EBS NBA Player Dataset Analysis Presentation

I need to do a presentation with power point, nothing too fancy that checks all the points in the guideline about a casual analysis using Stata (we can get to an agreement to which platforms to use).GuidelinesFinal Project Here are the instructions of the project. Below you can find a description of the datasets.Create a presentation (PowerPoint slides) that should contain the following sections:a) Motivation (Explain what made you conduct the study.)b) Research question (What is the causal relationship you are interested in?)c) Describing the empirical analysis (Description of the causal analysis you want touse. Should include the benchmark OLS regression)d) Data and main variables (Brief description of the dataset and the variables that youare using.)e) Bias analysis (Discuss problems that could potentially bias the previous regression.)f) Solution to the bias (Run alternative regression using methods to fix the bias, e.g., byadding control variables or by using instrumental variable. Discuss whether the resultschange.)g) Results (Detailed discussion of the findings of the analysis)h) Conclusion (Concluding remarks and potential shortcomings.)You must submit the projects in a folder that contains:a) The presentation slidesb) The do file (it can be in stata or r) containing the codes to create the tables andthe figures, and,c) The data file used for the analysisThe following steps will help you to understand how to approach the project.A) Select one of the datasets:1) productivity.dta2) fintech.dta3) fintech-panel.dta4) work_from_home.dta5) airfair.dta6) apple.dta7) salaries.dta8) crime.dta9) fertility.dta10) hprice.dta11) children.dta12) nba.dta13) patent.dta14) Optional: Your own datasetB) Explain a causal effect you want to test with the dataC) Select variables from the dataset and run a regression to test the causal effect you want tostudy.D) Discuss problems that could potentially bias the previous regression and suggest themeans to overcome the bias.E) Run an alternative regression using methods to fix the bias (if the data are available).Discuss whether the results changeDescription of datasets1) Productivity: Data of employees’ performance. The data come from many companiesin different countries.2) Fintech: Data of household characteristics, property ownership and debt. The datacome from the Spanish Household Finance Surveys of 5 different years.3) Fintech-panel: Data of household characteristics, property ownership and debt, fromthe Spanish Household Finance Surveys. Panel data of the same households form 3different years.4) Work from home: Data of employees’ demographics and performance from onecompany. The data come from a six-months study of employees working from homeversus employees working from the office.5) Airfair: Panel data on airline flights.6) Apple: Data on quantity of ecologically friendly apples desired by a survey ofindividuals7) Salaries: Salaries and proximity to college data8) Crime: data on county level crime rates9) Fertility: Data on number of living children, education, and demographic informationof a sample of women from Botswana10) Hprice: Data on housing characteristics and prices11) Children: Cross-sectional data on the number of children born and the mother’s workhistory and demographics12) NBA: Data on earnings, position played and demographics of a sample of NBA players13) Patent: Panel data on the number of patents sought and obtained by a sample of firms along with some firm-specific information.