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Introduction to statistics/ statistical data analysis
Firstly, the statistics process can define as the science concern with collect data, interprets data and then validate these collecting data. Here at this stage of statistics process data is useless and have no meaning so that the statistical data analysis process works now. Statistical data analysis process is a procedure or approach that uses the data in statistics to make some statistical methods. After performing methods on these data, the data will be something different and understandable. Finally, you can say that statistical data analysis process uses statistics process to handle collected data.
Before you start performing statistical analysis methods, here some steps that you should follow to make statistical data analysis process correctly:
- What is your purpose? This is the first step in data analysis process and it require to collect and analysis large amount of data
- Decide what type of data you want to measure and the way to measure these data.
- Now start collecting data: here you collect data after you know what data do you want. You can collect data from existing resources, existing databases or any others. You may need a large amount of data and the existing resources does not cover these data, so you have to collect new data. If you have more and more data with more better correlations, then you can easily make better models.
- Make your data clean: cleaning data process is a very important step in statistical data analysis process and it means that you have to improve the quality of your data. You can do clean data by handling missing data or sometimes by eliminating useless data. The importance of data cleaning process is that the existence of unneeded data may leads to generate unappropriated results.
- Data summarizing and visualizing: summarize exploring data process and visualize it will help you to better understanding data. For example, the method of variance indicates that how data distribute around the center.
- Make a modeling for your data: make recommendations by build your model that correlates data with your work results.
- Statistical data analysis process optimizing and repeating: the process of statistical data analysis may be a continuous process and may the process need improvements.
Data analysis process is the process of applying systemically statistical techniques or logical techniques for describing data process, evaluating data process, data illustration process, recapping data process and condensing data process.
The answer of this question is Yes. There are some considerations you are take in your mind when you analyze data process. Not just you, all researchers who work with data must consider these considerations:
- You have necessary skills to start analyzing data process
- Selecting what methods to use in collecting data process and which analysis is appropriate for collecting data in concurrently way
- Draw an unbiased inference for data: if the bias happens at collection of data process or select appropriate method, the drawing will be biased inference.
- Do not neglect the tests that failing to reach significant result. So that, the analyst should report both all tests even if some of these tests does not reach a significant result.
- Use conventional norms in data analysis process
- Determine if the gaining statistical significance has a true practical meaning
- Provide an accurate and honest analysis of collected data process
- The way to present results of collected data process
- As an analyst you should take in your account the contextual and environmental issues that may affected on data analysis process
- The method using for recording data process
- You should be aware about if there challenges of reliability and validity in analysis data process
- You should be aware of the potential for compromising data process integrity if you use statistical methods or non-statistical methods
Here you will define five basic methods that you can use them to accurate data-driven conclusions. The below explanation is about each of these methods and how can you calculate each method:
First method: Mean
The first method of statistics/ statistical analysis is the Mean method. Mean method also called an average which calculated for a data set by summing up all numbers of the data set and divide the summation by the number of items.
- Method of mean is allowing you to determine the overall trend of the data set
- Mean method is benefits to make simple and easy calculations
- The ability for the Mean method to obtain fast and concise view of data
Second method: Standard deviation
Standard deviation is the second method of statistics/ statistical analysis. Standard deviation method used to measure how data spread around the mean value. Also, standard deviation method used to select the dispersion of data points.
Third method: Regression
The third method of statistics/ statistical analysis is the regression method. Regression method explains the relationship between dependent variable and an independent variable. And also, regression method can be explained by how a variable can affect on another variable that means is there any change on one variable it leads to changes also to other variable.
Fourth method: Hypothesis analysis
Hypothesis analysis is the fourth method of statistics/ statistical analysis. In this method you make testing if a certain argument or conclusion is true abut the data set. This method of statistics/ statistical analysis compares the data against various assumptions and hypothesis.
Fifth method: Sample Size Determination: in the fifth method of statistics/ statistical analysis you need to select specifically the right size of the sample to be accurate to get best result of data analysis.
- SPSS: SPSS is abbreviated for Statistics Package for Social Sciences. It is the most common used statistical packages by researchers. SPSS used to compile descriptive statistics, parametric analyzes and non-parametric analyzes. Also, it allows researchers to get graphical depictions for statistical result.
- R: R foundations for statistical computing is a free statistical software which used widely in human research and other fields. Although R is also a powerful software statistical tool but it requires a certain degree of coding.
- MATLAB: is an analytical program and platform that used by scientists and engineers. As R MATLAB the learning path is steep, but also requires to build your code in some cases. There are many helpful toolboxes you can used to perform some codes.
- MS EXCEL: Microsoft Office Excel is a desktop program is a very simple tool that used for data visualization and also for simple statistics. MS Excel is a good option for those who want to start with statistics.
- SAS: SAS abbreviated for Statistical Analysis Software and it is a platform statistical analysis that used in business, healthcare and human behavior research. SAS allows you either to use GUI (Graphical user interface) or to create scripts for advanced analysis.
- Minitab: is a software tool that used GUI for simple analysis and scripted commands for advanced analysis. That’s because Minitab offers a group of basic and fairly statistical tools for data analysis. Also, Minitab allows you to use scripts commands for advances statistical analysis and GUI for graphic depictions.
GraphPad Prism: it is a software statistical tool that used primarily for statistics that related to biology
With greetings: Al - Manara Consulting to help researchers and graduate students