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Having performanced a linear regression in R with the lm function, I'm not sure how to interpret the results for the Intercept (as shown below).
It seems the probability of the intercept's relevance is low (i.e. Pr(>|t|) is 0.845, and higher that 0.05). Does this mean I should drop the intercept from the model by forcing it through zero? Alternatively, does it mean that I should still keep the intercept but recognise that it's not significant?
Call: lm(formula = DI ~ II) Residuals: Min 1Q Median 3Q Max -0.23960 -0.03306 -0.01116 008724 0.20568 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.07952 0.39953 -0.199 0.845 II 0.86381 0.04593 18.809 8.23e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1346 on 13 degrees of freedom Multiple R-squared: 0.9646, Adjusted R-squared: 0.9618 F-statistic: 353.8 on 1 and 13 DF, p-value: 8.23e-11
Additional Background Information My overall aim is to find a relationship between two datasets of mass that I have. So, if I have a value for DI, I am able to find out the corresponding value of II.