Correlation Analysis
Correlation Analysis is another technique that helps evaluate the relationship between two variables. This process can be started with plotting points on a scatter diagram.
This will show a visual representation of the relationship. The next step is to develop a quantitative measurement by creating a calculation, a correlation coefficient.
Another type of analysis is the Regression Analysis. This evaluation also examines the relationship between two variables. This type of analysis used the form of an equation to evaluate the relationship. It identifies independent and dependent variables, X and Y to evaluate the linear relationship between the two variables. The two variables being evaluated will be renamed X and Y for this analysis.
Correlation Analysis
Correlation analysis is useful in determining if two variables or two pieces of information that seem to have a cause and effect truly to affect one another. The analysis begins by gathering data, numerical, on the two variables and then determining which will be the dependent and the independent variables.
The independent variable describes the basis for estimation or predicting the dependent variable and the dependent variable is the variable up for estimation or prediction.
a scatter diagram offers visual representation of theses variable with the dependent variable being graphed on the x-axis and the independent variable being graphed on the y-axis. A correlation coefficient is a measure of the strength of the connection between the two variables and ranges between -1 and +1 on the diagram.
a value close to +1 represents a positive relationship between the two variables and is usually the outcome being pursued. In the formula used to determine the correlation coefficient r represents this value. In our text chart 13.4 represents a good example of this correlation coefficient and linear/correlation analysis.
Linear Regression
The article under reserved readings for this week called "Linear Regression 101" paired well with this week's learning assignments. The article opens up by showing many ways that linear regression analysis is used in daily life to yield relationships.
In linear regression analysis one dependent variable and one or many independent variables are analyzed to see what effects each has on the other. Variables then get plotted on a graph so that a line can be drawn to show the relationship. This article touched on the concepts of errors and outliers and states that once plotted these observations can be easily detected.
Reference
Hu, Y. (2011). Liner Regression 101. Journal of Validation Technology, 17(2), 15-22.
Linear Regression
Tracy and class after reading this article it is very helpful to see that in the simple comparison of some data a scatter plot (typical) can be drawn to easily see the association between the data and what the data comparison can mean or provide to a conclusion.
I know that in my daily tasks at work I compare data sets where the dependent variable is always sales and the independent variable are of a variety ( ADT, UPT, conversion, traffic, SPH, etc.).
This data is used so that I may track my business and see my success towards my goals. I have never had to graph them but often will find percentages towards my whole and estimate need and successes. I use Excel to chart the data, I should create a scatter plot to see the full association of this data.
Linear Regression
Tracy you did a great job talking about linear regression. i wanted to also go over this as well. i feel that it is very important to understand and learn.
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear regression.
For more than one explanatory variable, the process is called multiple linear regression. Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.