Evaluate the model fit residuals and comment on their randomness using autocorrelation functions (ACFs), histogram and a normality plot (Use a four-in-one graph set along with residual ACFs).ĩ. Rerun the model and evaluate the fit again including error measures, R adjusted square, F value, slope coefficient significance, DW and VIF.Ĩ. Determine the best remedies for any of the problems identified in 5 above and make the appropriate changes to the regression model if required. Evaluate model fit with 2 error measures (RMSE and MAPE).Ħ. Multicollinearity with the VIF statisticħ. Heteroscedasticity with a residuals versus order plot (look for a megaphone effect)Ĭ. Autocorrelation (Serial correlation) with the DW statisticī. Investigate the best model using appropriate statistics or graphs to comment on possible:Ī. Rule– if the coefficient is not significant then you may not use the model to forecast.ĥ. Note the significance of each slope term in the model. Use R square and F as primary determinants of the best model. Note that is any seasonal dummy variables are used all of the seasonal dummy variables must be used. Use regression to evaluate the variable combinations to determine the best regression model. Use either Decomposition centered moving average of Y (CMA) for Y and seasonal indices (SI) to seasonally adjust the Y variable or use dummy X variables in regression.Ĥ. for Y variable seasonality or significant events) and include a table of the dummy variable values for regression analysis. Determine if the model requires dummy variables (e.g. Create a table for the Y, X and X transformed values.ģ. If they do, calculate the transformed values and create a scatter plot with a regression line and run a correlation with Y for each transformed X. Determine if any of the variables require transformation. Note any seasonality in the Y data with ACF (autocorrelation analysis of Y).Ģ.
Be sure to complete each part and write the responses supported by Minitab/excel work.īe sure to comment on each of the 10 points below.ġ. Unlock the value of your data with Minitab.Evaluate the forecast error measures- Microeconomicsĭevelop a good regression model with X variables in the regression equation. In 2017, Minitab acquired Salford Systems, a leading provider of advanced analytics which delivers a suite of powerful data mining, predictive analytics and modelling capabilities. Thousands of businesses and institutions worldwide use Minitab® Statistical Software, Minitab Engage™, and Quality Trainer® to uncover flaws in their processes and improve them. For over 40-years, Minitab has helped organizations drive cost containment, enhance quality, boost customer satisfaction and increase effectiveness. Combined with unparalleled ease-of-use, Minitab makes it simpler than ever to get deep insights from data. Minitab helps companies and institutions to spot trends, solve problems and discover valuable insights in data by delivering a comprehensive and best-in-class suite of statistical analysis and process improvement tools. Our mission is to help people discover valuable insights in their data.
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