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One of the most popular and effective multidimensional statistical approaches is regression analysis. It postulates the nature and extent of significant relationships between the variables in a data set. Once you understand how to apply regression, you can identify and determine the relationship between two or more variables. Regression methods help researchers make inferences and forecast future outcomes and behaviours. For example, sales forecasts and weather forecasts depend upon the regression analysis. However, there are some common mistakes that every statistician mistakes in regression methods. These mistakes compromise the validity of the results and lead to incorrect inferences and predictions. This article will tell you about the eight mistakes that every statistician makes in regression analysis.

Regression analysis is used to measure and analyse data trends and patterns. Quantitative data is easy to analyse and evaluate. The research design of the quantitative research method assists the calculation process. Research design can have qualitative research or quantitative research design. A researcher may also use an integrated research method. The integrated research method incorporates both methods. It is challenging to conduct quantitative research as the data calculation, and interpretation in this process are challenging.