The 9/11 Commission was formed in 2002 after the events of September 11, 2001 to investigate what really happened (Entman & Stonbely, 2018). It was headed by former New Jersey Governor Thomas Keen (Hughes, 2020). The 9/11 attack in America is a series of coordinated terrorist acts that took place on September 11, 2001 in New York and Washington (USA) (Entman & Stonbely, 2018). As a result of the terrorist attacks, 2,974 people were killed (not including terrorists), 24 were missing. Citizens of the United States and 91 other states were killed (Entman & Stonbely, 2018). In particular, the Commission’s 9/11 investigation was supposed to consider the circumstances surrounding the causes of the tragedy of that day. It was also stated that there was an obvious need to allocate additional funding to ensure the security of members of Congress.

The Commission’s report was published on July 22, 2004 (Norris, 2019). It announced that a series of terrorist attacks was conceived, prepared and carried out by the Al-Qaeda organization (Johnson, 2018). Mohammed Atta’s luggage was detained at Boston Logan Airport. Documents revealing the identities of all 19 terrorists and a detailed description of the planned attacks were found in it (Johnson, 2018). The Commission’s agency intercepted several messages pointing to Osama Bin Laden as the organizer of the terrorist attacks (Norris, 2019). The ideologist of the air attack on America is Khalid Sheikh Mohammed, who, in an interview with an Al Jazeera correspondent in September 2002, called himself the head of the Al Qaeda military council (Johnson, 2018). He was arrested in Pakistan on March 1, 2003, and fully admitted his guilt (Norris, 2019). The report of the 9/11 Commission states that Khalid Sheikh Mohammed’s hostility to America is caused by an aggressive rejection of the US foreign policy supporting Israel.

### References

Entman, R., & Stonbely, S. (2018). Blunders, scandals, and strategic communication in U.S. foreign policy: Benghazi vs. 9/11. *International Journal of Communication, 12*(28), 3024-3047.

Hughes, D. A. (2020). 9/11 truth and the silence of the IR discipline. *Alternatives: Global, Local, Political, 71*(4), 1-28.

Johnson, K. A. (2018). 9/11 and international student visa issuance. *Journal of Studies in International Education, 22*(5), 393-413.

Norris, J. J. (2019). Explaining the emergence of entrapment in post‑9/11 terrorism investigations. *Critical Criminology, 27*(16), 467-483.

## “The Tragedy Of The Commons” By Garret Hardin

### General Description

The tragedy of the commons is a term that is used to refer to a situation where people with excess means of production use it to their advantage and are depleting it. The theory explains the tendency of people to make a decision that favors personal situations without minding the negative impact they may have on others. For example, the partakers of a cup of coffee may not see any harm they are causing to the environment. However, the long-term cultivation of the coffee plants, which is a naturally occurring resource, has led to the loss of habitat endangering other plant species. Another example is the overfishing happening in the oceans and seas across the globe. The overhunting of fish has the potential to push other aquatic species into extinction. The population of Bluefin tuna in the Pacific Ocean has reduced to three percent of its original population because of the overfishing.

### Hardin’s Opinion

Hardin argues that the world’s resources are limited and people must assume to work and find a solution. However, he rejects the idea of colonizing other planets as an option for solving Earth’s problems. He added that Earth’s resources can support more people. Hardin states that there is a difference between achieving maximum and optimal population. Maximum means having as, many people as possible on Earth while optimal refers to a situation that maximizes the quality of life. The population problem has no technical solution in the form of science and technology. Hardin contends that the use of the above two methods will only worsen the situation. Therefore, he advocates for change in behavior and culture by people rather than looking for a technical solution. I think Hardin’s assertions are true, the problem of overpopulation and depletion of resources requires a social solution rather than a technical one.

### Explanation of the Essay

Garret Hardin wrote the essay, “The Tragedy of the Commons” where he stated that overpopulation is depleting the available natural resources. Hardin argues that without putting proper measures in place, humans are doomed. The essay challenges the faith people have placed on science in developing technical solution to the overpopulation problems. However, the stalemates presented by the interplay between overpopulation and technology is what Hardin calls the “Tragedy of Commons.”

