Definition of Independent and Dependent variables
Both variables are mathematical tools used to maintain control over an experiment in a quantitative manner. Independent variables are changes or enactments made in order to do an experiment. In any valid experiment, there should only be one independent variable. On the other hand, dependent variables are those always affected by changes in independent variables; they can be more than one of these in a valid experiment.
Correlation and Regression analysis
The above mentioned analyses are statistical. Their only difference is in the ultimate goal when making mathematical inferences. The aim of correlation analysis is to see if two measurement variables co-vary or for inferring a strength relationship between any given variables. Finding the intercept and slope of a best fitting line on the given point is the main goal of regression anlaysis.This line gives a directly observable relationship of the given variables. It can also be used in estimating the unknown value when given an unknown and a known value. The two analyses are used when one has two measurement variables. They are used to put hypotheses to the test with respect to cause-and-effect experiments. Here the X-axis variable is independent (i.e. the cause) and the Y-axis is dependent (i.e. the effect) on the resultant values of the independent axis. Another use is the investigation of association between two variables; both being dependent variables. A particular usage of linear regression is the estimation of the correspondence between two values of different variables. It involves the rearrangement of the e regression line equation to produce the unknown variable X and make an estimation of the required value.
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Value of the Discussed terms
The mathematical description of the independent and dependent variables give the correlation and regression analyses meaning. The gravity of these terms gives the statistical theories value so that vital hypotheses can be made.
The DMAIC process in my organization
This DMAIC process brings measureable and significant improvement to existing processes that are below expectation in the organization. DMAIC can be used in generating solutions to a failing system when unprecedented issues arise and a real crisis jeopardizes its progress. Firstly, the definition of my organization needs to be made by using concrete terms. Critical-to-quality (i.e. CTQ) characteristics that have the most impact on quality are identified and used to create an improvement process based on defined measurable goals. Secondly, the measure phase begins with the use of proper critical measures to evaluate the success of this process. Its key inputs are prioritized to establish a short list for detailed analysis and determine potential ways in which the process could go wrong. Once this is done, preventative action plans are placed.
In the next phase of analysis, DMAIC can be used to determine the perceived cause of prevailing problems that needs some improvement. As such, the organization would use the approach to eliminate the gap between existing performance and the desired performance level. DMAIC may be used in the discovery of why defects were generated in any production process by deliberately identifying the key variables that are most likely to create process variation. Here, various process improvement scenarios are identified and the best net-benefit impact to the organization is determined. On analysis, I get solutions and a cost to benefit analysis. From the DMAIC, the organization develops an implementation plan with a change management approach to ensure adaptability capacity to the new system. DMAIC may necessitate the need for team training in order to create effective research groups for competitive reasons. Under this consideration, the necessary training and delegation of tasks is done, where the people involved are given responsibilities with a definite or clear follow-up plan.