LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that business statistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptive statistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome numerical amount. According to “Dr. Ashram’s Statistics site” “The quantities most commonly used to measure the dispersion of the values about their mean are the variance and its square root, the standard deviations. The variance is calculated by determining the mean, subtracting it from each of the sample values (yielding the deviation of the samples), and then averaging the squares of these deviations.”
According to Tanner, D. E., & Youssef–Morgan, C. M. (2013), “When certain conditions prevail, the sample can be a mechanism for understanding the characteristics of the more-difficult-to-access population, which means that the descriptive statistics for samples can provide a window into the characteristics of the population. This is the domain of inferential statistical analysis.” I feel that although there are similarities between descriptive and inferential statistics. Inferential statistics are more based on the other differences within the p-value elements it implies that there are differences based on true values or the difference between those values. Inferential statistics is like the confidence interval, it being based on data collected and determining if it is true.
Moving onto Hypothesis development and Testing, hypothesis testing and development deals with the null and alternative hypothesis. When developing these hypothesis we calculated the average or mean and the standard deviation for elements. The hypothesis are developed by taking relationships between 2 variables and calculating the differences, the differences can be significant or not. The calculations are then evaluated to see if we reject or do not reject the null hypothesis. According to Tanner, D. E., & Youssef–Morgan, C. M. (2013), “the null hypothesis predicts that the result is not significant. It is the hypothesis of no difference, and it indicates that the mean of the population from which the sample was drawn (μ1) has the same value as the mean of the population to which it is compared (μ2). The null hypothesis is written this way: Ho: μ1 = μ2.” Statistics
“The alternate hypothesis predicts significance. The alternate hypothesis uses this form: HA: μ1 ≠ μ2.”
There are many statistical test associated with testing like: the t-test which calculates if a Mean sample if different from another, correlation - reflects the association between variables, Chi-square test – categorizes data that is broken down between expected and observed values, other test include: paired t-test, ANOVA, regression and the list goes on.
These test to me are a vital part of statistical calculations that although have different functions they work together as a team giving a manager financial...
References: Tanner, D. E., & Youssef–Morgan, C. M. (2013). Statistics for Managers. San Diego, CA: Bridgepoint Education, Inc.
http://home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm Statistical Thinking for Managerial Decisions
Zimmerman, D. W. (2012). Correcting Two-Sample "z" and "t" Tests for Correlation: An Alternative to One-Sample Tests on Difference Scores. Psychological: International Journal of Methodology and Experimental Psychology, 33(2), 391-418.
McHugh, M. L. (2013). The Chi-square test of independence. Biochemical Medical, 23(2), 143-149. doi:10.11613/BM.2013.018
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