![]() It is possible to execute individual or groups of commands Stepping through a sequence of SHAZAM Commands. You may stop execution at any time by clicking the 'Stop' button.Ĭlick the 'Talk' button to set SHAZAM in Interactive Mode and execute each line in sequence each time the 'Return' key is pressed.Īdd commands at the end to interact directly with SHAZAM. Three methods of running SHAZAM commands are available with the Command Editor:Ĭlick 'Run' to execute the entire set of commands - your output will appear in the associated output window. The dataset will be automatically read into SHAZAM each time an analysis is run and the data may also be used with SHAZAM Wizards.įor more information please see the 'Working with Data' section in the User Guide. Adding the dataset to the current workspace and enabling the 'Automatic READ' feature.Programmatically using the SHAZAM commands READ, SAMPLE, SORT and GENR for variable and data file manipulation.Once a dataset is saved it may be used with other SHAZAM commands by: The SHAZAM Data Editor may be used to create, edit or open datasets for use with SHAZAM. To save the standard deviation of CONSUME in a variable called SDC for use in subsequent calculations and then show it, type the two commands: Should you wish to see descriptive statistics on the Textile data, simply type: Of the variables specified on the STAT command.Įg. ForĮxample, the PCOR option on the STAT command will print the correlation matrix ![]() There are many options available on the STAT command. To get simple statistics such as means, standard deviations, variances, minimums, and maximums for variables use the STAT Copy all the data or blocks of data.Ĭommand displays the results in the output viewer. Variable into which all previously listed variables will be copied. The last variable listed on the COPY command is the The COPY command copies vectors into matrices or Suppose you wish to create a matrix containing each variable of a Textile dataset as a column. #) is automatically substituted into the variable name. Which will produce variables named VAR1, VAR2, VAR3, VAR4, VAR5 where the DO loop indicator (eg. To create separately named variables from the columns of a matrix then you can use the powerful SHAZAM DO statement To create a vector from the first column of a matrix simply do:Įg. To solve the normal equations manually simply do:Įg. In SHAZAM matrices can be worked with using a natural syntax almost as easily as you write them on paper.Įg. Where the type= option describes the type ofĭistribution and where the DF= option specifies the degrees of freedom. You could then find the t-ratio probabilities by typing: Saved using the TRATIO option on the OLS command. You can use the DISTRIB command to get probabilities for t-ratios ![]() To GRAPH the variable Igm, the model predicted values (named "predicts") and the model residual (named "residual")Īgainst year all on the same graph, simply type:Ī graph will be created and will appear within SHAZAM.Īlternatively, write out the data using the WRITE command and then use the Graph Wizard.ĭouble click the graph to change graph properties.Ĭommand will compute the probability density function and the cumulativeĮg. SHAZAM produces graphs either using the easy to use Graph Wizard or by using the GRAPH You may write out the residuals to a file with the following command.Īn entry is automatically created in the current workspace with this file and you may double click on it to view these residuals within SHAZAM. Ols consume income price / resid=residual For example, to save the residuals on an OLS regression, With other options you may save the standard errors, predicted values, coefficients or residuals etc. For example, to print Analysis of Variance (ANOVA) tables, variance-covariance MATRIXĪnd residual summary statistics, simply add the ANOVA, and PCOV To run an Ordinary Least Squares regression with SHAZAM use the OLS command. To illustrate, let's consider a simple example: Ordinary Least Squares Regression SHAZAM includes a powerfully simple Command Language that makes complex analytical tasks easy to perform in repeatable ways.
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