Midgett Co. has accumulated data to use in preparing its annual profit plan for the upcoming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff suggested that linear regression be employed to derive an equation for maintenance hours and costs. Data regarding the maintenance hours and costs for the last year and the results of the regression analysis are as follows: Month Maintenance Cost Machine Hours Jan. $ 5,000 600 Feb. 3,644 440 Mar. 4,400 610 Apr. 3,337 480 May 5,222 660 June 3,390 410 July 3,618 470 Aug. 5,384 630 Sept. 5,114 590 Oct. 4,883 590 Nov. 3,925 430 Dec. 3,850 350 Sum $ 51,767 6,260 Average $ 4,313.92 521.67 Average cost per hour ($51,767/6,260) = $8.27 (rounded to the nearest cent) r = 0.85977 r2 = 0.73920 The percent of the total variance that can be explained by the regression equation is:
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Midgett Co. has accumulated data to use in preparing its annual profit plan for the upcoming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff suggested that linear regression be employed to derive an equation for maintenance hours and costs. Data regarding the maintenance hours and costs for the last year and the results of the
| Month | Maintenance Cost |
Machine Hours | |||||
| Jan. | $ | 5,000 | 600 | ||||
| Feb. | 3,644 | 440 | |||||
| Mar. | 4,400 | 610 | |||||
| Apr. | 3,337 | 480 | |||||
| May | 5,222 | 660 | |||||
| June | 3,390 | 410 | |||||
| July | 3,618 | 470 | |||||
| Aug. | 5,384 | 630 | |||||
| Sept. | 5,114 | 590 | |||||
| Oct. | 4,883 | 590 | |||||
| Nov. | 3,925 | 430 | |||||
| Dec. | 3,850 | 350 | |||||
| Sum | $ | 51,767 | 6,260 | ||||
| Average | $ | 4,313.92 | 521.67 | ||||
Average cost per hour ($51,767/6,260) = $8.27 (rounded to the nearest cent)
r = 0.85977
r2 = 0.73920
The percent of the total variance that can be explained by the regression equation is:
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