Typo fix on GeoLift_Walkthrough.Rmd

vignettes/GeoLift_Walkthrough.Rmd

@@ -343,7 +343,7 @@ The results show that the campaigns led to a 5.4% lift in units sold correspondi
 summary(GeoTest)
 ```
 
-The summary show additional test statistics such as the p-value which was equal to 0.01 confirming the highly statistical significance of these results. Moreover, the summary function provides Balance Statistics which display data about our model's fit. The main metric of model fit used in GeoLift is the L2 Imbalance which represents how far our synthetic control was from the actual observed values in the pre-treatment period. That is, how similar the synthetic Chicago + Portland unit we crated is from the observed values of these cities in the period before the intervention. A small L2 Imbalance score means that our model did a great job replicating our test locations while a large one would indicate a poor fit. However, the L2 Imabalnce metric is scale-dependent, meaning that it can't be compared between models with different KPIs or number of testing periods. For instance, the L2 Imbalance of a model run on grams of units sold will be significantly larger than a model ran for tons of product sold even if they represent the same basic underlying metric.
+The summary show additional test statistics such as the p-value which was equal to 0.01 confirming the highly statistical significance of these results. Moreover, the summary function provides Balance Statistics which display data about our model's fit. The main metric of model fit used in GeoLift is the L2 Imbalance which represents how far our synthetic control was from the actual observed values in the pre-treatment period. That is, how similar the synthetic Chicago + Portland unit we crated is from the observed values of these cities in the period before the intervention. A small L2 Imbalance score means that our model did a great job replicating our test locations while a large one would indicate a poor fit. However, the L2 Imbalance metric is scale-dependent, meaning that it can't be compared between models with different KPIs or number of testing periods. For instance, the L2 Imbalance of a model run on grams of units sold will be significantly larger than a model ran for tons of product sold even if they represent the same basic underlying metric.
 
 Therefore, given that it's hard to tell whether the model had a good or poor fit by simply looking at the value of the L2 Imbalance metric, we also included the Scaled L2 Imbalance stat which is easier to interpret as it's bounded in the range between 0 and 1. A value close to zero represents a good model fit while values nearing 1 indicate a poor performance by the Synthetic Control Model. This scaling is accomplished by comparing the Scaled L2 Imbalance of our Synthetic Control Method with the Scaled L2 Imbalance obtained by a baseline/naive model (instead of carefully calculating which is the optimal weighting scheme for the Synthetic Control, we assign equal weights to each unit in the donor pool). The latter provides an upper bound of L2 Imbalance, therefore, the Scaled L2 Imbalance shows us how much better our GeoLift model is from the baseline.