3.4 Statistics

3.4.1 Descriptive analysis

This analysis to present the descriptive statistics and measures of central tendency and dispersion. The result is included the mean (average), median, mode, range, variance and standard deviation of every variable in this research.

In the other words, this analysis help to detail and comprehend the characteristics of a specific data set, by bestow short statement of the main points about the sample and scope the data. The popular types of descriptive statistics are the mean, median and mode, which are used at all levels of calculation in math and statistics. Nevertheless, type of descriptive statistic that is not very known not mean it not important. Actually, all statistics are very important. People used descriptive statistics to reuse for a new purpose which is hard to understand quantitative intuitiveness across a large data set into specific descriptions. As an example a studentâ€™s Grade Point Average (GPA).

These descriptive statistics are measures by using graphs, tables, and general discussion to help people more understand each of the data being analyzed mean. It includes measuring of central tendency and also variability. Measures the central tendency is to explain the position of an allocation for a data set. It measures the common patterns of data set which it calculates the real data. For measures the variability is in analyzing how to divide the data distribution is for a set of data. These statistics are using an average of data.

3.4.2 Correlation analysis

The correlation analysis is a statistical analysis that used to measure the relationship between dependent variable and independent variables. The value of correlation between -1 and +1 and if 0 the two variables are totally uncorrelated. The sign negative and positive sign is referring to the correlation. The positive sign refers to direct correlation and negative sign refers to inverse correlation. It also is known as a vital tool in the hand of Six Sigma teams.

In order to accomplish the correlation analysis, we must have a complete data set for the variables under question. Once there is sufficient data, this data was plugged into a formula developed, (Karl Pearson). The formula was famously called Karl Pearsonâ€™s coefficient of correlation. It affected the complex calculation and administered the occupancy of a statistician in the Six Sigma team. But nowadays, the calculation is carried out by a software tool.

3.5 Econometric analysis

3.5.1 Regression analysis

Regression analysis defined as the numerical method in order to explain changes in the dependent variable because of the movements in the independent variables. It reveals the form of relationship between variables. Regression analysis also a generic term for all methods attempting to fit a model to observed data in order to quantify the relationship between two groups of variables, where the focus is on the relationship between a dependent variable and one or more independent variables.

The relationship, however, may not be exact for all observed data points. Hence, very often, such analysis includes an error element introduced to account for all other factors. The attempt is to arrive at a relation where deviation from it i.e. mean of the error should be close to zero and its standard deviation should be minimal. The dependent variable is a function of an independent variable. The test conducted for the regression analysis are t-test, F-test, R^2 and adjusted R^2.