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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 :**

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- 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.
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- 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