Material Security Analysis Paper
MULTIPLE REGRESSION ANALYSIS OUTPUT
Descriptive Statistics Mean Std. Deviation N
Modern day Material management .472587 .9387697 23
Material Management practices 1.079396 1.2708945 23
New technology in material management. .243857 .6473792 23
Issues Related to Vendors .742904 1.0021308 23
Material movement at site .330813 .7577001 23 Mean - This is the arithmetic mean across the observations. It is the most widely used measure of central tendency. It is commonly called the average. The mean is sensitive to extremely large or small values.
Std. Deviation - Standard deviation is the square root of the variance. It measures the spread of a set of observations. The larger the standard deviation is, the more spread out the observations are.
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Issues Related to Vendors Material movement at site
Pearson Correlation Modern day Material management 1.000 .328 .699 .591 .774 Material Management practices .328 1.000 .206 .789 .205 New technology in material management. .699 .206 1.000 .292 .753 Issues Related to Vendors .591 .789 .292 1.000 .523 Material movement at site .774 .205 .753 .523 1.000
Sig. (1-tailed) Modern day Material management . .063 .000 .001 .000 Material Management practices .063 . .173 .000 .174 New technology in material management. .000 .173 . .088 .000 Issues Related to Vendors .001 .000 .088 . .005 Material movement at site .000 .174 .000 .005 .
N Modern day Material management 23 23 23 23 23 Material Management practices 23 23 23 23 23 New technology in material management. 23 23 23 23 23 Issues Related to Vendors 23 23 23 23 23 Material movement at site 23 23 23 23 23
This is a correlation table with different pearson values. Modern day material management is the dependant variable and rest are the independent variable. Different value of pearson coefficient suggests the degree by which dependant variable gets affected by independent
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In addition to telling you whether variables are positively or inversely related, correlation also tells you the degree to which the variables tend to move together.
As stated above, covariance measures variables that have different units of measurement. Using covariance, you could determine whether units were increasing or decreasing, but it was impossible to measure the degree to which the variables moved together because covariance does not use one standard unit of measurement. To measure the degree to which variables move together, you must use correlation.
• If the correlation coefficient is one, the variables have a perfect positive correlation. This means that if one variable moves a given amount, the second moves proportionally in the same direction. A positive correlation coefficient less than one indicates a less than perfect positive correlation, with the strength of the correlation growing as the number approaches one.
• If correlation coefficient is zero, no relationship exists between the variables. If one variable moves, you can make no predictions about the movement of the other variable; they are
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