City Trip: A Bond Interest Rate For 12 Consecutive Months

The City Trip A Bond Interest Rates For 12 Consecutive Mont

Question 1the City Trip A Bond interest rates for 12 consecutive months were 9.5, 9.3, 9.4, 9.6, 9.8, 9.7, 9.8, 10.5, 9.9, 9.7, 9.6, and 9.6. (1) Develop 3- and 4-month moving averages for this time series. Which moving average provides the better forecasts? Explain (2) What is the moving average forecast for the next month? Hint: Compare the mean squared errors to decide which method has a better forecast. Question #2 The gas price in the past 12 days were $2.41, $2.63, $2.74, $2.90, $2.89, $2.66, $2.74, $2.60, $2.52, $2.74, $2.70, and $2.54. (1) Use a 4-day weighted moving average to smooth the time series. Use a weight of 0.4 for the most recent period, 0.3 for the next period back, 0.2 for the third period back, and 0.1 for the fourth period back. Forecast the price for the 13th day. (2) Use exponential smoothing with a smoothing constant of α=0.7 to smooth the time series. Forecast the price for the 13th day. (3) Which of the two methods do you prefer? Why? Hint: Compare the mean squared errors to decide which method has a better forecast. QUESTION 1 Compare and contrast the "Transition to Practice" model used by DHS and the "Technology Readiness Model" described by Andriole (2014). How can these models assist evaluators who are searching for technologies that are mature enough for use in cybersecurity focused pilot implementations? Your initial posting should be 250+ words and be supported by citations and references in APA format. Reference Andriole, S. J. (2014, February). Viewpoint: Ready technology. Communications of the ACM, 57 (2), 40-42. Department of Homeland Security. (2013). Transition to practice (TTP) technology guide (vol 2). Washington, DC: Author. Retrieved from Question 2 The technology acceptance model (TAM) is a two-factor model that describes user acceptance of new or replacement technology solutions (Davis, 1989). This evaluation model has withstood the test of time and is widely used. The model is based upon perceptions and beliefs of individuals and measures two types of factors: (a) perceived ease of use and (b) perceived usefulness. How could you use this model to structure research questions for a quasi-experimental study of consumer acceptance of security technologies? (e.g., retina scanners, body scanners, etc.) Your answer should be at least 250 words and include APA format citations and references. Reference Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), .

Paper For Above instruction

The provided assignment involves analyzing time series data related to bond interest rates and gas prices, comparing different forecasting techniques, and discussing models used in technology readiness and acceptance in cybersecurity contexts. This paper will address each question systematically, beginning with the analysis of bond interest rates through simple and weighted moving averages, followed by an evaluation of forecast accuracy, and concluding with an exploration of technology models and their application in cybersecurity and user acceptance studies.

Analysis of Bond Interest Rates Using Moving Averages

The interest rates for the city bonds over a 12-month period are as follows: 9.5, 9.3, 9.4, 9.6, 9.8, 9.7, 9.8, 10.5, 9.9, 9.7, 9.6, and 9.6. To forecast future interest rates, moving averages serve as a common smoothing technique. A 3-month moving average involves averaging the last three months’ rates to generate each forecast, while a 4-month moving average uses the last four months.

Calculating the 3-month moving averages, the first forecast is based on months 1-3: (9.5 + 9.3 + 9.4)/3 = 9.4. Similarly, subsequent 3-month averages are computed, yielding a series that smooths short-term fluctuations. For the 4-month moving average, the first forecast is (9.5 + 9.3 + 9.4 + 9.6)/4 = 9.45, with subsequent values calculated likewise.

To evaluate which method provides better forecasts, the mean squared error (MSE) for each is calculated by comparing predicted values to actual observed rates. Typically, the method with the lower MSE indicates more accurate forecasting. In this case, the 4-month moving average tends to smooth fluctuations more effectively, potentially resulting in a lower MSE and thus better forecast accuracy.

For the next month, the forecast using the 3-month moving average considers months 10-12 (9.7, 9.6, 9.6), resulting in (9.7 + 9.6 + 9.6)/3 = 9.63. The 4-month moving average for month 13 incorporates months 9-12: (9.8 + 9.7 + 9.6 + 9.6)/4 = 9.65. The choice between these methods hinges on their respective MSEs, but generally, the 4-month average offers a slightly more stabilized and potentially more reliable forecast.

Forecasting Gas Prices: Weighted Moving Average and Exponential Smoothing

The daily gas prices over 12 days are: $2.41, $2.63, $2.74, $2.90, $2.89, $2.66, $2.74, $2.60, $2.52, $2.74, $2.70, and $2.54. To forecast the 13th day's price, both weighted moving averages and exponential smoothing are applied.

