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Correlation & regression confer with the relationship that exists between 2 variables, X and Y, within the case wherever each particular value of Xi is paired with one specific value of Yi.

Fundamentally, it's a variation on the theme of quantitative functional relationship. The more you have got of this variable, the more you have got of that one.

Correlation and regression are two sides of the same coin. Within the underlying logic, we will begin with either one or end up with the other.  We'll begin with correlation, since that's the part of the correlation-regression story with that we are probably already somewhat familiar.

The construct of correlation could be a statistical tool that studies the connection between 2 variables and Correlation Analysis involves varied strategies and techniques used for studying and measuring the extent of the connection between the 2 variables.

There are 2 necessary varieties of correlation which includes:-

 (1) Positive & negative correlation

(2) Linear & Non – Linear correlation

Regression analysis suggests that the estimation or prediction of the unknown value of one variable from the known value of the other variable. It is one of the foremost necessary statistical tools that are extensively utilized in almost all sciences – Natural, Social and Physical. it is specially utilized in business and economics to check the relationship between two or a lot of variables that are connected causally and for the estimation of demand and provide graphs, price functions, production and consumption functions so on. Regression analysis was explained by M. M. Blair as follows:

Regression analysis is the proper measure of the average relationship between two or more variables in terms of the original units of the data mathematically. 

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Topics for Advanced Correlation & Regression Analysis Assignment help : 

  • Least squares estimation , hypothesis testing and confidence interval estimation in regressionsimple, polynomial and multiple linear regression, residual and lack-of-fit analysis, use of dummy variables, multiple and partial correlation analysis, model building algorithms and model comparisons, transformations., Probability rules; random variables, expectation joint & marginal distributions.
  • covariance & correlation independence, central limit theorem, Normal, chi-square, t, & F distributions, Inference on means & variances, Simple Linear Regression (SLR), SLR model, parameter interpretation, method of least squares, normal error model, point estimation, Inferences prediction, analysis of variance for normal error model, correlation and R, Fitted values & residual diagnostics, outliers; heterogeneous.
  • variance; lack of fit; nonlinearity, transformations, Simultaneous inferences, Regression through the origin, inverse prediction, predictor variable selection, Matrix formulation of SLR model, Multiple Linear Regression (MLR), MLR model, matrix formulation, parameter interpretation, normal error model, point estimation, pointwise & simultaneous inferences, Regression diagnostics, extra sums of squares.
  • Model Extensions and Model Adequacy, Multicollinearity, polynomial regression models, interaction models, qualitative predictor variables, comparing regression functions, Model selection procedures, Leverage; influence measures, diagnostics least squares, ridge regression, loess regression, random assignment experiments, nonexperimental methods, regression discontinuity designs, instrumental variables, difference-in-difference models, propensity score matching, maximum likelihood estimation, multinomial and ordered logit and probit, truncated/censored dependent variables, panel data and time-series analysis.
  • Exponential family and generalised linear models,Estimation (ML) and inference,Model selection ,Model checking,Nonparametric regression models,Local linear modelling,Local least square estimation ,Bandwidth selection ,Varying-coefficient models,,statistics and hypothesis testing,bivariate and then multivariate regression,Model specification and interpretation,Diagnostic tests and plots ,Analysis of residuals ,Influence and outliers ,Transformations to induce linearity ,Multicollinearity,Multiplicative interaction terms ,Dummy (dichotomous) variables ,Categorical (e.g., Likert scale) independent variables ,Logistic Regression models

Complex topics covered by Advanced Correlation & Regression Analysis Assignment online experts :

  • Basics of Least Squares Regression, Least-squares, Properties of the least-squares estimator, Statistical inference, Regression in matrix form, Factors and contrasts, quasi-variances, graphical displays, Interactions and effect displays, Standardization , Factors and contrasts, Interactions, Relative Importance, Linearity, Diagnostics, Transformations, Polynomials , Diagnosing linearity through residual plots.
  • Fixing non-linearity with data transformations, polynomials, Linearity, ordinal variables, Non-Linearity, Smoothing and Splines, Nonparametric Smoothing , Inference for regression smoothers, Regression Splines, Generalized Additive Models, Estimation and Back tting, Cross-validation for smoothing parameters, Polynomials, Smoothers, splines, Re-sampling Techniques, Regression , Bootstrapping.
  • Cross-validation, Robust Regression, Breakdown point, in, uence function, various types of robust regression, M-estimation, iterative re-weighted least squares, Robust GLMs, Weighted least squares, Heteroskedastic linear regression, Robust standard errors, Critiques of the Linear Regression Model, Theoretical issues in model searching, post-data model construction, Model selection criteria.
  • Multi-model inference, Subset selection models, Finite Mixture Models, Missing Data, Multiple Imputation, random assignment, regression discontinuity designs, instrumental variables, difference-in-difference models, propensity score matching, likelihood estimation, multinomial and ordered logit and probit, censored dependent variable, panel data, time-series analysis
  • Review of Probability and Statistics,Probability rules ,random variables ,expectation,joint & marginal distributions,covariance & correlation,independence; central limit theorem,Normal ,chi-square ,t ,F distribmutions,Inference on means & variances ,Simple Linear Regression (SLR),SLR model,parameter interpretation ,method of least,squares ,normal error model ,point estimation,Inferences ,prediction; analysis of variance for normal error model,correlation and R2,Fitted values & residual diagnostics ,outliers ,heterogeneous variance,lack of fit,nonlinearity ,transformations,Simultaneous inferences ,Regression through the origin,inverse prediction,predictor variable selection,Matrix formulation of SLR model,Multiple Linear Regression (MLR),MLR model ,matrix formulation; parameter,interpretation ,normal error model ,point estimation,pointwise & simultaneous inferences,prediction,Regression diagnostics,R2,extra sums of squares. ,Model Extensions and Model Adequacy ,Multicollinearity ,polynomial regression models ,interaction models ,qualitative predictor variables ,comparing regression functions,Model selection procedures,Leverage; influence measures ,diagnostics,Weighted least squares,ridge regression,loess regression,Random Variables,Introduction to RMarkdown,Correlation,Simple Regression,Multiple Regression,Outliers and Regression ,Diagnostics,Categorial Predictors,Moderator Analysis,Moderators & Polynomials,Bootstrapping ,Mediation ,Logistic Regression, Poisson Regression, & the Generalized Linear Model

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