A company selling a relatively high-value service wants to test which of two web designs does a better selling job. It is reasonable to think that a web design that holds people's attention longer will lead to more sales. Hence, our metric is average session time (how long people spend on the website), comparing Design A to Design B. "web_page_data.csv" includes your sample data of 36 observations. 1) Which statistical test can tell us if the average session time in Design A and Design B are different? 2) Create a useful visualization or a summary of the data for this analysis. 3) Conduct the appropriate statistical test and interpret the results in a comment line. Part 2 In the "Fav_Sports.csv" dataset, students at Le Moyne College and Syracuse University were asked whether Basketball, Soccer, or Football was their favorite sport. Do these data provide evidence to suggest that favorite sports vary by the university? 4) Which statistical test can help us answer this question? 5) Create a useful visualization or a summary of the data for this analysis. 6) Conduct the appropriate statistical test and interpret the results in a comment line. Part 3 A video game developer is testing a new game on three different segments. Each segment represents a different target market for the game (A: Duelists, B: Tacticians, or C: World Designers). The developer collects satisfaction scores and education levels from a random sample from each segment. The results are given in "Video_Game.csv". Are the scores among the segments different? 7) Which statistical test can help us answer this question? 8) Create a useful visualization or a summary of the data for this analysis. 9) Conduct the appropriate statistical test and interpret the results in a comment line.

# Part 1 ------------------------------------------------------------------

myDesigns <- read.csv("web_page_data.csv")

# 1) Which statistical test can tell us if the average session time in Design A and Design B are different?

# 2) Create a useful visualization or a summary of the data for this analysis.

# 3) Conduct the appropriate statistical test and interpret the results in a comment line.

# Part 2 ------------------------------------------------------------------

mySports <- read.csv("Fav_Sports.csv")

# 4) Which statistical test can help us answer this question?

# 5) Create a useful visualization or a summary of the data for this analysis.

# 6) Conduct the appropriate statistical test and interpret the results in a comment line.

# Part 3 ------------------------------------------------------------------

myScores <- read.csv("Video_Game.csv")

# 7) Which statistical test can help us answer this question?

# 8) Create a useful visualization or a summary of the data for this analysis.

# 9) Conduct the appropriate statistical test and interpret the results in a comment line.