Question

# 1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=21.0213; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950.According to these results the relationship between C and Y is:

• A. no relationship
• B. impossible to tell
• C. positive
• D. negative

2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. The t-ratio for the regression slope coefficient is

• A. 0.959254
• B. 0.0697828
• C. -3.2675
• D. 21.0213

1. We have given b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=21.0213; R-squared=0.130357; and SER=8.769363.

The total number of observations is 2950. the value of correlatoon coefficient is   = 0.361

D positive

2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively.

the value of t for b1 is 1.46693/21.0213 = 0.0697828

• B. 0.0697828

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