excel data for regression analysis economics

The assumption of normality is also essential for most statistical data analysis methods. It may give an inflated R-square and erroneous significance level of the regression model. Real-world financial data, which is generally collected for a continuous period, shows a correlation that makes the errors in the regression model interdependent and rejects the assumption of independent errors.

The significance of the predictors changes significantly due to the removal of outliers from the data. The robust alternatives, such as Least Median Squares estimators (LMS), Least Trimmed Squares estimators (LTS), and M estimators, can tolerate up to a certain level of contaminations. However, the necessary assumption in regression analysis that as normality, homoscedasticity, multicollinearity etc., presence of outliers and influential observations makes the least square regression analysis vulnerable to such data and may give misleading and wrong results. Regression estimation is an important statistical method used in analyzing financial data, which often applies the Ordinary Least Square (OLS) method of regression to analyze such data. A hybrid intelligent model was proposed for forecasting stock market prediction based on news using the psycholinguistic variables. Data mining can also be used to analyze enormous amounts of information and datasets, which can help business organizations to solve various managerial problems, manage risks, and find opportunities. Optimization based Data Mining mainly focuses on Multiple Criteria Programming, and Support Vector Machines also have applications in various including finance. Several methods can be employed to analyze the financial data in this context. Data science is the study of analysing these large volumes of data by employing various modern methods of data analyses like complex machine learning algorithms to get meaningful information which helps in efficient and timely decision making. Recently, the covid-19 pandemic has also caused a drastic change in the financial data.

Twenty-one public sector banks were selected to study based on the importance of public sector banks in India.Īnalyses of real-world financial and economic data are a great challenge due to their high volatility, vastness and other economic and political factors. The present study analyzed the financial performance of public sector banks in India by applying a robust linear regression technique for more reliable outcomes. Public sector banks are the backbone of the Indian financial system. It is necessary to elaborate on the applications of the robust regression model in analyzing real-world financial data that do not fulfil the assumptions of most statistical methods of data analysis to get reliable results. It is an alternative method for the least square regression for such data. Robust estimation is a statistical method to analyze such financial data with outliers. However, the ordinary least square regression technique can give misleading and wrong results in the presence of outliers and influential observations in the data. The regression analysis has significance for all the fields of study, and almost all the fields apply least square regression methods for data analysis. Regression analysis is a statistical method to analyze financial data, commonly using the least square regression technique.

Excel data for regression analysis economics