Process
DQ1. What is the value of performing hypotheses tests to solve problems related to business and operations management? Provide specific examples.
“We define two hypotheses: (1) The null hypothesis is that which represents the status quo to the party performing the sampling experiment-the hypothesis that will be accepted unless the data provide convincing evidence that it is false. (2) The alternative, or research, hypothesis is that which will be accepted only if the data provide convincing evidence of its truth. Hypotheses test are extremely helpful to businesses and operations because it helps to determine if a variable differs in a particular value or if it has a systematic differ or even sampling error” (McClave, Benson, & Sincich, 2011). Hypotheses test help to solve problems because is sets a level of certainty. An example would be when a piece of equipment on a production line needs to be repaired. The expected value of the number of movements it is to make in an hour would be the relevant variable, the actual values can be sampled, the information can then be used for the mean, the standard deviation and sample size will determine the probability of differences. If the probability is higher than the threshold that is set then action will need to be taken.
References
McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Prentice Hall.
What is the purpose of using non-parametric tests in operations management decisions? Provide examples of what you look for when conducting these tests.
Proper units occur when a statistical variable understudy can signify a ratio or an interval scale of measurement. When an assumption is made on the variability of the distribution, the variable is fit to be tested parametrically. Some parametric testing methods are t-test and z-test and they each have different restrictions on the data. When a statistical variable is nominal or ordinal it does not qualify to
“We define two hypotheses: (1) The null hypothesis is that which represents the status quo to the party performing the sampling experiment-the hypothesis that will be accepted unless the data provide convincing evidence that it is false. (2) The alternative, or research, hypothesis is that which will be accepted only if the data provide convincing evidence of its truth. Hypotheses test are extremely helpful to businesses and operations because it helps to determine if a variable differs in a particular value or if it has a systematic differ or even sampling error” (McClave, Benson, & Sincich, 2011). Hypotheses test help to solve problems because is sets a level of certainty. An example would be when a piece of equipment on a production line needs to be repaired. The expected value of the number of movements it is to make in an hour would be the relevant variable, the actual values can be sampled, the information can then be used for the mean, the standard deviation and sample size will determine the probability of differences. If the probability is higher than the threshold that is set then action will need to be taken.
References
McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Prentice Hall.
What is the purpose of using non-parametric tests in operations management decisions? Provide examples of what you look for when conducting these tests.
Proper units occur when a statistical variable understudy can signify a ratio or an interval scale of measurement. When an assumption is made on the variability of the distribution, the variable is fit to be tested parametrically. Some parametric testing methods are t-test and z-test and they each have different restrictions on the data. When a statistical variable is nominal or ordinal it does not qualify to
References: McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Prentice Hall. What is the purpose of using non-parametric tests in operations management decisions? Provide examples of what you look for when conducting these tests. Proper units occur when a statistical variable understudy can signify a ratio or an interval scale of measurement. When an assumption is made on the variability of the distribution, the variable is fit to be tested parametrically. Some parametric testing methods are t-test and z-test and they each have different restrictions on the data. When a statistical variable is nominal or ordinal it does not qualify to be parametrically tested. In some cases a sample is obtained which may not have a well-defined population. When this happens there is no population parameter to fall back on being that the population itself is nonexistent. This is when a non-parametric test is created. There are no requirements of normality or homogeneity in the data and there are few outliers and their effects that will be ignored. The advantaged of a non-parametric test is that it can give answers quickly with little computation work. However, being that the tests are non-parametric it becomes difficult to quantitatively justify the differences. An example of using a non-parametric test would be using the chi-square tests to analyze the preferences that people have for movies. The same test can be used to see if there is gender bias in the selection of candidates. Another example would be to use the Wilcoxon Mann Whitney Test when analyzing the square footage of a three bedroom home to a four bedroom home by assigning ranks to the houses.