edcommstatistics

As a special case of association or relationship, influence or effect is another notable concept in statistics. Instead of testing whether there is a relationship between communication quality and customers’ satisfaction, one may be more interested to investigate whether communication quality has an influence on customers’ satisfaction. Similarly, rather than questioning whether public perception and destination image of Jakarta are correlated, a researcher may enquire whether public perception has effect on destination image of Jakarta. One of many statistical tools to address such questions is regression analysis. In fact, regression analysis has many types and uses. What we are discussing here is the simplest form of regression model called simple linear regression. To perform the regression analysis, firstly we have to classify the variables of interest into dependent variable and independent/explanatory variable. The variable which is hypothesized to have influence on another

Statistics always deals with variability. There will be no statistics without variability. In our daily life, variability is ubiquitous. Today, we are going to study a kind of variability widely found in measurements. Let's see the case of manufacturing bottled water. Suppose that the labels on the bottles say that the volumes are 220 cc. Does it mean that all the bottles contain exactly 220 cc of water? In fact, if we measure the volumes accurately with a sophisticated measurement device, not all of them has the volume of 220 cc, but the volumes are around 220 cc. Some of them have the volume of 219 cc, some have the volume of 222 cc, 218, 219.5, etc. Statistically speaking, the volumes of water in the bottles have a mean, and since the volumes vary, they have a standard deviation. Now, can we estimate the percentage of bottled water whose volume is less than 217 cc? This is what this post addresses. If we know that the volume is normally distributed with a certain mean and st