Regression Analysis Assignment Help by Statistical Expert
Regression Analysis is a statistical tool that can determine the relationship between the variables. The analysis involves two variables- one is the dependent variable and the other one is the independent variable. It is a useful technique that can predict or forecast changes in the dependent variables based on the changes occurring in the independent variables. The popular models of regression analysis are linear regression, simple regression, polynomial regression, general linear model, discrete choice, non-parametric, and many more. Most of these models are highly challenging and very complex to understand. Regression analysis can be solved following a step by step procedure to get an accurate answer within the minimum possible time. A number of steps are involved to solve a regression analysis problem like understanding the problem, choosing the relevant variables, a collection of data, the specification of the regression model, validification of the model, and thereafter using the selected model. It appears simple but actually, the analysis process is complicated and requires in-depth understanding. Hence, the Statistical students who find it demanding avail Regression Analysis assignment help from BookMyEssay. We provide the statistics assignment help by educating the complex topics in a detailed and simplistic manner. The students who have taken assignment writing help on regression analysis have scored the highest grades. Moreover, we provide timely solutions not only to the assignments but also the case studies, homework writing, and term papers.
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Regression Analysis Types
Different forms of regression analysis can be formed and some of them are mentioned below as follows:
- Linear Regression – Linear Regression technique is a widely known one. This analysis technique has the most academic applications as well as the number of businesses. In this technique, the dependent variable is a continuous one while the predictor variable may be either discrete or continuous. It establishes the relationship between the predictor variable (x) and the dependent variable (y).
- Simple linear Regression – Explores the relationship between a predictor variable and a dependent variable. If it has more than one independent variable, it is known as multiple linear regression.
- Logistic Regression – Logistic regression is also known as the Logit Model and it analyzes a dataset where one or more than one independent variable is there. It is utilized for predicting a binary outcome like true/false, Yes/No from the given set of independent variables. For representing the categorical outcome, dummy variables are used.
- Polynomial Regression – It is a non-linear regression. In this model, the relationship between the predictor variables and the dependent variables are estimated by the nth degree of the polynomial. This model uses the least squares method.
Regression Analysis a statistical technique that has wide applications. The two most important primary applications of this analysis are Optimization and Forecasting. Linear regression can predict the estimates and evaluate the trends. It can also analyze the pricing, sale promotion, and the marketing effectiveness of the product.
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