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I would suggest that you create a plot with the outcome on the y-axis, prob_diff on the x-axis, and then you draw three lines (they will likely be curves) representing each of your conditions. You can do this by plugging in relevant values of prob_diff into the regression equation and calculating the probability of choice. I don't know if MATLAB has a ...


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Ordinary least squares (OLS) minimizes the residual sum of squares (RSS) $$ RSS=\sum_{i}\left( \varepsilon _{i}\right) ^{2}=\varepsilon ^{\prime }\varepsilon =\sum_{i}\left( y_{i}-\hat{y}_{i}\right) ^{2} $$ The mean squared deviation (in the version you are using it) equals $$ MSE=\frac{RSS}{n} $$ where $n$ is the number of observations. Since $n$ is a ...


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