[url=https://wiki.geogebra.org/en/Normalize_Command]Normalize()[/url][math]\nearrow[/math][br][br]If there is a big differenz between x/y-data (e.g. percent x-values to ~10[sup]3[/sup] y-values) you may get polynom coefficients ~0 (see Regression Model in Scatterplot vs p[sub]0[/sub](x) ). Set rounding up to 15 decimal places to avoid rounding errors OR do[br][br]Normalization of data[br][br][math]\mathrm{x} \mapsto \frac{\mathrm{x}-\operatorname{Min}[\text { list] }}{\operatorname{Max}[\text { list] }- \text { Min[list] }}\quad by \;{normalize\; command} [/math][br][br]Functions for single value processing:[br][i]XNorm(t) do normalization of x-values [math]\mapsto[/math] [0, 1][br]YNorm(t) do normalization of y-values [math]\mapsto[/math] [0, 1][br]Yinvn(t) do denormalization of y-values[/i][br] [br]Fit with list of data, no normalization [br][i]p[sub]0[/sub](x) = 0.00003748588368154x² - 0.3256415866742x + 614.1874647092[/i][br][br]Fit with normalized data : normdata = Normalize(data)[br][i]p[sub]n[/sub](x) = 0.3908102766798x² - 1.430560227623x + 1.039570076342[/i][br][br][table][tr][td]x-value [math]\mapsto[/math] 1200 normalized: [/td][td][i]Xnorm(1200) = 1/3[/i][/td][td][/td][/tr][tr][td]regression y-value of normalized data: [/td][td][i]p[sub]n[/sub](Xnorm(1200)) = 0.6061400312094 [/i][/td][td][/td][/tr][tr][td]denormalized y-value of normalized data:[/td][td][i]Yinvn([i]p[sub]n[/sub](Xnorm(1200))[/i]) = 277.3972332016[/i][/td][td][/td][/tr][tr][td]y-value of regression no normalization: [/td][td][i]p[sub]0[/sub](1200)=277.3972332016[/i][/td][td][/td][/tr][/table]