academic misconduct, but will also likely attract poor marks, because ChatGPT does not know what you learned in ECON2032.

There are two submissions you must make as part of this report.

The first submission is a PDF formatted file containing your report. In most cases, you will be assessed entirely on the contents of the PDF file. Therefore, any content that you think the marker needs to see to properly assess your work MUST be in the PDF report. This PDF file should be submitted on ilearn to the link "Assignment (Submit report pdf here)", located immediately below this question link. The PDF should be typed. Do NOT hand-write your report and upload a pdf scan. Handwritten reports will NOT be accepted and will attract a mark of zero.

Your PDF report should should provide a clear statement of your answers to the above questions, a clear description of the econometric techniques and results that you used to generate your answers, and a clear, convincing justification of the techniques that you used. Your main objective is to convince the marker that your results are credible, so for every estimator or statistic that you use in your answers, you should clearly state the properties that you believe the estimator or statistic has in your application, and explain why you think these properties hold in your particular case (for example, if you believe that the classical assumptions are reasonable for your model, then the OLS estimator would be unbiased and efficient, and this would be a good reason to base your answers on the OLS method, provided that you can convince the marker that the classical assumptions hold). If you think that there are any weaknesses in your results or approach, then you should state them clearly. Your report should consist of fewer than 1000 words (possibly much fewer). It should NOT include appendices. Instead, any tables, figures, etc that you think are relevant should be included in the text of the report at the point at which they are discussed. You should proofread your work and ensure that the spelling and grammar are correct. You should use a font type and size that are easy to read (e.g. Times New Roman 12). Any equations should be typeset using your software package's equation editor (or equivalent). Tables, figures, etc should have titles and appropriate labels. Your report should be saved as a PDF file. Recent versions of Microsoft Word are able to save files in the PDF format (but you should check the output carefully), as are OpenOffice, LibreOffice and many other document preparation software packages. You should check that you are able to create a PDF file with your software before you start typing your report. If you need help creating PDF files, make a request in the online discussion forums. Marks will be deducted for poor presentation and, in extreme cases where the marker is unable easily to understand parts of your assignment, part (or all) or your assignment may attract no marks.

The second submission is any supplementary files used in your calculations. At a minimum, this should include a gretl session file showing the model(s) you estimated in gretl. When you estimate a model in gretl, in the model output screen, click File --> Save to session as icon. Upon doing this, a list of icons will appear showing the contents of the current session. The latest in the list will be the model you just saved. Please give it a useful name by right clicking the icon and using the "rename" option. You can do this for multiple models. Once you have finished all analysis in gretl, in the main gretl window, click File --> Session Files --> Save session as. The file you save is the gretl session file, and should be submitted at the link titled "Assignment (Submit gretl/excel session files here)", located immediately below the pdf submission link. While most students probably won't have an Excel file, if you did happen to use Excel for any transformations, please also submit the workbook at this same link.

Ideally, the marker should not need to refer to your gretl session file, as all relevant content to understand what you have done SHOULD be contained within your PDF report. However, in some instances, elements of a report may be unclear in how they were obtained, and so in these instances, the marker may refer to your session files to try and work out what you have done. It follows that not including a session file could potentially result in a very poor grade, since if the marker is not able to follow what you have done in your PDF report, and has no session file to fall back on, then they can not award marks for things they do not understand.

please create a pdf that meets the above requirements