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 as ‘Predictors’. Through regression analysis, the investigator tries to ascertain the causal effect of one variable over another, for example decrease in demand due to increase in price. In addition, ‘statistical significance’ of the estimated relationships is also assessed. Multiple regression techniques have been a key to the field of ‘Econometrics’ and have wide range of applications such as evaluate trends and make estimates of forecasts. Regression analysis is also used to generate insights on customer behavior and estimating parameters for profitability.
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The general regression model can be represented as
Y= f (X, β)
Where, Y is Dependent variable,
X is Independent variable
Β is 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 with these different types of regression models and can provide online quality regression analysis assistance on 24*7 basis. Our Regression analysis writing services is one of the best in industry due to well qualified, experienced team of our professional regression analysis experts tutors. Our online experts have so far provided regression analysis homework help to numerous students across UK, USA and Australia. Our regression analysis demonstrate the difference among multiple techniques through below examples and justifications
Linear regression is one of the most widely known application technique. It also has highest number of business as well as academic applications. In the linear regression technique, dependent variable is continuous whereas predictor variable(s) can be continuous and discrete. It establishes 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 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 variables, it is called as multiple linear regression.
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Ordinary Least squares (OLS) regression: In ordinary least square technique, the equation is estimated by determining the equation such that sum of squared distances from each data point to the regression line as minimum as possible. Certain assumptions are considered for OLS to provide most precise results such as
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 categorical dependent variable and one of 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 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 dependent and the predictor variables is estimated using nth degree of the polynomial. These regression models are usually fit using method of least squares.
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Regression is a popular statistical technique and has 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 with diverse applications of regression analysis. They have years of experience solving the regression analysis homework and assignment and have deep expertise in all the regression academic concepts.
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|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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