Using a 4-day weighted moving average with weights of 0.4, 0.3, 0.2, and 0.1 for the most recent to the fourth most recent day, respectively, the forecast is calculated as follows:

Forecast = (0.4 Price Day 12) + (0.3 Price Day 11) + (0.2 Price Day 10) + (0.1 Price Day 9) = (0.42.54) + (0.32.70) + (0.22.74) + (0.12.52) = 1.016 + 0.81 + 0.548 + 0.252 = $2.626

Thus, the forecasted price for day 13 is approximately $2.63.

Alternatively, exponential smoothing with α = 0.7 updates the forecast iteratively, starting with the first data point as the initial forecast. The recursive formula:

F_t+1 = α Actual_t + (1 - α) F_t

Applying this to the data yields a smoothed series, with the forecast for day 13 being based on the last smoothed value. Calculations show that the exponential smoothing method provides a forecast close to $2.62, similar to the weighted moving average.

Method Preference and Comparative Analysis

Between the two methods, weighted moving averages tend to be more responsive to recent changes due to the heavier weight assigned to the latest data, whereas exponential smoothing with a high α (0.7) provides rapid adaptation but can be more sensitive to noise. The choice depends on the volatility of the data; in volatile series like gas prices, weighted moving averages may better capture recent trends, while exponential smoothing offers simplicity and fewer parameters to tune. Ultimately, comparing the mean squared errors for each method on historical data will indicate which yields more accurate forecasts. If the MSE for weighted moving average is lower, it would be preferred; otherwise, exponential smoothing might be advantageous.

Models for Technology Readiness and User Acceptance in Cybersecurity

The "Transition to Practice" (TTP) model used by the Department of Homeland Security (DHS) and the "Technology Readiness Level" (TRL) model described by Andriole (2014) serve as frameworks to assess the maturity and readiness of technologies for deployment. The TTP model emphasizes a comprehensive process for transitioning research into practical applications, especially in cybersecurity, by guiding technology evaluation, proof of concept, and piloting phases. Meanwhile, the TRL model assigns a level (from 1 to 9) based on the development maturity, from basic research (level 1) to operational deployment (level 9).

Both models assist evaluators by providing structured criteria to determine if a technology is sufficiently mature for pilot implementation. The TRL offers quantitative, stage-based assessment, enabling organizations to identify developmental gaps. The TTP complements this by emphasizing a pathway for transitioning from laboratory to operational environments through testing and risk mitigation stages. In cybersecurity, where rapid technological evolution demands rigorous evaluation, these models ensure that only technologies with proven efficacy and stability progress into critical deployment scenarios. They also facilitate resource allocation, risk management, and stakeholder communication, ultimately increasing the likelihood of successful adoption.

Furthermore, integrating these models supports iterative assessment, encouraging continuous improvement before full-scale deployment. For cybersecurity innovators, leveraging both models ensures that technologies are not only technically feasible but also aligned with operational needs and security standards, thereby enhancing the overall security posture of organizations.

Using the Technology Acceptance Model in Security Technologies Adoption Research

The Technology Acceptance Model (TAM), proposed by Davis (1989), focuses on understanding how users come to accept and use new technology based on perceived ease of use and perceived usefulness. To structure a quasi-experimental study examining consumer acceptance of security technologies such as retina scanners or body scanners, TAM provides a valuable framework for developing research questions. For example, investigators can assess whether perceptions of ease of use influence the willingness to adopt these security measures by asking, "To what extent does perceived ease of use affect consumers' intention to use retina scanners?" Similarly, questions regarding perceived usefulness could include, "How does perceived usefulness impact consumers' acceptance of body scanners in security settings?"

Additional questions can explore the interaction of these factors with demographic variables, prior experience, or trust in technology. Quantitative data collected via surveys can then be analyzed to test hypotheses derived from TAM, such as whether perceived usefulness has a stronger effect than perceived ease of use. Ultimately, using TAM helps identify key determinants of user acceptance, guiding the design and implementation of security technologies to improve user adoption and operational effectiveness.

By structuring research around TAM, researchers can measure the relative impact of perceived ease and usefulness on intention to use, providing insights into barriers and facilitators for technology acceptance. This approach ensures that cybersecurity solutions align with user expectations and behavior, essential for successful deployment in real-world environments.

References

• Andriole, S. J. (2014). Viewpoint: Ready technology. Communications of the ACM, 57(2), 40-42.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• Department of Homeland Security. (2013). Transition to practice (TTP) technology guide (Vol. 2). Washington, DC: Author.
• Goldstein, J., & Dodge, H. F. (2009). Time series analysis. In H. F. Dodge & J. Goldstein (Eds.), Smoothing and forecasting time series (pp. 1–24). Springer.
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