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maestria_docs/06 MODELACION MATEMATICA 2024 (LUCY)/TAREA/salida Yuniel.rtf

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\viewkind4\uc1\pard\b\f0\fs20 X-Y Plot - peso fresco hojas vs. evaluacion dds\b0\f1\fs16
\par
\par Analysis Summary
\par
\par X variable: evaluacion dds
\par Y variable: peso fresco hojas
\par
\par 15 values
\par
\par
\par The StatAdvisor
\par ---------------
\par This procedure creates a scatterplot of peso fresco hojas versus
\par evaluacion dds. To fit a curve to this data, select Relate - Simple
\par Regression from the main menu.
\par
\par
\par
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\par
\par
\par Simple Regression - peso fresco hojas vs. evaluacion dds
\par
\par Regression Analysis - Linear model: Y = a + b*X
\par -----------------------------------------------------------------------------
\par Dependent variable: peso fresco hojas
\par Independent variable: evaluacion dds
\par -----------------------------------------------------------------------------
\par Standard T
\par Parameter Estimate Error Statistic P-Value
\par -----------------------------------------------------------------------------
\par Intercept -12,3615 7,51385 -1,64516 0,1239
\par Slope 1,44326 0,175905 8,20473 0,0000
\par -----------------------------------------------------------------------------
\par
\par
\par Analysis of Variance
\par -----------------------------------------------------------------------------
\par Source Sum of Squares Df Mean Square F-Ratio P-Value
\par -----------------------------------------------------------------------------
\par Model 12049,9 1 12049,9 67,32 0,0000
\par Residual 2327,01 13 179,001
\par -----------------------------------------------------------------------------
\par Total (Corr.) 14376,9 14
\par
\par Correlation Coefficient = 0,915501
\par R-squared = 83,8143 percent
\par R-squared (adjusted for d.f.) = 82,5692 percent
\par Standard Error of Est. = 13,3791
\par Mean absolute error = 10,2957
\par Durbin-Watson statistic = 0,711833 (P=0,0005)
\par Lag 1 residual autocorrelation = 0,5828
\par
\par The StatAdvisor
\par ---------------
\par The output shows the results of fitting a linear model to describe
\par the relationship between peso fresco hojas and evaluacion dds. The
\par equation of the fitted model is
\par
\par peso fresco hojas = -12,3615 + 1,44326*evaluacion dds
\par
\par Since the P-value in the ANOVA table is less than 0.01, there is a
\par statistically significant relationship between peso fresco hojas and
\par evaluacion dds at the 99% confidence level.
\par
\par The R-Squared statistic indicates that the model as fitted explains
\par 83,8143% of the variability in peso fresco hojas. The correlation
\par coefficient equals 0,915501, indicating a relatively strong
\par relationship between the variables. The standard error of the
\par estimate shows the standard deviation of the residuals to be 13,3791.
\par This value can be used to construct prediction limits for new
\par observations by selecting the Forecasts option from the text menu.
\par
\par The mean absolute error (MAE) of 10,2957 is the average value of
\par the residuals. The Durbin-Watson (DW) statistic tests the residuals
\par to determine if there is any significant correlation based on the
\par order in which they occur in your data file. Since the P-value is
\par less than 0.05, there is an indication of possible serial correlation.
\par Plot the residuals versus row order to see if there is any pattern
\par which can be seen.
\par
\par
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\par
\par
\par Polynomial Regression - peso fresco hojas versus evaluacion dds
\par
\par Polynomial Regression Analysis
\par -----------------------------------------------------------------------------
\par Dependent variable: peso fresco hojas
\par -----------------------------------------------------------------------------
\par Standard T
\par Nonlinear Regression - peso_fresco_hojasParameter Estimate Error Statistic P-Value
\par -----------------------------------------------------------------------------
\par CONSTANT 4,55717 9,06444 0,502753 0,6251
\par evaluacion dds -2,55775 1,19144 -2,14676 0,0550
\par evaluacion dds^2 0,16032 0,0434819 3,68704 0,0036
\par evaluacion dds^3 -0,00164573 0,000438196 -3,7557 0,0032
\par -----------------------------------------------------------------------------
\par
\par Analysis of Variance
\par -----------------------------------------------------------------------------
\par Source Sum of Squares Df Mean Square F-Ratio P-Value
\par -----------------------------------------------------------------------------
\par Model 13357,9 3 4452,64 48,06 0,0000
\par Residual 1019,03 11 92,6393
\par -----------------------------------------------------------------------------
\par Total (Corr.) 14376,9 14
\par
\par R-squared = 92,912 percent
\par R-squared (adjusted for d.f.) = 90,979 percent
\par Standard Error of Est. = 9,62493
\par Mean absolute error = 6,45156
\par Durbin-Watson statistic = 1,30911 (P=0,0066)
\par Lag 1 residual autocorrelation = 0,332456
\par
\par
\par
\par
\par The StatAdvisor
\par ---------------
\par The output shows the results of fitting a third order polynomial
\par model to describe the relationship between peso fresco hojas and
\par evaluacion dds. The equation of the fitted model is
\par
\par peso fresco hojas = 4,55717-2,55775*evaluacion dds +
\par 0,16032*evaluacion dds^2-0,00164573*evaluacion dds^3
\par
\par Since the P-value in the ANOVA table is less than 0.01, there is a
\par statistically significant relationship between peso fresco hojas and
\par evaluacion dds at the 99% confidence level.