## Statistical Study Of Alcoholism Among Students

### Introduction: Research Question

This research paper investigates the relationship between workday alcohol consumption and several characteristics of students’ social, economic, and academic status. In particular, a large set of gender, demographic, and family data allows several research questions to form.

- Does the number of absences from school affect workday alcohol use?
- Does a final semester grade (in math) affect workday alcohol use?
- Is there an increase in the combined effect of school absences and final semester grades on workday alcohol use?
- Is there a relationship between the frequency of meetings with friends and alcohol consumption during the workday?
- Is there a relationship between age and alcohol consumption during the workday?

### The Significance of this Question

It is not difficult to conclude that alcohol is a severe problem not only for the college community but for humanity as a whole. As a detrimental factor, alcohol mainly affects the physical and emotional health of the individual. The choice of the student community to study the connections described above is not accidental: in youth, alcohol is often synonymous with fun and partying. Large amounts of drinking on a regular basis are often perceived as a natural part of a student’s everyday life, but this behavior has destructive consequences. As a young student, they develop chronic alcoholism, the effects of which will tell on their well-being later in life — in addition, drinking while studying leads to a drop in academic performance and a decline in the student’s social skills (Flagel, 2021). In this regard, examining some of the patterns seems like a quite meaningful strategy and has academic relevance.

### Hypotheses

- The more often a student misses schoolwork, the more likely he or she is to consume alcohol during the workday.
- The higher the student’s final grade for the semester, the lower the student’s propensity to drink alcohol during the workday.
- Skipping classes and a higher final grade have no apparent change from the individual strength of each variable.
- The more often a student goes out with friends, the more likely they are to drink alcohol during the workday.
- There is no significant relationship between age and alcohol consumption.

### Data Description

In this paper, the student-mat data were obtained from publicly available sources. It is data collected from a large-scale survey of students who are enrolled in a high school mathematics course. The overall survey results are 33 variables, each describing a respondent’s social, economic, behavioral, or academic status. The selected data are the following set of variables:

- Dalc – workday alcohol consumption (numeric: from 1 – extremely low to 5 – extremely high).
- absences – number of school absences (numeric: from 0 to 93).
- G3 – final grade (numeric: from 0 to 20, output target).
- age – student’s age (numeric: from 15 to 22).
- goout – going out with friends (numeric: from 1 – exceptionally low to 5 – exceedingly high).

Thus, this paper uses five variables, of which Dalc is the dependent variable for all measurements, and the other variables are treated as influential parameters.

### Justification for the Method Used

All five variables were numeric and thus represented a value from a predetermined interval. Additionally, they were discrete variables. At the same time, an essential predictor for choosing a specific statistical testing methodology was the nature of the relationship of interest: the effect of one — or more — variables on the amount of alcohol consumed. Given the theoretical considerations learned in this course, the best solution for such a data set would be to use correlation analysis and linear regression. Recall, correlation determines the strength and direction of the relationship between two variables (Vedantu, 2020). In contrast, regression describes how a change in one variable affects a change in another variable. Regression and correlation are not interchangeable tests but using them together will produce exciting results for this paper. In addition, if the regression fits the data well (as determined by the coefficient of determination R^{2}), it becomes possible to use the equation to predict results for a particular measurement. A prediction is an essential tool in the academic setting, and for this reason, regression analysis is an integral component of this statistical study.

### Results

As general findings, five different correlations and regressions — depending on the purpose — were plotted according to each of the research questions. Each of the corresponding tables is listed below.

Model Summary – Dalc | ||||||||||||||||

Model | R | R² | Adjusted R² | RMSE | ||||||||||||

H₀ | 0.000 | 0.000 | 0.000 | 0.891 | ||||||||||||

H₁ | 0.112 | 0.013 | 0.010 | 0.886 | ||||||||||||

Coefficients | ||||||||||||||||

Model | Unstandardized | Standard Error | Standardized | t | p | |||||||||||

H₀ | (Intercept) | 1.481 | 0.045 | 33.045 | <.001 | |||||||||||

H₁ | (Intercept) | 1.410 | 0.055 | 25.728 | <.001 | |||||||||||

absences | 0.012 | 0.006 | 0.112 | 2.233 | 0.026 | |||||||||||

*Table 1. Linear regression test results for the relationship between alcohol use and school absenteeism.*

