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Regression analysis

General

The regression analysis option in hymosincludes:

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The multiple linear functions can be fitted by means of multiple or step-wise regression techniques. The algebraic forms of the functions are presented in next section.

Series codes

Data of series with the same time interval, read from the database, can enter the regression analysis. The series can be selected by selecting a series in the series list box and pressing the '>>' button. To deselect one of the series, selected the series in the spreadsheet and press '<<'. For regression analysis between series a minimum of two series must be selected. When only one series is selected, a regression against time will be calculated. When selecting a series, the minimum and maximum values of the series for the defined time period will be calculated. Each series must be given a code.

Coding of variables

0: free variable available for regression
1: forced variable, which will enter regression
2: variable not entering regression
3: dependent variable (only one per selection )

Minimum value / Maximum value

The lower and upper boundaries for each series must be set; data outside these boundaries are eliminated; hymosshows default the minimum and maximum values.

Regression function

Choose a regression option from the list box by clicking the function.If you select <Polynomial> the degree of the polynomial has to be entered as well.
If you select <Stepwise> following data have to be entered:

• maximum number of steps in the stepwise regression analysis;
• whether or not the results of each step in the stepwise analysis have to be printed; if you enter <N>, only the results of the last step will be presented.
• F-values to enter and delete variables from the regression analysis, which only applies for the free variables .
  

  Save Relation

  Save the calculated relation for the time period defined in the Relation Validity Period. The start and end-time of this period can be changed by double-clicking the time-label. The coefficients of the regression equation will be stored in the data base. There are, however, some limitations to the number of coefficients that can be stored:
  
• in case of polynomial regression, the degree of the polynomial should be £4;
• in case of multiple/stepwise regression the number of independent variables should be £4.
  

  Regression equations

  The following types of regression equations are available, with Y the dependent variable and X~j~ 's the independent variables:
  

  Type	Equation
  Polynomial	Relation curves^image012.gif! with: n = degree of polynomial: n £4, C~j~ = coefficient
  Simple linear	Y~i~ = A + B*.X~i~ with: A,B = coefficients
  Exponential 1	Y~i~ = A exp(B*X~i~ ) with: A,B = coefficients
  Exponential 2	Y~i~ = A exp(B/X~i~ ) with: A,B = coefficients
  Power	Y~i~ = A.X~i~ B with: A,B = coefficients
  Logarithmic	Y~i~ = A + B.ln(X~i~ ) with: A,B = coefficients
  Hyperbolic	Y~i~ = A + B/X~i~ with: A,B = coefficients
  Multiple linear	Relation curves^image014.gif! with: n = number of series on independent variables, n£4 C~j~ = coefficient

  
  

  Computation procedure

  All coefficients are determined by the least squares method.
  The Multiple Linear equation may be established by multiple or by stepwise regression:
  
• In the multiple regression situation all independent variables enter the regression equation at one go.
• In the stepwise regression situation the independent variables enter the regression equation one by one. The order of entry may be:
• free: the variables to which this option applies are called free variables ; their entry is determined by statistical properties; the variable added is the one which makes the greatest improvement in goodness of fit. Among the free varia­bles only the significant variables are included in the final regression equation.
• predetermined: the variables to which this option applies are called forced variables ; i.e. their entry is not based on correlation but solely requested by the user.
  

  Confidence limits

  For the linear regression methods, confidence intervals can be computed. Based on the sampling distributions of the regression parameters, the following estimates and confidence limits hold (see e.g. Kottegoda and Rosso, 1998).
  If the linear regression function is as follows:
  
  where:
  Y = dependent variable, also called response variable,
  X = independent variable or explanatory variable,
  a,b= regression coefficients,
  e= residual because of imperfect match of regression function through measurement points.
  An unbiased estimate of the error variance is given by:

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