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Linear Regression Essay

1111 words - 5 pages

Linear regression analyzes the relationship between an independent and dependent variable to find the line that best fits between them. It has been used to predict a continuous dependent variable from a number of independent variables. This report will discuss the different tools used for such analysis and will also describe histograms and bivariate plots. It will also discuss the value of a slope which is shown as the ratio of change in the y-value over the change in the x-value. Lastly, this report will show how linear regression can be utilized in the management environment.

Linear regression is an independent variable x and those values which are fixed, and a dependent ...view middle of the document...

The chart below (wadsworth, 2010) show the grade point average and the SAT scores that correlate with the scores.
The graph shows the correlation coefficient for grade point average correlation.


Macmillan Dictionary of Toxicology. (1999). Linear Regression. Retrieved August 3, 2010, from Macmillan Dictionary of Toxicology:

Macmillan Reference Ltd. (2000). Correlation Coefficient. Retrieved August 3, 2010, from Macmillan Reference Ltd:

wadsworth. (2010). The Pearson Product Moment Correlation Coefficient. Retrieved August 3, 2010, from

What is the slope intercept formula and how is it beneficial in a management environment?      The slope intercept formula could be defined as” the equation of each line with y-intercept (0, b) and slope m is y= mx +b (Dugopolski, 2002). In addition to that, the formula to the slope intercept is:
Slope Intercept Form:
Slope Intercept Form = y = mx + c
              c = y-intercept of the line,
              m = Slope of the line

Slope Intercept Form Diagram
[pic] “(Slope Intercept Form Tutorial, para.).
In a management perspective, this particular formula could be used for a variety of reason. First, graphing income within a company is an example of how management could use the intercept slope formula. For example by taking the number of years and the net income to show how a business has progressed over the years.

|Year |Net Income |
|2008 |991352.1 |
|2007 |896236.3 |
|2006 |686833.1 |
| | |
|Regression Statistics |  | | | | |
|Multiple R |0.977319989 | | | | |
|R Square |0.95515436 |Goodness of Fit >= 0.80 | | | |
|Adjusted R Square |0.910308721 | | | | |
|Standard Error |46657.63567 | | | | |
|Observations |3 | | | | |
| | | | | | |
|ANOVA ...

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