Regression analysis is the statistical tool for estimating relationships among the variables. In the regression analysis, the focus is primarily on determining the relationship between the dependent variable and one or more independent variables. The independent variables are also called ‘Predictors’. Through regression analysis, the investigator tries to ascertain the causal effect of one variable over another, for example, a decrease in demand due to an increase in price. In addition, the ‘statistical significance of the estimated relationships is also assessed. Multiple regression techniques have been a key to the field of ‘Econometrics’ and have a wide range of applications such as evaluating trends and making estimates of forecasts. Regression analysis is also used to generate insights on customer behaviour and estimate parameters for profitability.
Such wide applications have resulted in increased usage of regression analysis for academic purposes. Students across the universities need to solve numerous assignments, homework and projects based on regression analysis. Our Statistics assignment help has been designed to cater to all the subject areas covered under Regression analysis. Our online regression analysis help assists students across UK, USA and Australia with regression modelling, preparing data analysis and insights building. All of our online Statistics experts are well versed in various academic concepts of regression analysis and provide plagiarism-free, high-quality solutions. All the online statistics tutors are equally adept with the use of statistical software and tools such as SPSS, SAS, Minitab and STATA and assist you with regression analysis solutions even during tight timelines. If you are one of the students who find regression analysis complicated, avail regression analysis help from our Statistics expert tutors.
The general regression model can be represented as
Y= f (X, β)
Where Y is the Dependent variable,
X is the Independent variable
Β is the constant or unknown parameter
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Based on the relationships between the dependent and predictor variables, various forms of regression models can be defined such as Simple Linear regression, Multiple Linear regression, Logistic regression, Polynomial regression etc. All of our online Statistics experts are well versed in these different types of regression models and can provide online quality regression analysis assistance on a 24*7 basis. Our Regression analysis writing services are one of the best in the industry due to well qualified, experienced team of our professional regression analysis expert tutors. Our online experts have so far provided regression analysis homework help to numerous students across UK, USA and Australia. Our regression analysis demonstrates the difference among multiple techniques through below examples and justifications
Linear regression is one of the most widely known application techniques. It also has the highest number of business as well as academic applications. In the linear regression technique, the dependent variable is continuous whereas the predictor variable(s) can be continuous and discrete. It establishes a relationship between the dependent variable (Y) and one or more predictor variables (x) using the best fit line whose nature is linear. The best fit line is also called as Regression line.
The linear regression can be represented as
Y= f (X, β)
Where Y is the dependent variable
β0 is intercept or constant
B1 is slope
e is error
Simple linear regression examines the relationship between one dependent variable and one predictor (independent) variable. If the model includes more than one predictor or independent variable, it is called as multiple linear regression.
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Ordinary Least squares (OLS) regression: In the ordinary least square technique, the equation is estimated by determining the equation such that the sum of squared distances from each data point to the regression line is as minimum as possible. Certain assumptions are considered for OLS to provide the most precise results such as
The regression model is linear
Residuals have normally distributed and have a mean of zero
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Logistic regression also called as Logit Model measures the relationship between a categorical dependent variable and one or more predictor variables. The model estimates the probabilities using a logistic function which is cumulative logistic distribution. According to logistic regression assignment help experts, logit regression can be treated as a specialized case of the generalized linear model and thus is analogous to linear regression.
Polynomial regression is a non-linear type of regression. In the polynomial regression model, the relationship between the dependent and predictor variables is estimated using an nth degree of the polynomial. These regression models are usually fitted using the method of least squares.
In addition to these models, our statistics assignment help experts provide online assistance for Stepwise regression, Ridge regression, Lasso regression and Elastic Net regression. So, get in touch with our customer service and get online regression assignment homework help.
Regression is a popular statistical technique and has a wide range of applications. Two primary applications of Regression analysis are Forecasting and Optimization. Linear regression is used to evaluate trends and predict estimates. It can also be used to analyze marketing effectiveness, pricing and promotion on sales of the product. Our Statistics assignment experts are well versed in diverse applications of regression analysis. They have years of experience solving regression analysis homework and assignment and have deep expertise in all the regression academic concepts.
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Regression Analysis | |
---|---|
Correlation and R | Multiple Linear Regression |
Probit Regression | Logistic Regression |
Multi Co-Linearity | Confidence Interval Estimation |
Residual Error | Generalized Linear Model |
Best Fit Equation | Bootstrapping |
Ordinary Least Squares Regression | Ride Regression |
Simple Linear Regression | Non-Parametric Regression |
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