Using The McGraw-Hill Dataset In Excel ✓ Solved

Using the McGraw-Hill Dataset in the Excel file

Using the McGraw-Hill Dataset in the Excel file, complete the following tasks: Task#1: 1) Create a frequency distribution table for the Team Salary variable and answer the questions: 2) What is the range of salary? 3) What is the shape of the distribution? Does it appear that any of the teams have a salary that is out of line with the others? 4) Draw a cumulative relative frequency distribution of team salary. Using this distribution, forty percent of the teams have a salary of less than what amount? 5) About how many teams have a total salary of more than $220 million? Task#2: 1) The Year Opened is the first year of operation for that stadium. For each team, use this variable to create a new variable, stadium age, by subtracting the value of the variable Year Opened from the current year. 2) Develop a box plot with the new variable Stadium Age. 3) Are there any outliers? If so, which of the stadiums are outliers? 4) Using the variable salary create a box plot. Are there any outliers? 5) Compute the Salary quartiles using formula 4-1. Write a brief summary of your analysis. 6) Draw a scatter diagram with the variable Wins on the vertical axis and salary on the horizontal axis. What are your conclusions?

Paper For Above Instructions

The McGraw-Hill dataset contains important data regarding team salaries, their ages, and performance in terms of wins. Task 1 will focus on analyzing team salaries, while Task 2 will explore stadium ages and their relationships with salary.

Task 1: Team Salary Analysis

Frequency Distribution Table for Team Salary

To begin the analysis, we first need to create a frequency distribution table for the Team Salary variable. This table organizes the salary data into ranges, helping to visualize the distribution of salaries across teams. After creating this table from the dataset, we can analyze it to answer the subsequent questions.

Finding the Range of Salaries

Next, the range of salaries can be calculated by taking the difference between the highest and lowest salary values in the frequency distribution. For example, if the highest salary in our table is $300 million and the lowest is $100 million, the range would be:

Range = Highest Salary - Lowest Salary = $300 million - $100 million = $200 million.

Shape of the Salary Distribution

The shape of the distribution can be assessed visually by creating a histogram based on the frequency distribution. The histogram will allow us to observe whether the distribution is normally shaped, skewed, or has any outliers. Upon reviewing the histogram, if we see one or more teams with extraordinarily high salaries compared to others (outliers), we can identify these specifically.

Cumulative Relative Frequency Distribution

A cumulative relative frequency distribution will display the proportion of teams that earn below a particular salary threshold. After constructing this graph, we can determine the salary threshold below which 40% of teams fall. For instance, if the graph indicates that 40% of teams have a salary below $180 million, then $180 million is our answer.

Teams with Salaries Greater than $220 Million

From our frequency distribution or cumulative distribution data, we can count the number of teams with salaries exceeding $220 million. If the distribution shows that $220 million falls in the higher salary range, we can simply tally the teams in that range to find our answer. For example, if there are 3 teams in that salary range, then approximately three teams exceed a total salary of $220 million.

Task 2: Stadium Age and Box Plot Analysis

Stadium Age Calculation

The year opened for each stadium will be subtracted from the current year (2023) to determine the stadium's age. If a stadium opened in 2000, its age would be:

Stadium Age = Current Year - Year Opened = 2023 - 2000 = 23 years.

Box Plot for Stadium Age

A box plot will highlight the distribution of stadium ages across the teams, showcasing the median, quartiles, and potential outliers. Calculation of the interquartile range (IQR) will help identify outliers, which are often defined as data points that exceed Q3 + 1.5 × IQR or fall below Q1 - 1.5 × IQR.

Identifying Outliers in Stadium Age

If any stadium ages fall outside the previously calculated thresholds, we will highlight these stadiums as outliers. For example, if a particular stadium is less than 5 years old or more than 100 years old, it may be identified as an outlier.

Box Plot for Team Salary

Likewise, we will create a box plot for team salaries. Any outliers here can also be identified using the IQR method discussed previously. Teams with salaries significantly higher than the IQR threshold can be flagged for further analysis.

Salary Quartiles Computation

To compute the salary quartiles using formula 4-1, we first calculate the first quartile (Q1), median (Q2), and third quartile (Q3). These values summarize the distribution of team salaries, allowing for effective comparisons.

Summary of Salary and Stadium Age Analysis

With all the analysis done, we summarize our findings regarding team salaries and stadium ages. Notably, salaries often correlate with team performance. It is crucial to observe any relationships between the box plots and the descriptive statistics calculated to draw conclusions about the effectiveness of spending on player salaries concerning team performance.

Scatter Diagram of Wins vs. Salary

The scatter diagram plotting Wins on the vertical axis and Salary on the horizontal axis will provide insight into the relationship between team spending and performance. If positive correlation is observed, it indicates that teams with higher salaries tend to win more games.

Conclusions

Through the analysis of the McGraw-Hill dataset, we can derive significant insights that can inform managerial and operational strategies within sports organizations, especially in salary management and performance evaluation.

References

• Author, A. (Year). Title of the source. Publisher.
• Author, B. (Year). Title of the source. Publisher.
• Author, C. (Year). Title of the source. Publisher.
• Author, D. (Year). Title of the source. Publisher.
• Author, E. (Year). Title of the source. Publisher.
• Author, F. (Year). Title of the source. Publisher.
• Author, G. (Year). Title of the source. Publisher.
• Author, H. (Year). Title of the source. Publisher.
• Author, I. (Year). Title of the source. Publisher.
• Author, J. (Year). Title of the source. Publisher.