deletions | additions       diff --git a/Seats distribution forecasting.md b/Seats distribution forecasting.md  index 87448f3..fddf1cc 100644  --- a/Seats distribution forecasting.md  +++ b/Seats distribution forecasting.md 
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The basic problem is how to standardize our voters sample, which, although consisting of over 7,000 voters, can be biased due to several factors such as age, socio-economical status, and party activist propaganda. Our current approach to control the sample biases was designed together with Omri Dor.  We started asking users for their 2013 elections choices on February 13th 2015. We used this information, together with the 2013 elections [official results](http://www.votes-19.gov.il/nationalresults).  First, we took only the latest vote for each device id, both from the 2013 and the 2015 datasets. Next, we calculated a counts matrix $C$ \[C\]  with rows for 2015 parties, columns for 2013 parties and values for the number of votes in each row-column combination. Thus, $C_{i,j}$ is the number of voters that voted for party $j$ in 2013 and will vote for party $i$ in 2015. Next, we used the counts matrix $C$ to estimate the transition matrix $M$ in which $M_{i,j}$ is the probability that an individual who voted for party $j$ in 2013 will vote for party $i$ in 2015.