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The role of descriptive and inferential statistics in hypothesis development and proper statistical test selection for evaluating the statistical results. It also discusses the use of tools to aid perception and provide ways to shed light, routes to understanding, instruments for monitoring and guiding, and systems to assist decision making. the difference between descriptive and inferential statistics and how they are used in data analysis. It also discusses the importance of sample size, mean, median, mode, variance, and range in descriptive statistics and confidence intervals and margin of error in inferential statistics.

Typology: Essays (university)

2022/2023

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Download Statistics for Managers: Descriptive and Inferential Statistics and more Essays (university) Financial Accounting in PDF only on Docsity! 1 STATISTICS FOR MANAGERS BUS 308 Statistics for Managers Ashford University Introduction Statistics can be an intimidating term for many of the older generation. The first thoughts that come to mind at the mention of statistics for many are mathematical expressions and formulas. One example of the powerful intimidation was presented by Dr. Collin’s in his week 4, lecture 2, “…..we need to calculate a t-value for each correlation, using the formula: t = r * sqrt(n-2)/sqrt(1-r^2), df = n-2;” (Statistics For Managers, 2019). WOW, that looks a little complex to the average manager. Technological advancements and the availability of computers have simplified the use of statistics and data analysis. Modern statistics reported by J. Hand, “the modern discipline is all about: the use of tools to aid perception and provide ways to shed light, routes to understanding, instruments for monitoring and guiding, and systems to assist decision making” (Hand D. J., 2008). This paper will examine the role of both descriptive and inferential statistics in hypothesis development and proper statistical test selection for evaluating the statistical results. Descriptive Statistics Descriptive statistics is the gathering, sorting, and summarizing of data. The first step is collecting a sample from a population. It is much quicker to take a random sample of the population than to test every individual within it. Descriptive statistics use the mean, median, and mode, to measure the sample. The mean is the average of the sample. The median is thevalue that occurs in the middle of the set. That leaves the mode which is the value that occurs or repeats the most in the sample. Variations and consistency are also important descriptive statistics. Variance and range are how consistency and variation are measured. Data with little variation allows the sample set to give a better representation of the population. This suggests that no matter how many different sample sets are taken the results will remain similar. The opposite would be a population with greater variations. In this case, the sample sets would not be a close representation of the whole; therefore, different samples would give varying results. The larger the sample size the better it will represent the population. Inferential Statistics Inferential statistics is another tool used in data analysis; unlike descriptive statistics, it lets one make predictions and draw conclusions about the population. Confidence intervals and the margin of error are two tools used to show probability and how sure one is of the conclusion. The descriptive statistics allow one to make inferences about the total population. To answer the question of equal pay for female workers; descriptive statistic allows us to compare both male and female salaries, years of service, and level of education. One could compare the midpoint for salary and see which is higher than the other. The mode would tell us the value that repeats the most. When comparing male and female modes we might find the female mode higher than the male. Descriptive statistics illustrate the sample being explored. Samples offer the opportunity to discover the variable distribution of populations without testing every individual within the population. The shape of the data gives a visual understanding or translation of its distribution within the sample. The empirical rule of probability implies that 95% of data observed in a normal distribution lies within two standard