Regression.pptx

statistics for engineers

1. Regression

2. Introduction of Regression; Cause and effect relationships, Simple linear regression, Logistic regression, and Multiple regression

3. Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more  independent variables . It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them Regression

4. Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship. Regression analysis offers numerous applications in various disciplines, including finance.

5. Cause and effect is the  relationship between two things or events where one event caused another event, or several events, to happen. Cause and Effect   R elationship

6. For example: Smoking causes lung cancer, is not about an particular smoker but states a special relationship exists between the property of smoking and the property of getting lung cancer.

7. Simple linear regression is a model that assesses the relationship between a dependent variable and an independent variable. The simple linear model is expressed using the following equation : Y = a + bX + ϵ Simple linear regression

8. Where: Y  – Dependent variable X  – Independent (explanatory) variable a  – Intercept b  – Slope ϵ  – Residual (error)

9. Find the correlation between age and blood pressure using simple and Spearman's correlation coefficients, and comment. Find the regression analysis? Solve through SPSS

10. Logistic regression  is the appropriate  regression  analysis to conduct when the dependent variable is dichotomous (binary).   Logistic regression  is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. Logistic regression

11. The dependent variable should be dichotomous in nature (e.g., presence vs. absent). There should be no outliers in the data, which can be assessed by converting the continuous predictors to standardized scores, and removing values below -3.29 or greater than 3.29. There should be no high correlations ( multicollinearity ) among the predictors.  This can be assessed by a correlation matrix among the predictors. Tabachnick and Fidell (2013) suggest that as long correlation coefficients among independent variables are less than 0.90 the assumption is met. Binary Logistic Regression Major Assumptions

12. Logistic regression (1) Table 2 Age and signs of coronary heart disease (CD)

13. Multiple regression  is an extension of simple  linear regression . It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). Multiple R egression

14. Simple linear regression is a function that allows an analyst or statistician to make predictions about one variable based on the information that is known about another variable. Linear regression can only be used when one has two continuous variables—an independent variable and a dependent variable. The independent variable is the parameter that is used to calculate the dependent variable or outcome. A multiple regression model extends to several explanatory variables.