# Linear Regression Intercept

## Function Prototype

/* Linear Regression Intercept */
/* Type: indicator */
/* Input arrays: 1    Options: 1    Output arrays: 1 */
/* Inputs: real */
/* Options: period */
/* Outputs: linregintercept */
int ti_linregintercept_start(TI_REAL const *options);
int ti_linregintercept(int size,
TI_REAL const *const *inputs,
TI_REAL const *options,
TI_REAL *const *outputs);

## Description

This documentation is still a work in progress. It has omissions, and it probably has errors too. If you see any issues, or have any general feedback, please get in touch.

The Linear Regression Intercept performs simple linear regresssion over a moving window and returns the Y-intercept.

It takes one parameter, the period n.

Linear Regression and Time Series Forecast both fit the same linear regression line. However, Linear Regression returns the height of the line above the last input bar, and Time Series Forecast returns the height of the line one bar past that. Linear Regression Intercept returns the height of the line for the first input bar in the moving period.

## Example Usage

### Calling From C

/* Example usage of Linear Regression Intercept */
/* Assuming that 'input' is a pre-loaded array of size 'in_size'. */
TI_REAL *inputs[] = {input};
TI_REAL options[] = {5}; /* period */
TI_REAL *outputs[1]; /* linregintercept */

/* Determine how large the output size is for our options. */
const int out_size = in_size - ti_linregintercept_start(options);

/* Allocate memory for output. */
outputs[0] = malloc(sizeof(TI_REAL) * out_size); assert(outputs[0] != 0); /* linregintercept */

/* Run the actual calculation. */
const int ret = ti_linregintercept(in_size, inputs, options, outputs);
assert(ret == TI_OKAY);


### Calling From Lua (with Tulip Chart bindings)

-- Example usage of Linear Regression Intercept
linregintercept = ti.linregintercept(input, 5)


## Example Calculation

period = 5

dateinputlinregintercept
2005-11-0181.59
2005-11-0281.06
2005-11-0382.87
2005-11-0483.00
2005-11-0783.6181.23
2005-11-0883.1581.75
2005-11-0982.8483.08
2005-11-1083.9983.08
2005-11-1184.5583.08
2005-11-1484.3682.95
2005-11-1585.5383.10
2005-11-1686.5483.78
2005-11-1786.8984.20
2005-11-1887.7784.58
2005-11-2187.2985.85

## Other Indicators

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