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1. The more the merrier, but the fewer the better!
Often, it’s difficult to determine the impact of an individual influencer on the response variable when multiple influencing factors have more or less the same influence. Let’s streamline this with a realistic example. Say for instance, we want to examine a child’s weight based upon various influencing factors including child’s height and age. It becomes evident that as children grow older, they get taller! Hence, both height as well as age are highly correlated in determining child’s weight. So, this case study has indeed a multicollinearity problem!
By a small sample, we may judge the whole pic!
All of us might have used the concept of sampling in our routine life. For instance, while purchasing vegetables from a shop market, don’t we examine a few to assess the quality of the whole lot? Doesn’t a doctor examine a few drops of blood as a sample in order to draw conclusions about the blood constitution of the entire body? Most of the time while dealing with big data problems, it’s not feasible to collect data from the whole population. Thus, sampling techniques are a useful procedure for selecting a subgroup (i.e., sample) from a population that is expected to be a representative of the whole population, in turn saving the time, cost as well as the efforts needed in examining the complete data. If anything goes wrong with the sample of data, then it will be directly reflected in the final result.
1. Machine Maintenance is always cheaper then downtime!
Sooner or later, all machines run to fail and monitoring the condition of the machine is crucial for any enterprise as any unplanned downtime can have greater economic impact resulting in reduced productivity and ultimately losing the customers. That being so, a regular way to keep the machine in a good condition is to timely monitor it and detect the patterns to predict the breakdowns. Predictive maintenance of machines helps in evaluating the timely collected machine data in order to gage its condition and predict when maintenance work is needed.