MHA FPX 5107 Assessment 1 Descriptive Statistics and Data Visualization

Descriptive Statistics and Data Visualization

It is essential to examine the drawbacks of a healthcare system. The overall performance of a healthcare facility is evaluated in this assessment using statistical analysis and visual aids. The information gathered includes readmission rates, patient satisfaction, and monthly healthcare service utilisation. After conducting the statistical analysis using the Microsoft Office Excel Analysis Tool Pack, the results are interpreted. Additionally, the evaluation addresses the consequences of various interpretations in light of the body of current scholarship.

MHA FPX 5107 Assessment 1

The results are evaluated once the statistical analysis is completed using the Microsoft Office Excel Analysis Tool Pack, providing information about the trends, patterns, and correlations between the variables. Making educated decisions and comprehending the underlying processes under study depend on this interpretation. The assessment also discusses the implications of different interpretations in view of the corpus of recent research.

This entails evaluating how the data might contradict or confirm accepted beliefs, comparing the results with past research, and pointing out any similarities or differences with current literature. Such an analysis identifies possible directions for future research and aids in placing the findings into the larger academic discourse. It also emphasises how crucial strict methodological techniques are to obtaining accurate and repeatable outcomes (Cooksey, 2020).

Descriptive Statistics Test

Descriptive statistical tests are used in healthcare data analysis to evaluate two important factors: data dispersion and central tendency. The mean, median, and mode have been selected for each of the three variables (readmissions, satisfaction, and utilisation) in order to assess central tendency. These metrics offer a basis for comprehending broad patterns in the dataset by shedding light on the typical or average value for every variable.

The degree of data dispersion, on the other hand, has been measured using variance and standard deviation, which aid in determining how dispersed the values are around the centre point. These tests determine whether the data points are widely scattered or closely grouped around the central value by assessing the variability. When combined, these statistical methods offer a thorough analysis of the dataset, pointing out trends and abnormalities (Data Visualization, 2024).

MHA FPX 5107 Assessment 1

These metrics provide a foundation for understanding general trends in the dataset by illuminating the average or typical value for each variable. For each important statistic, including patient satisfaction, utilisation, and readmission rates, measures of central tendency, such as the mean, median, and mode, offer important information about the overall trend or norm. Variance and standard deviation, on the other hand, have been used to quantify the degree of data dispersion.

These metrics help determine how widely distributed the values are around the central point. For instance, a low standard deviation would suggest that the values are more closely clustered around the mean, whereas a large standard deviation would signal that the data is significantly variable. These statistical methods work together to provide a more comprehensive insight (Thijmen van Alphen et al., 2022).