Assignment 1 Cost Accounting Last Date For Submission 15 Oct ✓ Solved

Assignment 1 Cost Accountinglast Date For Submission 15 October 2017yo

Summarize the key differences between operating plans and actual operations. Discuss techniques or strategies for making higher-quality decisions. Calculate the total monthly costs and average cost per mobile phone for Omar Corporation, given variable costs of $20 per mobile, a factory rent of $2,500 per month, and different production quantities (10 and 100 mobiles). Identify statistical tools that highlight the significance of the regression model in estimating cost behavior. Explain how uncertainties and biases can influence decision quality. Evaluate pseudocode for calculating BMI with provided test values and interpret its outputs. Write an algorithm to find the perimeter and area of a rectangle. Identify errors in given programming statements, explain why they are errors, and suggest solutions. Design a Clock class with a constructor that correctly initializes hour, minute, and second properties. Describe the implicit and explicit parameters in specific method calls. Implement body definitions for addition, subtraction, multiplication, and division methods in a Calculator class written in Java.

Sample Paper For Above instruction

The distinction between operating plans and actual operations is fundamental to effective management accounting. Operating plans serve as premeditated strategies and benchmarks set by management to guide organizational activities, resource allocations, and performance expectations over a specific period (Drury, 2013). These plans include detailed forecasts of sales, production, costs, and profit margins. Actual operations, however, represent the real-time activities and outcomes that occur during the execution of these plans. Deviations between planned and actual results are inevitable and can arise from various internal and external factors such as market fluctuations, supply chain disruptions, or managerial inefficiencies.

Understanding and analyzing these deviations allows managers to refine their decision-making processes. Techniques like variance analysis enable organizations to compare actual results against planned expectations, identify the sources of variances, and determine their significance (Hilton & Platt, 2013). Such insights facilitate corrective actions, resource reallocation, and strategic adjustments to improve overall operational efficiency. Additionally, tools such as dashboards and performance scorecards enhance real-time decision-making by providing visual representations of operational metrics against targets.

Decision-making quality is enhanced through systematic data collection, thorough analysis, and the application of appropriate decision models. Higher-quality decisions are characterized by their alignment with organizational goals, economic efficiency, and predictive accuracy (Simon, 2019). Quantitative methods like cost-volume-profit analysis, break-even analysis, and sensitivity analysis help managers evaluate different scenarios, assess risks, and select optimal courses of action. Moreover, leveraging big data analytics and machine learning algorithms can uncover patterns and insights not readily apparent through traditional methods, thus enabling more informed and strategic decisions (Brynjolfsson & McAfee, 2014).

In the context of Omar Corporation, understanding the complete cost structure is vital. The total monthly costs include fixed costs, such as rent, and variable costs, which depend on production volume. For instance, with a fixed rent of $2,500 and variable costs of $20 per mobile phone:

  • Producing 10 mobiles per month:

Total variable costs = 10 mobiles × $20 = $200

Total costs = Fixed costs + Variable costs = $2,500 + $200 = $2,700

Average cost per mobile = Total costs / Number of mobiles = $2,700 / 10 = $270

  • Producing 100 mobiles per month:

Total variable costs = 100 mobiles × $20 = $2,000

Total costs = $2,500 + $2,000 = $4,500

Average cost per mobile = $4,500 / 100 = $45

Regarding statistical tools for regression analysis, key metrics include R-squared, which indicates the proportion of variance in the dependent variable explained by the model, and standard error, which measures the average distance that the observed values fall from the regression line (Montgomery et al., 2012). F-tests assess the overall significance of the regression model, while t-tests evaluate the significance of individual predictors. These tools collectively confirm the reliability of the regression model in estimating cost behavior.

The influence of uncertainties and biases on decision quality is profound. Uncertainties stem from incomplete or imperfect information about future events, leading to probabilistic risks that can skew evaluations. Biases, such as confirmation bias or overconfidence, distort judgment and may cause managers to overlook critical data or favor certain outcomes (Kahneman, 2011). To mitigate these effects, organizations adopt risk management strategies, scenario planning, and critical thinking practices. Incorporating multiple perspectives and rigorous data analysis helps reduce biases, ultimately leading to superior decision-making outcomes.

In assessing pseudocode for calculating BMI with provided values:

  • For height 1.80 meters and weight 78 Kg:

BMI = 78 / (1.80)^2 ≈ 78 / 3.24 ≈ 24.07, which falls within the "Normal" range (18.5 - 24.9).

  • For height 1.50 meters and weight 85 Kg:

BMI = 85 / (1.50)^2 ≈ 85 / 2.25 ≈ 37.78, indicating "Overweight" (above 25).

The pseudocode effectively categorizes BMI levels based on the computed values, enabling health assessments.

Creating an algorithm to find the perimeter and area of a rectangle involves defining inputs for length and width, then applying simple formulas:

  • Perimeter = 2 (length + width)
  • Area = length × width

Implementation in pseudocode:

Input length


Input width


Perimeter = 2 * (length + width)


Area = length * width


Output Perimeter


Output Area

Common errors in programming statements include assignment versus comparison errors, mismatched data types, and improper object creation. Examples include:

  • a. 23 = 25; (Error: cannot assign to a literal, should be a comparison: 23 == 25)
  • b. Int percentage% = “15”; (Error: variable name cannot contain '%' and assigning string to int; should be: int percentage = 15;)
  • c. Rectangle void = new Rectangle(25, 30, 40, 50); (Error: incorrect variable name and parameters; should be something like Rectangle rect = new Rectangle(25, 30);)
  • d. double 7average = 0; 7average.length(); (Error: variable name cannot start with a digit; correct: double average = 0;)

The Clock class should have a constructor that initializes the hour, minutes, and seconds. The correct constructor body is:

  • Option D: hour = hr; minutes = min; seconds = sec;

In method calls, the implicit parameter is the object immediately before the dot (.), while the explicit parameter(s) are the arguments passed within parentheses. For example:

  • A) System.out.println("Assignment 1"); – No parameters, so none explicitly or implicitly.
  • B) myName.length(); – ‘myName’ is the object, and ‘length’ is a method with no explicit parameters, using the implicit parameter 'myName.'

In Java, implementing arithmetic operation methods in a Calculator class involves defining:

```java

public int addition(int x, int y) {

return x + y;

}

public int subtraction(int x, int y) {

return x - y;

}

public int multiplication(int x, int y) {

return x * y;

}

public double division(int x, int y) {

if (y == 0) {

throw new ArithmeticException("Cannot divide by zero");

}

return (double) x / y;

}

```

These methods perform the respective arithmetic operations based on provided inputs, facilitating calculations within Java applications.

References

  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  • Drury, C. (2013). Management and Cost Accounting. Springer.
  • Hilton, R. W., & Platt, D. E. (2013). Managerial accounting: Creating value in a dynamic business environment. McGraw-Hill Education.
  • Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
  • Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis. Wiley.
  • Simon, H. A. (2019). Administrative Behavior: A Study of Decision-making Processes in Administrative Organizations. Free Press.

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