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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.