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Information about Correlation & linear regression

Topic: Correlation & Linear regression

Subject: QTIA

Software: SPSS

Dr Faisal Afzal Siddiqui

Subject: QTIA

Software: SPSS

Dr Faisal Afzal Siddiqui

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Bivariate Correlations Bivariate Correlation Paersone Correlation or Co-efficient of correlation Scale level of measurement

p<0.05 Significant Correlation Researcher can be 95% confident that the relationship between these two variables is not due to chance

Denoted -1 by r ≤ r ≤ +1 0 ------- ±0.3 No Relation ±0.3 ------- ±0.5 Weak Relation ±0.5 ------- ±0.8 Moderate Relation ±0.8 ------- ±1 Strong Relation

1 is total positive correlation, 0 is no correlation, and −1 is negative correlation The closer the value is to -1 or +1, the stronger the association is between the variables

Linear Regression Analysis

Outlier There should be no significant outliers. Outliers are simply single data points within your data that do not follow the usual pattern. The problem with outliers is that they can have a negative effect on the regression equation that is used to predict the value of the dependent (outcome) variable based on the independent (predictor) variable.

Multiple Regression: Model Sum a. R tells the reliability & mathematical relationship. 1. R Square (co-efficient of determination) tells the percentage of accuracy. 2. Also percentage of variation that can not be controlled i.e. 3. (1-R Square) i. Adjusted R2, It can be negative & always less than or equal to R ii. Adjusted R2 will be more useful only if the R2 is calculated based on a sample, not the entire population iii. Adjusted R2 increases only if the new term improves the model more than would be expected by chance

ANOVA ANOVA table tests whether the overall regression model is a good fit for the data. p<0.05 The table shows that the independent variables statistically significantly predict the dependent variable, F(3, 16) = 32.811, p < .0005 (i.e., the regression model is a good fit of the data)

Coefficients

Summary. Use linear regression or correlation when you want to know whether one measurement variable is associated with another measurement variable; you ...

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Correlation and linear regression are not the same. What is the goal? Correlation quantifies the degree to which two variables are related.

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Correlation and linear regression are the most commonly used techniques for investigating the relationship between two quantitative variables.

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In statistics, simple linear regression is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional ...

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In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables ...

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This introductory statistics with R tutorial covers the topics of Correlation and Linear Regression.

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Regression and correlation analysis: Regression analysis involves identifying the relationship between a dependent variable and one or more independent ...

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In the single predictor case of linear regression, the standardized slope has the same value as the correlation coefficient. The advantage of the linear ...

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Open topic with navigation. Simple Linear Regression and Correlation Menu location: Analysis_Regression and Correlation_Simple Linear and Correlation.

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Correlation. Correlation is used to test for a relationship between two numeric variables or two ranked (ordinal) variables. In this tutorial, we assume ...

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