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Linear regression

This Presentation will guide through brief intro about Linear regression and its applications.

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Linear regression

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  1. Linear Regression Swipe

  2. What is Regression Regression is a statistical technique used in finance, investing, and other fields to evaluate the degree and nature of a connection between two or more dependent variables.

  3. Uses of Regression Determining the strength of predictors. Forecasting an effect. Trend Forecasting.

  4. Linear Regression By fitting a linear equation to observed data, linear regression seeks to model the connection between two variables. The equation for a linear regression line is Y = a + bX, with X as the explanatory variable and Y as the dependent variable.

  5. Linear Regression Selection Criteria Classification and regression capabilities. Data quality. Computational complexity. Comprehensible and transparent.

  6. Linear Regression Used Where is Linear regression used:- Evaluating trend and sale estimate. Analyzing the impact of price changes. Assessment of risk in financial service and insurance domain.

  7. Linear Regression Algorithm Understanding Linear Regression algorithm:- y Line Dependent variable x Independent variable

  8. Linear Regression The first order Linear model Y = Dependent variable Y=b0+b1 X+e X = Independent variable b+ = Y- intercept b1 = slope of the line e = errorvariable

  9. Application of Linear Regression If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of Y and X value. After developing such model, if an additional value of X is then given without its accompanying value of Y. The Fitted Model can be used to make a prediction of the Value of Y. Given a variable y and a number of variable X1,.........Xn that may be related to Y, linear regression analysis can be between Y and the Xj. To assess which Xi may hvae no relationship with y at all, and to identify which subset of the Xj contain redundant information about y.

  10. Topics for next Post Logistic regression Naive bayes Linear Discriminant Analysis Stay Tuned with

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