5-3FinalProjectDataAnalysisMilestoneThree_ProcessandCalculations.docx

To what extent do the ages of MI patients vary by gender?

Maria Williams
Southern New Hampshire University

Biostatistics 21TW4

06/13/2021

Description Of the Statistics That Will Be Used to Answer the Health Question

Descriptive statistics are analyzing, synthesizing, and reporting information from a sample or complete population. The three main kinds of explanatory figures are Central Tendency Measures, Inferential Analysis, and Variation Measures. While descriptive statistics may give information about a data set, they could be used to conclude data collection and analysis as well as describe the relevant facts. Besides, descriptive statistics improve data visualization (Kaliyadan & Kulkarni, 2019). It allows data to be presented in a more meaningful and understandable way, providing a better comprehension of the data set at hand. Furthermore, descriptive statistics allow a data set to be summarized and presented in various ways, including tabular and graphical displays, as well as a commentary of the findings. Also, descriptive statistics are used to summarize complex quantitative data. The basic objective is to estimate population parameters in inferential statistics. The unknown values for the whole population, such as sampling distribution and standard deviation, are these parameters.
The independent t-test, also recognized as the two-sample t-test, student’s t-test, or independent-samples t-test, is a statistical inference test that determines if the means of two unrelated groups differ statistically substantially (Emerson, 2017). In a T-test, complete samples are simplified into a single t-value. Since t-values are unitless, without an extra context they might be difficult to comprehend. Therefore, t-distributions are utilized; they assume that the zero hypothesis is true of the sampling population and give a wider framework for determining the uniqueness of t-value.
On the other hand, side-by-side box charts are useful for comparing critical information about two data sets, such as the median values and the range of values represented by the data. Side-by-side box charts provide a concentrated overview and evaluation of data. On their own, boxplots can only deal with one quantitative variable. However, due to the 5-number data summary, a box track can manage a vast quantity of data and offer a summary. This is because a box plot comprises the median, indicating the middle point of the data set, the upper and lower quartiles, indicating the highest, lower, and minimum data quarters, and the upper and lower limits data values above and below. The organization of data in a plot of boxes utilizing five key principles is an effective technique for dealing with vast amounts of data that are too unmanageable for graphs like line plots or leaf and stem plots. The separate sample T-test is a statistical tool to test hypotheses that assesses if the mean of two independent samples is statistically significantly different.
A box painting is a highly visually efficient approach to see a concise overview of one or more data sets. It is especially handy for swiftly summarizing and comparing several sets of findings from various trials. On a look, a box plot allows the distribution of findings to be graphically displayed and offers symmetry hints within the data (Yu et al., 2019). However, the plot does not maintain the exact values and intricacies of the distribution outcomes, which in this case is a problem with processing such vast volumes of data. A box plot displays a brief overview of the distribution of the results to see the findings and compare them with other data quickly. It should be noted that one of the relatively few techniques of statistical graphs showing outliers is a box plot. There may be one outlier or several of them in a set of data, both below and above the lowest and maximum data values. Thus, the box graph outlines or obfuscates findings by stretching the lesser and larger data to a maximum of 1.5 times the interquartile range. Any data findings that go outside of the upper and lower bounds known as outliers may be easily identified on a diagram in a box.

References

Emerson, R. W. (2017). ANOVA and t-tests. Journal of Visual Impairment & Blindness, 111(2), 193-196.

Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.
Yu, X., Li, C., Chen, H., & Yu, X. (2019). Evaluate the effectiveness of multiobjective evolutionary algorithms by box plots and fuzzy TOPSIS. International Journal of Computational Intelligence Systems, 12(2), 733-743.