Question

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

Answer 1

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:

ANS: C) POSITIVE

EXPLANATION:

The relationship between the dependent and independent variable is indicated by the sign of the coefficent. so in the given model the relationship between the Ci (dependent variable) and Yi (independent variable) is indicated by the coefficient sign (β1). here the coefficient value is 1.46693 which is a positive value, so this indicate a positive relationship between the Ci and Yi. We can interpret the β1 as one unit increase in the Yi increases Ci by 1.46693.

why it is option c) positive why not others??

Ci has some positive relationship with Yi so there exists some relationship, so option A) no relationship is not a correct answer

we can predict the relationship between the dependent and indepedent value by seeing the sign of coefficient, so option B) impossible to tell is not a correct answer.

so here positive is the correct answer, it shows a positive relationship so it cannot be negative so option d) negative cannot be a correct answer.

CORRECT ANSWER IS POSITIVE RELATIONSHIP.

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

t-ratio for the regression slope coefficient is

correct answer is D) 21.0213

explanation:

T ratio is the estimate divided by the standard error. Here we want to find the t-ratio for slope coefficient here we want to divide b1 by standard error of b1. so here we want to divide the estimate of Yi ( slope coeffient) by the standard error of Yi.

so the answer is 21.0213, so it is the right option it cannot be any other value.