DATASET: https://www.kaggle.com/artimous/complete-fifa-2017-player-dataset-global

Professional athletes are often judged in terms of performance on a wide variety of metrics, which feed into a wide array of uses from fantasy leagues and gambling to AI development in video games. We will use the Fifa 2017 video game data as a proxy for actual soccer (football) player performance. Import the Fifa CSV file.

Mean composite scales are variables that represent a mean score built from other variables. Use R to construct a mean composite scale that represents a player's kicking abilities. Use the following variables: Shot_Power, Finishing, Long_Shots, Curve, Freekick_Accuracy, Short_Pass, and Long_Pass. Attach your new variable to the Fifa 2017 dataset. See the R setup file for more on how to construct a mean composite scale with an example.

The following questions will have you evaluate the new kicking ability scores of all Fifa 2017 players, with those of the top and bottom ranked football clubs: FC Barcelona and Longford Town respectively. [1] The club names are the same in the R data. You will need to use subsetting techniques to isolate the scores for each team. You may use whichever subsetting approach you prefer.

2a) Report summary statistics below for this new kick scale variable for all Fifa players, FC Barcelona, and Longford Town clubs (PQ table with n, min, max, 1^st & 3^rd quartiles, median, mean, and sd).

2b) Calculate a 95% confidence interval for the mean for all Fifa players, FC Barcelona, and Longford Town. Do this by hand below and confirm your work using R. Round your answer to 2 decimal places.

What can you conclude from your confidence intervals? Not graded, be descriptive.

[1] As reported using the top and bottom non-zero average rating on https://www.futhead.com/17/clubs/?sort=-average_rating.