Description Usage Arguments Details Value Author(s) References See Also Examples

`ols`

can be used to calculate the values of Ordinary Least Square Estimated values and corresponding scaler Mean Square Error (MSE) value.

1 |

`formula` |
in this section interested model should be given. This should be given as a |

`data` |
an optional data frame, list or environment containing the variables in the model. If not found in |

`na.action` |
if the dataset contain |

`...` |
currently disregarded. |

Since formula has an implied intercept term, use either `y ~ x - 1`

or `y ~ 0 + x`

to remove the intercept.

If there is any dependence present among the independent variables (multicollinearity) then it will be indicated as a warning massage. In case of multicollinearity Ordinary Least Square Estimators are not the best estimators.

`ols`

returns the Ordinary Least Square Estimated values, standard error values, t statistic values,p value and corresponding scalar MSE value. In addition if the dataset contains multicollinearity then it will be indicated as a warning massage.

P.Wijekoon, A.Dissanayake

Nagler, J. (Updated 2011) Notes on Ordinary Least Square Estimators.

1 2 3 |

```
$`*****Ordinary Least Square Estimator******`
Estimate Standard_error t_statistic p_value
X1 2.1930 0.1853 11.8367 0.000
X2 1.1533 0.0479 24.0565 0.000
X3 0.7585 0.1595 4.7551 0.001
X4 0.4863 0.0414 11.7443 0.000
$`*****Mean square error value*****`
MSE
0.0638
Warning message:
In ols(Y ~ X1 + X2 + X3 + X4 - 1, data = pcd) :
There is a multicollinearity
```

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