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The TI-83 was the first calculator in the TI series to have built-in assembly language support. The TI-92, TI-85, and TI-82 were capable of running assembly language programs, but only after sending a specially constructed (hacked) memory backup. The support on the TI-83 could be accessed through a hidden feature of the calculator.
Bayesian linear regression applies the framework of Bayesian statistics to linear regression. (See also Bayesian multivariate linear regression .) In particular, the regression coefficients β are assumed to be random variables with a specified prior distribution .
It also depends on the TI calculator character set because it is tokenized. [2]: 25 Aside from these differences, TI-BASIC retains most control flow statements: conditionals, various loops, GOTOs and Labels. Conditionals and loops use End to denote the end of their bodies. Each command can be placed on a new line, or separated by a colon for ...
In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...
TI-BASIC is the official [1] name of a BASIC-like language built into Texas Instruments' graphing calculators. TI-BASIC is a language family of three different and incompatible versions, released on different products: TI-BASIC 83 (on Z80 processor) for TI-83 series, TI-84 Plus series; TI-BASIC 89 (on 68k processor) for TI-89 series, TI-92 ...
Deming regression (total least squares) also finds a line that fits a set of two-dimensional sample points, but (unlike ordinary least squares, least absolute deviations, and median slope regression) it is not really an instance of simple linear regression, because it does not separate the coordinates into one dependent and one independent ...
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.