Fix grammatical errors in theory.md

Author: RevathyRamalingam

theory.md

@@ -97,7 +97,7 @@ y = B0 + B1*x1 + ... + Bn * xN
 There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.
 
 1. **Linear relationship** between features and target variable.
-2. **Additivity** means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items _a_ sold and the number of items _b_ sold. When company sells more items _a_ the revenue increases and this is independent of the number of items _b_ sold. But, if customers who buy _a_ stop buying _b_, the additivity assumption is violated.
+2. **Additivity** means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company has two features - the number of items _a_ sold and the number of items _b_ sold. When company sells more items _a_ the revenue increases and this is independent of the number of items _b_ sold. But, if customers who buy _a_ stop buying _b_, the additivity assumption is violated.
 3. Features are not correlated (no **collinearity**) since it can be difficult to separate out the individual effects of collinear features on the target variable.
 4. Errors are independently and identically normally distributed (y<sub>i</sub> = B0 + B1*x1<sub>i</sub> + ... + error<sub>i</sub>):
    1. No correlation between errors (consecutive errors in the case of time series data).