To do this, you create so-called scatter diagrams

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So, as in my example: SMEs and NON_SMEs. This is worthwhile as soon as you survey 2 or more groups. There are differences between SMEs and NON_SMEs, for example in the number of days working from home, etc. This makes sense as soon as you want to compare something or work out differences or, like me, specifically examine SMEs. regression analysis Here you try to show the dependency of one independent variable on another. For example, a CEO wants to know how much money he has to invest in advertising so that something changes in the company, such as sales figures or new customers.


and look to see whether there is a connection between the selected variable malta phone number resource and the others. In contrast to the next method, here the cause and effect is examined precisely. This enables forecasts, which is worthwhile for research questions if you want to make forecasts. correlation analysis Here you look at the connection between two variables. In other words, whether they are related. For example, you can say whether employee satisfaction and days spent working from home can be related.


Does this increase or decrease with more days spent working from home? This makes sense if the research is investigating an influence on something. In contrast to regression, no cause and effect is determined here, but only how similar two variables are. So here you are more likely to be investigating the connection in the here and now. Reading tip: Study by me with correlation analysis cluster analysis With a cluster analysis, you can determine similarities in large groups and summarize them.