Quantitative and Analytical Techniques for Managers Coursework Essay

Quantitative and Analytical Techniques for Managers Coursework - Essay ExampleThis indicates that some of the provinces may be very poor while others may be very rich although we still energise to validate this when the data are transformed into sodding(a) regional yield per capita.A standard aside of 759,648.2 million kwai which is very close to the mean value of 892,031.93 million yuan. This indicates a very spicy variability. The high variability is also indicated by the high variance of 5,770,653,964 million yuan.From the perspective of the manager, the mean as measure of central tendency is very useful. However, the mean can mask a situation in which some of the provinces or cases have actually very high or very low changeable values. The mode can be almost useless for ratio data but is very useful for nominal phrase data or variables. The median is extremely useful to identify at what value the universe is divided into 2 equal parts half(prenominal) below the median w hile the other half is supra the median. For instance, in the data supra, the median is 609,110 million yuan versus the mean value of 892,031.93 million yuan or that the median is lower than the mean. This indicates that a few provinces with high values of the gross regional product are raising the mean to be above the median.Given a poverty figure, for example, we can check up on through the median whether at least half of the population are below or above the poverty figure. Another option is to use a measure of reenforcement standard. A median above the living standard would indicate that at least half of the population are above the living standard.For ratio data, it is the belief of this writer that the using both the median and the mean simultaneously would be useful. However, for nominal variables, the identification of central tendency through the mode will the one useful.1.2. The Pearson correlation coefficient between the gross regional product and gross capital is posit ive 0.97408077 versus the perfect correlation of positive 1.