\par
\par The R-Squared statistic indicates that the model as fitted explains
\par 92,912% of the variability in peso fresco hojas. The adjusted
\par R-squared statistic, which is more suitable for comparing models with
\par different numbers of independent variables, is 90,979%. The standard
\par error of the estimate shows the standard deviation of the residuals to
\par be 9,62493. This value can be used to construct prediction limits for
\par new observations by selecting the Forecasts option from the text menu.
\par The mean absolute error (MAE) of 6,45156 is the average value of the
\par residuals. The Durbin-Watson (DW) statistic tests the residuals to
\par determine if there is any significant correlation based on the order
\par in which they occur in your data file. Since the P-value is less than
\par 0.05, there is an indication of possible serial correlation. Plot the
\par residuals versus row order to see if there is any pattern which can be
\par seen.
\par
\par In determining whether the order of the polynomial is appropriate,
\par note first that the P-value on the highest order term of the
\par polynomial equals 0,00317827. Since the P-value is less than 0.01,
\par the highest order term is statistically significant at the 99%
\par confidence level. Consequently, you probably don't want to consider
\par any model of lower order.
\par
\par
\par
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}\b0\f1\fs16
\par
\par
\par
\par
\par Nonlinear Regression - peso_fresco_hojas
\par
\par
\par
\par
\par
\par
\par
\par Nonlinear Regression
\par --------------------
\par Dependent variable: peso_fresco_hojas
\par Independent variables:
\par evaluacion_dds
\par
\par Function to be estimated: a/(1+b*2.7182^(-k*evaluacion_dds))
\par Initial parameter estimates:
\par a = 70,0
\par b = 0,1
\par k = 0,0001
\par
\par Estimation method: Marquardt
\par Estimation stopped after maximum iterations reached.
\par Number of iterations: 31
\par Number of function calls: 152
\par
\par Estimation Results
\par ----------------------------------------------------------------------------
\par Asymptotic 95,0%
\par Asymptotic Confidence Interval
\par Parameter Estimate Standard Error Lower Upper
\par ----------------------------------------------------------------------------
\par a 73,7837 3,50418 66,1487 81,4186
\par b 979,585 1462,3 -2206,5 4165,67
\par k 0,199684 0,0494305 0,0919838 0,307384
\par ----------------------------------------------------------------------------
\par
\par Analysis of Variance
\par -----------------------------------------------------
\par Source Sum of Squares Df Mean Square
\par -----------------------------------------------------
\par Model 40669,8 3 13556,6
\par Residual 655,738 12 54,6448
\par -----------------------------------------------------
\par Total 41325,5 15
\par Total (Corr.) 14376,9 14
\par
\par R-Squared = 95,439 percent
\par R-Squared (adjusted for d.f.) = 94,6788 percent
\par Standard Error of Est. = 7,39221
\par Mean absolute error = 4,58125
\par Durbin-Watson statistic = 1,34144
\par Lag 1 residual autocorrelation = 0,309594
\par
\par Residual Analysis
\par ---------------------------------
\par Estimation Validation
\par n 15
\par MSE 54,6448
\par MAE 4,58125
\par MAPE
\par ME -0,169735
\par MPE
\par
\par
\par The StatAdvisor
\par ---------------
\par The output shows the results of fitting a nonlinear regression
\par model to describe the relationship between peso_fresco_hojas and 1
\par independent variables. The equation of the fitted model is
\par
\par 73,7837/(1+979,585*2.7182^(-0,199684*evaluacion_dds))
\par
\par In performing the fit, the estimation process was stopped before
\par convergence due to the maximum number of iterations having been
\par exceeded. To continue with the estimation process, set the initial
\par estimates of the unknown parameters to the current estimates and start
\par the estimation process again.
\par
\par The R-Squared statistic indicates that the model as fitted explains
\par 95,439% of the variability in peso_fresco_hojas. The adjusted
\par R-Squared statistic, which is more suitable for comparing models with
\par different numbers of independent variables, is 94,6788%. The standard
\par error of the estimate shows the standard deviation of the residuals to
\par be 7,39221. This value can be used to construct prediction limits for
\par new observations by selecting the Forecasts option from the text menu.
\par The mean absolute error (MAE) of 4,58125 is the average value of the
\par residuals. The Durbin-Watson (DW) statistic tests the residuals to
\par determine if there is any significant correlation based on the order
\par in which they occur in your data file.
\par
\par The output also shows aymptotic 95,0% confidence intervals for each
\par of the unknown parameters. These intervals are approximate and most
\par accurate for large sample sizes. You can determine whether or not an
\par estimate is statistically significant by examining each interval to
\par see whether it contains the value 0.0. Intervals covering 0.0
\par correspond to coefficients which may well be removed form the model
\par without hurting the fit substantially.
\par
\par
\par
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