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How to write up regression

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Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable or sometimes, the outcome variable. The variable we are using to predict the other variable's value is called the independent variable or sometimes, the predictor variable. For example, you could use linear regression to understand whether exam performance can be predicted based on revision time; whether cigarette consumption can be predicted based on smoking duration; and so forth.
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Linear Regression Algorithm from Scratch

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Logistic regression - Wikipedia

Company X had 10 employees take an IQ and job performance test. The resulting data -part of which are shown below- are in simple-linear-regression. The main thing Company X wants to figure out is does IQ predict job performance? And -if so- how? We'll answer these questions by running a simple linear regression analysis in SPSS. A great starting point for our analysis is a scatterplot.
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Simple linear regression

Published on February 19, by Rebecca Bevans. Revised on October 26, Regression models describe the relationship between variables by fitting a line to the observed data. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line.
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The survey included some statements regarding job satisfaction, some of which are shown below. The main research question for today is which factors contribute most to overall job satisfaction? The usual approach for answering this is predicting job satisfaction from these factors with multiple linear regression analysis. One of the best SPSS practices is making sure you've an idea of what's in your data before running any analyses on them.
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