One of the most common questions that analysts get asked is what are the things to do in data analysis. The answer to this question is as wide-ranging as the number of things to do in it. Data analysis is very important to many people in all kinds of fields. There are many more things to do in data analysis than simply use it in making tables or graphs.
Data analysis has been around for a long time. It was first developed by James radar in equations of variance and probability. Later on, Sir Ronald Fisher extended this to include non-parametric statistical methods. Fisher’s famous logistic regression explained much of the problem of how to deal with abnormal data. Today, with computers and bigger data sets available, more people are turning to data analysis to explain things to them.
The first things to do in data analysis are normally modeling the data sets and the variables that can be analyzed. Models are usually written in a programming language like R or SML. The main concept of models is that they can be studied in terms of how they fit together and how to interpret the results given to you by your models. The more accurate the model, the better the fit between the variables and the data. This is orlando for improve your data analytic ideas.
With more sophisticated models, sometimes it is a good idea to run the model multiple times just to check for any kind of outliers, because sometimes simple models can fail when the numbers they are trying to predict changes significantly from time to time. After the time period for the best fitting model is over, one can then plot the residuals and see what kind of trend you are getting. Some people prefer to plot their data in a table format and then graph it using Microsoft Excel.
One can also make some assumptions about the normal distribution of data and use them to fit a range of probability distributions. The data is normally distributed normally, but it can be plotted as a logistic curve so that it becomes clearer how much more or less certain sample are more likely to be included in the data set than others. One can also plot the data as a normal range, but only if the range is known beforehand, because otherwise the data set can be much larger than needed. Another thing to do in data analysis is to take the mean of all the data points. Then this gives us a probability density function, which can then be used to calculate the probability of a certain outcome, or the probability of being in a certain range.
In some cases, especially with real data, it is possible to reconstruct the probability density function by mathematical procedures called density based statistics. This is usually done for log-normal data. There is much more to the subject than this brief overview. If you are going to take on the subject, you might want to spend more time learning about the topic. It’s worth it, though, once you understand what types of things to do in data analysis.