Find The Standard Deviation And Other Statistical Measures

Find The Standard Deviation and Other Statistical Measures

Calculate the standard deviation of the given data set: $60, $58, $62, $67, $48, $51, $72, $70. Additionally, find the mean, median, and mode for the salary data, and the standard deviation for employee medical leave days. Lastly, compute the future value and compare simple and compound interest for a loan, and determine which investment yields more interest based on compound interest calculations.

Paper For Above instruction

Introduction

Statistical measures such as mean, median, mode, and standard deviation are fundamental tools in data analysis, providing insights into the distribution, variability, and central tendency of datasets. This paper covers diverse applications of these statistical tools, including calculating the standard deviation for product costs, analyzing salary data, employee leave days, and exploring future value and interest calculations for business decisions. These analyses not only offer quantitative insights but also support informed decision-making in business contexts.

Calculating Standard Deviation of Product Costs

Given data: \$60, \$58, \$62, \$67, \$48, \$51, \$72, \$70.

Step 1: Calculate the mean (average).

Mean = (Sum of all data points) / (Number of data points) = ($60 + $58 + $62 + $67 + $48 + $51 + $72 + $70) / 8

Sum = 60 + 58 + 62 + 67 + 48 + 51 + 72 + 70 = 488

Mean = 488 / 8 = \$61

Calculating (Data - Mean) and (Data - Mean)²

Data Data - Mean (Data - Mean)²
$60-11
$58-39
$6211
$67636
$48-13169
$51-10100
$7211121
$70981

Sum of squared deviations = 1 + 9 + 1 + 36 + 169 + 100 + 121 + 81 = 518

Variance = Sum of squared deviations / (Number of data points - 1) = 518 / 7 ≈ 73.86

Standard deviation = √Variance ≈ √73.86 ≈ 8.59

Analysis of Salary Data: Mean, Median, and Mode

Salaries: \$48,345; \$27,957; \$42,591; \$19,539; \$32,450; \$37,574.

Calculating the Mean

Sum of salaries = 48,345 + 27,957 + 42,591 + 19,539 + 32,450 + 37,574 = 208,986

Number of salaries = 6

Mean = 208,986 / 6 ≈ \$34,831

Calculating the Median

Sorted salaries: \$19,539; \$27,957; \$32,450; \$37,574; \$42,591; \$48,345

Since there are 6 data points, median = (3rd + 4th) / 2 = (\$32,450 + \$37,574) / 2 ≈ \$35,012

Mode

Each salary appears only once; hence, there is no mode in this dataset.

Standard Deviation of Employee Medical Leave Days

Data: 11, 2, 19, 3, 20, 11

Step 1: Calculate the mean.

Mean = (11 + 2 + 19 + 3 + 20 + 11) / 6 = 66 / 6 = 11

Step 2: Calculate squared deviations.

Data Data - Mean (Data - Mean)²
1100
2-981
19864
3-864
20981
1100

Sum of squared deviations = 0 + 81 + 64 + 64 + 81 + 0 = 290

Variance = 290 / (6 - 1) = 290 / 5 = 58

Standard deviation = √58 ≈ 7.62

Future Value and Interest Comparison

Loan amount: \$12,000; interest rate: 11% annually; period: 3 years.

Future Value with Compound Interest

FV = P × (1 + r)^t = 12,000 × (1 + 0.11)^3 = 12,000 × 1.11^3

Calculate 1.11^3 ≈ 1.11 × 1.11 × 1.11 ≈ 1.367

FV ≈ 12,000 × 1.367 ≈ \$16,404

Interest Paid (Compound)

Interest = FV - Principal = 16,404 - 12,000 = \$4,404

Simple Interest Calculation

Interest = P × r × t = 12,000 × 0.11 × 3 = \$3,960

Comparison: The compound interest (\$4,404) is higher than the simple interest (\$3,960), emphasizing the benefit of compounding.

Comparison of Investment Growth

Investment amount: \$1,000; duration: 1 year.

4% Compounded Quarterly

Interest rate per quarter = 4% / 4 = 1%

Number of quarters = 4

FV = P × (1 + r/n)^(nt) = 1,000 × (1 + 0.01)^4 ≈ 1,000 × 1.0406 ≈ \$1,040.60

4% Compounded Annually

FV = 1,000 × (1 + 0.04)^1 = 1,000 × 1.04 = \$1,040

Conclusion: The quarterly compounding yields slightly more interest (\$40.60 vs. \$40), demonstrating the effect of more frequent compounding periods.

Conclusion

The calculations demonstrated show how various statistical and financial formulas are applied in real-world business and economic scenarios. Understanding these measures allows businesses to assess variability, make informed salary and leave policies, analyze investments, and optimize financial decisions. Accurate computation of standard deviation helps in understanding data distribution, and financial calculations such as future value and interest comparisons support strategic financial planning.

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