Establishing patterns and trends in the data collected

1. What role does graphical analysis play when performing an initial statistical analysis? According to ( PV, 2020), graphical analysis helps in analyzing the problems in the process. It helps us to visualize the patterns in data and provides key insights. The graphical analysis also helps in understanding the patterns in the data and correlation in the process parameters. When performing Initial statistical analysis, it is vital that the team selects appropriate graphical analysis tools as there are multiple tools available such as Bar charts, box plots, histograms, scatter diagrams, Pareto charts, and time series plots. Graphical analysis can be used to plot any data, whether it is live or historical. In an initial statistical analysis lot of data is collected, and sometimes the team does not understand it unless it is in some form of a graph. The graphical analysis helps understand the nature of the process and provides a viewpoint for further analysis (Saadeddin). 2, Explain the difference between correlation and causation. Correlation - It can be described as the relationship between any of the two or multiple variables that can change if one of them changes (S, 2019). Causation - The primary purpose of causation is to understand the cause and effect (S, 2019). Difference - Causation and correlation can exist together, but a correlation does not imply causation. Causation could be a correlation with a reason. Causation has a sequence of events. The second event is caused by the first one. A correlation has a weaker connection between two occurances. ---------------------------------------------------------------------------------------------------- 1) What role does graphical analysis play when performing an initial statistical analysis? Graphical analysis is very important when performing any kind of statistical data analysis being historical data that already existed or the live data which is collected while performing an experiment or in a process. Very long data lists can be confusing to analyze and this is where graphical analysis comes to play. The best starting point would be to plot the data on a graph and see what the data is telling or indicating. Different types of graphical analysis can reveal different trends or characteristics like central tendency, dispersion of the data, and many more things. Graphical analysis can help us learn about the nature of the process, enables better communication, and helps in focusing for further analysis which saves time. Sources of variation in the data are a key part of understanding what is actually happening in the process and this is where graphical analysis comes into play. 2) Explain the difference between correlation and causation. Correlation is a statistical measure that is express in a number that describes the direction of a relationship between two or more variables. It does not necessarily mean that the change in one variable is the cause of change in the other variable. While causation indicates that the result of one event is because of the occurrence of the other event. There is a causal relationship between the two events. This is also referred to as cause and effect. In theory, the difference between the two types of relationships is easy to identify — an action or occurrence can cause another, or it can correlate with another. In practice, however, it remains difficult to clearly establish cause and effect, compared with establishing correlation.