BUS 308 Statistics For Managers At Ashford University

Today in order to advance the quality of operations or functionality, statistical techniques are used for problem solving that brings a cutting edge to an organization designing process. It is mathematical knowledge acquired using four different data types with descriptive and inferential statistics. Determining the appropriate test, hypothesizing, and assessing the results creates opportunity cost for sound decisions that aid developments to most operations. Therefore, having a solid understanding to statics is very important. The course “BUS 308 Statistics for Managers” at Ashford University provided a learning process of obtaining knowledge to descriptive statistics,inferential statistics, selection the appropriate statistical test,

The two branches of statics is descriptive and inferential analysis (Tanner & Youssef-Morgan, 2013). Descriptive statistics is the summarization of data from a sample using graph, charts, and numeric to display results (Tanner & Youssef-Morgan). The counts and measurements of descriptive statistics is either central tendency or measured variability. Central tendency is the calculated mean, median, and mode to a data set. The measured variability can be the calculated standard deviation, a minimum and maximum of variables, kurtosis,and skewness. The relationship of variables is counted and measured as pair variables in the following test: contingency table, cross-tabulation, scatter plots, conditional distributions, quantitative dependence, and histograms. On the other hand, inferential analysis is the random data used from a population for an analysis regarding that population. A inferential analysis is used with large population due to the lack of ability to analysis the population as a whole (Tanner & Youssef-Morgan, 2013). Inferential statistics is based on probability (Tanner & Youssef-Morgan, 2013). A sample mean is draw with population parameters to the area of interest. The counts and measurements is the calculated difference among samples that have probability of being part of a population. There are many test of inferential statistics like t-test, ANOVA, regression,

The appropriate test is selected by identifying the purpose of the test, what data is used, how many samples, level of measurement, and if there is a hypothesis. Looking for a relationship, comparing data, or testing a hypothesis are what a statistical test can answer at different level of measurements and counts based on a researcher’s needs. The purpose of a test will provide direction to what type of population parameter is selected. The population parameter controls the level of measurement in one or more variables of a sample mean or population mean. A sample mean can have one or more dependent or independent variables. The level of measurement is the degree of research needed to fulfill the purpose of a test that will satisfy an assumption to a question for decision on a sample mean or population mean. The data known as calculated variables can be nominal, ordinal, interval, and rations depending on the area of interest within a population (Tanner & Youssef-Morgan, 2013). Nominal data is categorical, qualitative, and nonparametric which is measured by percentage, proportion, or frequency. Examples of testing using nominal data are test of proportion, difference of two proportions, and chi-square test for independence. Interval and ratio data is quantitative and parametric. Interval and ratio is commonly tested for the mean in tests like test for a mean, difference of two means,