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# SLR_FORE

Calculates the forecast value, error and confidence interval for regression model.

## Syntax

**SLR**(

^{i}_FORE**X**,

**Y**,

**Intercept**,

**Target**,

**Return_type**,

**Alpha**)

**X**

is the independent (aka explanatory or predictor) variable data array (one dimensional array of cells (e.g. rows or columns)).

**Y**

is the response or the dependent variable data array (one dimensional array of cells (e.g. rows or columns)).

**Intercept**

is the constant or the intercept value to fix (e.g. zero). If missing, an intercept will not be fixed and is computed normally.

**Target**

is the value of the explanatory variable.

**Return_type**

is a switch to select the return output (1 = forecast (default), 2 = error, 3 = upper limit, 4 = lower limit).

Method | Description |
---|---|

1 | Mean Value |

2 | Standard Error |

3 | Upper Limit |

4 | Lower Limit |

**Alpha**

is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.

## Remarks

- The underlying model is described here.
- The SLR_FORE function computes the prediction interval (aka confidence interval) for a given value of the explanatory variable.
- The mean prediction values are computed by:

Where:

- is the conditional prediction mean value of .
- is the value of the explanatory variable.
- is the conditional expectation operator.

- The prediction error is driven by the regression mean error and the value of itself.

Where:

- is the number of observations.
- is the empirical sample average for the explanatory variable ().

- The sample data may include missing values.
- Each column in the input matrix corresponds to a separate variable.
- Each row in the input matrix corresponds to an observation.
- Observations (i.e. row) with missing values in X or Y are removed.
- The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
- The SLR_FORE function is available starting with version 1.60 APACHE.

## Examples

## References

- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Kenney, J. F. and Keeping, E. S. (1962) "Linear Regression and Correlation." Ch. 15 in Mathematics of Statistics, Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252-285