Descriptor
| Algorithms | 10 |
| Computer Programs | 10 |
| Mathematics | 10 |
| Mathematical Models | 5 |
| Statistical Analysis | 4 |
| Correlation | 3 |
| Goodness of Fit | 3 |
| Mathematical Applications | 3 |
| Analysis of Variance | 2 |
| Computer Oriented Programs | 2 |
| Factor Analysis | 2 |
| More ▼ | |
Author
| Joreskog, Karl G. | 2 |
| Kirk, David B. | 2 |
| Claybrook, Billy G. | 1 |
| Jensema, Carl | 1 |
| Lederman, Barbara J. | 1 |
| Maier, Bruce | 1 |
| Manchester, Mark | 1 |
| Pennell, Roger | 1 |
| Urry, Vern W. | 1 |
| Van Thillo, Marielle | 1 |
Publication Type
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Download full textKirk, David B. – 1971
Improvements of the Gaussian quadrature in conjunction with the Newton-Raphson iteration technique (TM 000 789) are discussed as effective methods of calculating the bivariate normal correlation coefficient. (CK)
Descriptors: Algorithms, Computer Programs, Correlation, Mathematical Models
Lederman, Barbara J. – Journal of Computer-Based Instruction, 1975
Useful programming tricks are suggested to allow the reduction of many equations and non-polynomial-like expressions and the subsequent application of the mathematical algorithms. (Author)
Descriptors: Algebra, Algorithms, Computer Programs, Computer Science
Kirk, David B. – 1971
In this paper a reliable method is found for approximating the value of the Bivariate Normal Correlation Coefficient, rho, given values of the joint probability and the normal deviates, h and k, or the related areas. This technique finds useful application in the computation of the tetrachoric correlation coefficient, r, when the underlying…
Descriptors: Algorithms, Computer Programs, Correlation, Mathematical Applications
PDF pending restorationClaybrook, Billy G. – 1974
A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and…
Descriptors: Algorithms, Artificial Intelligence, Computer Programs, Computers
Joreskog, Karl G.; Van Thillo, Marielle – 1971
A new basic algorithm is discussed that may be used to do factor analysis by any of these three methods: (1) unweighted least squares, (2) generalized least squares, or (3) maximum likelihood. (CK)
Descriptors: Algorithms, Computer Programs, Correlation, Expectation
Peer reviewedMaier, Bruce – School Science and Mathematics, 1972
Descriptors: Algorithms, Computer Oriented Programs, Computer Programs, Geometric Concepts
Jensema, Carl; Urry, Vern W. – 1971
Procrustes rotation involves fitting a factor pattern matrix to a specified target matrix in factor analysis. These rotations are useful for the investigator who wishes to see how well his data can be made to fit a hypothesized factor pattern matrix. The mathematical problems involved in these transformations are outlined and computer algorithms…
Descriptors: Algorithms, Computer Programs, Factor Analysis, Factor Structure
Peer reviewedManchester, Mark – Mathematics Teacher, 1972
Descriptors: Algorithms, Computer Oriented Programs, Computer Programs, Decimal Fractions
Pennell, Roger – 1970
A model and a computer program for performing conjoint measurement is developed. (AG)
Descriptors: Algorithms, Analysis of Variance, Computer Programs, Goodness of Fit
Joreskog, Karl G. – 1970
A general method for estimating the unknown coefficients in a set of linear structural equations is described. In its most general form the method allows for both errors in equations (residuals, disturbances) and errors in variables (errors of measurement, observational errors) and yields estimates of the residual variance-covariance matrix and…
Descriptors: Algorithms, Analysis of Covariance, Analysis of Variance, Computer Programs