Model Summary – Dalc | ||||||||||||||||

Model | R | R² | Adjusted R² | RMSE | ||||||||||||

H₀ | 0.000 | 0.000 | 0.000 | 0.891 | ||||||||||||

H₁ | 0.055 | 0.003 | 0.000 | 0.891 | ||||||||||||

Coefficients | ||||||||||||||||

Model | Unstandardized | Standard Error | Standardized | t | p | |||||||||||

H₀ | (Intercept) | 1.481 | 0.045 | 33.045 | <.001 | |||||||||||

H₁ | (Intercept) | 1.592 | 0.111 | 14.288 | <.001 | |||||||||||

G3 | -0.011 | 0.010 | -0.055 | -1.085 | 0.278 | |||||||||||

*Table 2. Linear regression test results for the relationship between alcohol use and math final grades.*

Model Summary – Dalc | |||||||||

Model | R | R² | Adjusted R² | RMSE | |||||

H₀ | 0.000 | 0.000 | 0.000 | 0.891 | |||||

H₁ | 0.126 | 0.016 | 0.011 | 0.886 | |||||

*Table 3. Linear regression test results for the joint relationship between alcohol use and absenteeism in school and math final grades.*

Pearson’s Correlations | |||||||

Variable | goout | Dalc | |||||

1. goout | Pearson’s r | — | |||||

p-value | — | ||||||

2. Dalc | Pearson’s r | 0.267 | — | ||||

p-value | <.001 | — | |||||

*Table 4. Correlation analysis between alcohol consumption and meetings with friends.*

Pearson’s Correlations | |||||||

Variable | Dalc | age | |||||

1. Dalc | Pearson’s r | — | |||||

p-value | — | ||||||

2. age | Pearson’s r | 0.131 | — | ||||

p-value | 0.009 | — | |||||

*Table 5. Correlation analysis between alcohol consumption and age.*

### Interpretation of Results

### Alcohol Consumption and Absenteeism from Class

Table 1 shows that the correlation coefficient (R) between these variables is 0.112, indicating a weak positive relationship. In terms of the unstandardized beta coefficient, each 1 unit increase in school absenteeism resulted in a 0.012 unit increase in the frequency of alcohol use. The P-value for this beta was below the 0.05 level of significance, which means that the coefficient is statistically significant.

### Alcohol Use and Math Scores

Table 2 clearly shows that there is an inverse relationship between the variables. An increase in the semester grades each step led to a 0.011 decrease in the intensity of alcohol use at a p-value more significant than the critical level. Consequently, it can be concluded that there is no statistically significant relationship between these two variables.

### Co-influence on Alcohol Consumption

It was interesting to clarify whether there was a common effect. According to Table 3, the joint effect for alcohol consumption is weak: the R coefficient was 0.126. It is already evident that the main contribution was from absenteeism since the math was shown not to present statistically significant patterns for alcohol.

### Correlation of Alcohol use and Meetings with Friends

The correlation analysis shown in Table 4 shows a correlation coefficient between the variables of 0.267 with a statistically significant result (p-value<0.001). This is a weak relationship, showing that the two variables were virtually unrelated.

### Relationship of Alcohol use and Age

Table 5 also shows that there was not even a moderate relationship between the student’s age and alcohol use: the r coefficient was 0.131. This is a critically low level of correlation, showing that age cannot directly influence the frequency of alcohol use.

### Conclusion

The statistical analysis conducted with respect to high school students’ alcohol use suggests several important conclusions. First, increasing absences had virtually no effect on increasing alcohol consumption in any of the parameters studied. The most significant effects were found for frequency of meetings with friends (r=0.267), age (r=0.131), and absenteeism (r=0.112). Second, increases in final math grades showed no statistically significant relationship with increases in alcohol use in high school.

The results obtained may be helpful for school principals, parents, and social industry workers. It was shown that neither meetings with friends, age, nor school truancy were reliable predictors of alcohol use, which means that restricting any of these factors — as a punishment for the student, for instance — would not have the expected result. It also means that the cause of alcohol use is more profound, which means that one needs to continue to look for critical predictors to improve the academic agenda in high schools.

### References

Flagel, J. 2021. *Who alcohol use disorder affects — college students*. ARH.

Vedantu. (2020). *Difference between correlation and regression*. Vedantu.