MGM330-0704A-04 Business Decision-Making - Phase 3 Discussion Board - Essay Example

There are various statistical models, methods and techniques that allow statisticians, managers, scientist and other professionals to perform quantitative analysis that provide insight about different sets of data. The results or outputs of the statistical methods are great tools that allow people to make decision supported by a scientifically proven statistical analysis. Two methods that are popular because of their usefulness and practical applications that are tremendous decision making tools are hypothesis testing and regression analysis.

This short discussion describes these two methods. Hypothesis testing is a statistical method can be utilized in a variety of applications. The method basically allows a researcher to make a claim about a particular population (data) called the null hypothesis which is then tested by this method to determine if the claim was true or false (Levine & Stephan & Krehbel & Berenson, 2006, p.332). The test works by providing a null hypothesis and an alternative hypothesis. If the null hypothesis is either accepted or rejected.

If the null hypothesis is rejected that means there is sufficient evidence to validate the alternative hypothesis is true. The normal curve and its corresponding normal distribution table which is associated with Z values is an important variable in the process of a hypothesis test. Hypothesis testing follow a particular algorithm or procedure that must be perform every time the test is perform. The key steps of a hypothesis test are:State null hypothesisState alternative hypothesisChoose level of significanceChoose sample sizeChoose statistical technique (Z test is the most common, but there other alternatives)Arrange the critical values that divide the rejection and non rejection regionsCompute value of test statisticDetermine if the value calculated falls in rejection or non rejection regionMake statistical decision about the resultsExpress verbally what the results mean(Levine, et. al., 2006, p.339).Another powerful and very useful statistical method is regression analysis.

The regression analysis method is a predictive model that allows the statistician to determine the values of a variable within an equation based on the given data about the other variable. This method has very valuable application for forecasting. A common technique of this model is simple regression which can be applied utilizing the least square approach. This approach uses the formula Y = aX + b (McClave & Benson & Sinich, 2001, p.460). The two key variables are called the dependent and the independent variables.

An example of an application of this model is a production problem. A company can used this equation to predict many sales it can generate in the future based on past sales totals of the past years. Software such as Microsoft Excel, Minitab or graphical calculators such as the Ti-89 Titanium created by Texas Instrument can be utilized to perform linear regression analysis. There is another type of regression analysis called multiple regressions. A multiple regression analysis follows the same basic principles as simple regression but instead of two variables there are multiple variables involved in the analysis.

ReferencesLevine, D., Stephan, D., Krehbiel, T., Berenson, M. (2006). Statistics for Managers: Using Microsoft Excel (4th edition). India: Prentice HallMcClave, J., Benson, G., Sincich, T. (2001). Statistics for Business and Managers (8th ed.). New Jersey: Prentice Hall.