Stock {{ if player.in_practice }} {{ player.round_number }}'s {{ else }} {{ paid_stock_letter }}'s {{ endif }} true EPS, \(T\), is unknown and drawn from a normal distribution with mean {{ C.T_MEAN }} and variance {{ C.T_VAR }}.
For this stock, the analysts' forecasts of the true EPS are generated in this way:
\(X_1 = T + \varepsilon_1\) where \(\varepsilon_1\)'s variance is {{ e1_var }}
{{ if alpha == 0 }}
\(X_2 = T + \varepsilon_2\)
{{ else }}
\(X_2 = {{ alpha }} \, X_1 + {{ one_minus_alpha }} \, T + \varepsilon_2\)
{{ endif }}
where \(\varepsilon_2\)'s variance is {{ e2_var }}.
The analysts' forecasts are shown in the table below:
| \(T\): Stock {{ if player.in_practice }} {{ player.round_number }}'s {{ else }} {{ paid_stock_letter }}'s {{ endif }} true EPS | Unknown |
| \(X_1\): Analyst 1's forecast of \(T\) | {{ player.x1 }} |
| \(X_2\): Analyst 2's forecast of \(T\) | {{ player.x2 }} |
| \(P\): Your prediction of \(T\) |
Please enter your prediction of Stock {{ if player.in_practice }} {{ player.round_number }}'s {{ else }} {{ paid_stock_letter }}'s {{ endif }} EPS above.
{{ formfield_errors 'p' }}If you wish to see the reward scheme, click on the "Scheme" button below. To see the experimental instructions again, click on the "Instructions" button. Also, you can review the statistical concepts by clicking on the "Statistical Concepts" button.