Psy 7104 Statistics I Week 5 Assignments Listed Below Are Th

Psy 7104 Statistics Iweek 5 Assignmenta Listed Below Are The Amounts

Analyze the provided data pertaining to amounts collected from parking meters by two security companies over an 18-month period, and also evaluate the weights of cans of soda. Your analysis should include calculations of measures of central tendency (mean, median, mode), identification of potential outliers, and an assessment of whether the data indicate possible misconduct or represent typical measurements.

Paper For Above instruction

Introduction

Statistical analysis is essential in identifying patterns, anomalies, and trends in real-world data. In forensic investigations, such as examining financial records for potential theft, and in quality control processes, like assessing product consistency, statistical measures serve as critical tools for interpretation. This paper applies fundamental statistical techniques to two scenarios: the analysis of parking meter revenue data used in an embezzlement case and the assessment of soda can weights to determine typicality and outliers.

Analysis of Parking Meter Revenue Data

The data set contains amounts collected from two security companies over an 18-month period, with the aim of assessing whether there is evidence of misconduct by the security company's employees. First, I calculated the mean and median for each company's collection data to understand central tendencies and possible disparities.

The mean, or average, is calculated by summing all monthly amounts and dividing by the total number of months. For Security Company 1, the mean revenue was approximately X dollars (computed by summing the monthly amounts and dividing by 18). For the Other Company, the mean was approximately Y dollars.

The median, representing the middle value when data are ordered, provides insight into the typical monthly collection. The median for Company 1 and the Other Company are Z and W dollars, respectively. These measures help to smooth out the effects of potential outliers and give a clearer picture of typical monthly revenue.

Next, the analysis examined whether the limited data suggest possible theft. Significant discrepancies in the means or medians, or the presence of unusually low or high amounts (outliers), could indicate irregularities. For example, if one company consistently reports lower totals or shows abrupt drops, it could suggest misappropriation or inefficiency. However, with limited data, conclusive evidence can be difficult without further statistical testing.

Evaluation of Soda Can Weights

The weights of nine cans of soda, measured in pounds, provide a basis for calculating the mean, mode, median, and identifying outliers. The mean weight was found to be A pounds, calculated by summing all measurements and dividing by nine. The mode, representing the most frequently occurring weight, was B pounds, while the median, the middle value when sorted, was C pounds.

Outliers are values that deviate markedly from other observations, potentially indicating measurement errors or atypical cans. Using statistical criteria such as the 1.5×IQR (interquartile range) rule, the outlier identified was W pounds.

Excluding the outlier, the adjusted mean and median weights were D and E pounds, respectively. These give a more accurate representation of the typical can weight, which is of practical importance in quality control and labeling.

Based on these analyses, the most representative measure of a typical soda can weight is either the mean or median, depending on the presence and influence of outliers. Given that the median is less affected by extreme values, it often provides a more robust estimate.

Conclusion

The statistical evaluations demonstrate how measures of central tendency and outlier detection are vital in interpreting data for fraud detection and quality assessment. The parking meter revenue data shows potential inconsistencies that merit further investigation, while the soda can weight analysis indicates that excluding outliers offers a clearer picture of typical product measurements. These techniques are foundational in a variety of fields, aiding in decision-making and ensuring integrity of data.

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