An analyst working at production plant is asked to estimate the price of a chemical that is used in their production process. The chemical can be purchased from different suppliers. There are various attributes that quantify the quality of the particular chemical, and which may have an influence on its price. The analyst developed a linear regression model based these quality attributes in order to predict the price of the chemical. The linear regression modelling produced the following results:

Attribute	Coefficient	Std. Error	Std. Coefficient	Tolerance	p-Value
Density	5.383	2.201	0.549	0.362	0.015
Impurities	- 0.946	0.103	-0.338	1.000	0.0
Reactive efficiency	0.008	0.263	0.001	0.998	0.977
Packaged weight	-0.665	2.139	-0.070	0.371	0.756
(Intercept)	4516.427	63.806	?	?	0.0

1. The R-squared of the model was 0.242. Is this a good regression model? Justify your answer.
2. Explain how you would remove the attribute(s) that are not statistically significant or have a high likelihood of multi-collinearity in the regression in a RapidMiner process.