{"id":1403,"date":"2018-12-19T02:32:34","date_gmt":"2018-12-19T08:02:34","guid":{"rendered":"https:\/\/www.questionbang.com\/blog\/?p=1403"},"modified":"2020-01-08T03:55:31","modified_gmt":"2020-01-08T09:25:31","slug":"predicting-jee-cut-off","status":"publish","type":"post","link":"https:\/\/www.questionbang.com\/blog\/2018\/12\/19\/predicting-jee-cut-off\/","title":{"rendered":"Predicting JEE cut-off"},"content":{"rendered":"

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The cut-off is a popular word during the admission season, especially in India. For us at Questionbang, the cut-off is something often asked by mock-set-plus users – Has my score is good enough to qualify Joint Entrance Examination (JEE)? Hence, we decided to predict the cut-off for the next season and have it in\u00a0result analysis<\/a>.<\/p>\n

<\/code><\/p>\n

Background<\/strong><\/h3>\n

Most of us have used basic regression analysis during our class 12th maths, e.g, time series and forecasting. In reality, the outcomes of such predictions are going to be dependent on various factors. The use of simple regression may not be sufficient in such cases.<\/p>\n

Let us consider our requirement – predicting cut-off marks for JEE. The table below shows cut-off scores for the\u00a0last 6 years<\/span>.<\/p>\n

\"JEE<\/a>
Table 1. Cut-off scores for the last 6 years.<\/figcaption><\/figure>\n

 <\/p>\n

Let us try a simple curve fitting approach (Figure 1); this is giving a prediction of 69 for the year 2019. However, we cannot relate these points to any reasoning. As we can see, the\u00a0cut-off\u00a0(Y) was 113 for the year 2013 and became 74 in 2018 (Table 1). Surely many factors influence the cut-off.<\/p>\n

 <\/p>\n

\"scatter<\/a>
Figure 1. Scatter plot diagram for table 1.<\/figcaption><\/figure>\n

 <\/p>\n

\u00a0PRACTICE \u00a0JEE \u00a0ONLINE\u00a0 TESTS \u00a0 \u00a0>><\/strong><\/span><\/a><\/h3>\n

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Assumptions<\/strong><\/h3>\n

Let us assume those cut-offs (Table 1) are a measure of competition and hence, are a function of the following variables:<\/p>\n