Paper Example. Regression Analysis Discussion Post

I am interested in option one a problem that deals with two possible related variables (Y and X). Let y be the total number of COVID-19 cases in a country and x the travel restriction by country. I am interested in assessing whether travel restrictions were effective in reducing the spread of COVID-19.

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The problem is that the spread of COVID has been continuing and posing a big threat to people’s health. Centers for Disease Control and Prevention (CDC) made recommendations for travel restrictions, particularly air travel, to reduce the spread. Therefore, it is important to investigate whether travel restrictions have been effective or not. The regression model is the most appropriate method for solving this problem because the response variable is continuous, while the independent variable is ordinal (Harrell, 2010).

Specifically, the statistical question that I am asking is;

Do travel restrictions reduce the spread of COVID-19?

Through the regression analysis, I will be able to tell the direction and magnitude of the effect of travel restrictions on the spread of COVID-19. The regression model can also be used to predict the number of COVID-19 cases for a country with a certain level of travel restrictions. I would want to predict the number of COVID-19 cases to warn governments of increased risk of the disease if they failed to restrict their people’s movements. However, with the regression model, I am only able to predict the number of COVID-19 cases for a country with travel restriction levels captured in the CDC classification.

I will gather secondary data from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC). The travel restrictions will be sourced from the CDC official website and will be measured using a scale of 0-3 with values 0, 1, 2, 3 representing no restrictions, reopening soon, partially open, and completely closed, respectively. On the other hand, the total number of COVID-19 cases will be sourced from the WHO website.

Classmate 1 Response

This is a very great post. The problem is very interesting, especially to owners of the coffee shops who would wish to prepare in advance before the customers arrive. I see this as a very genuine and valid problem. Also, the temperature can influence the number of hot beverages sold. Hence, a regression model is very appropriate since it will reveal both the magnitude and direction of the effect.

The estimate of interest is the coefficient of temperature (gradient or slope of the regression equation). The standard error of the estimate is a measure of the accuracy of the prediction. It is obtained by taking the square root of the average square deviations. A small value of standard error indicates that the prediction is more accurate and vice versa.

Classmate 2 Response

This is a very good post. The problem discussed in this post is valid. Ideally, the number of hours of cardio exercise per week for a man influences body fat. However, it is not possible to tell the magnitude and direction of the influence. The regression analysis method presents an appropriate approach to solving the problem since it can reveal both the direction and magnitude of the influence of exercise on body fat.

The estimate of interest is the coefficient of temperature (gradient or slope of the regression equation). The standard error of the estimate is a measure of the accuracy of the prediction. It is obtained by taking the square root of the average square deviations. A small value of standard error indicates that the prediction is more accurate and vice versa.

References

Harrell, F. E. (2010). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer.