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Identify the complete relational schema for the final ERD below. You need to identify all relations, their attributes, Primary Keys (PK) and Foreign Keys (FK) keys, entering them into the blank  boxes according to the following example: Example: PROJECT(pno, dno) PK: (pno)  FK: (dno) First, enter name of relation and the attributes; then the value(s) for the PK and then values for the FK (if any). Relations fit on specific boxes. Look carefully how to place the relations in the boxes provided so all the responses could fit (attributes, PKs, FKs). NOTE: Use exactly the same spelling and letter case, otherwise it will be not recognized correctly by Carmen.  R1 is [R1] ([a1],[a2],[a3]), PK:([pk1]), FK:([fk1]) R2 is [R2] ([b1],[b2]), PK:([pk2]) R3 is [R3] ([c1],[c2],[c3]),         PK:([pkc1], [pkc2],[pkc3]),         FK: ([fkc1]) R4 is [R4] ([d1]),          PK:([pkd1]), R5 is [R5] ([e1],[e2]),         PK:([pke1], [pke2]),         FKs: ([fke1],[fke2])  

A firm in a competitive market is earning no economic profit in the short run.  What would one expect

7. The adjusted R2 statistic can get smaller if non-significant terms are added to a model.

4. In an unreplicated design the degrees of freedom associated with the ‘pure error’ component of error is zero.

Use the following information to answer Questions 21 – 24. Round all answers to two decimal places.  Question 21: Construct a 95% confidence interval for the regression coefficient for X1. The LCL is ____________ .  

A quadratic term in a model can result in a mound or a bowl in a response surface. 

Construct a 95% prediction interval for a new observation at .  The LPL is _______________.

If the Hanning filter was applied to the time series data in Question 3, the new dataset would have [value1] observations and the value of would be  [value2]. 

The advantage of the REML method is that it provides the standard errors of the variance components so that confidence intervals can be easily obtained. 

The PRESS statistic is a good way to judge the potential predictive capability of a model.