There Are Some Things Money Can't Buy You are interested in analyzing the determinants of credit card approvals. For now, you believe that someone's employment status (=1 if employed, 0 otherwise) is sufficient to explain credit approval. Approved is a dummy variable = 1 if approved, 0 otherwise.

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There Are Some Things Money Can't Buy
You are interested in analyzing the determinants of credit card approvals. For now, you believe that
someone's employment status (=1 if employed, 0 otherwise) is sufficient to explain credit approval.
Approved is a dummy variable = 1 if approved, 0 otherwise.
Source
Model
Residual
Total
approved
employed
_cons
SS
37.0161774
125.285046
162.301223
df
approved
employed
priordefault
37.0161774
1
652 .192154978
653
Coef. Std. Err.
MS
.480806 .0346418
.2513369 .0226668
.248547049
t P>|t|
13.88 0.000
11.09 0.000
corr approved employed priordefault
(obs=654)
Number of obs
F(1, 652)
Prob > F
R-squared
Adj R-squared
Root MSE
approved employed priord~t
1.0000
0.4776 1.0000
0.7190 0.4467 1.0000
=
=
=
=
A. What is the effect of being employed on approval odds?
(PERCENT or PERCENTAGE POINTS) Round to the nearest whole number. First box is the value,
second box is either PERCENT or PERCENTAGE POINTS.
B. Is this effect statistically significant at the 99% level of confidence? Answer either "YES" or "NO"
=
C. Your friend says that this is wrong, that the real regression is missing whether or not applicants
have defaulted in the past or not (that is, they failed to pay their debts). Let priordefault be a dummy
variable = 1 if the applicant defaulted in the past and O otherwise. You want to run this regression:
approved Bo + B₁ employed + B₂priordefault + u
You are also given this information about the relationship between the variables:
654
192.64
0.0000
0.2281
0.2269
.43835
[95% Conf. Interval]
.4127831 .5488288
.2068282 .2958456
If you omitted priordefault, does your original model in part (A) suffer from omitted variable bias for
your estimate of B₁? Answer "YES" or "NO". yes
D. Which direction is this bias? Answer either "UPWARD" or "DOWNWARD" upward
Transcribed Image Text:There Are Some Things Money Can't Buy You are interested in analyzing the determinants of credit card approvals. For now, you believe that someone's employment status (=1 if employed, 0 otherwise) is sufficient to explain credit approval. Approved is a dummy variable = 1 if approved, 0 otherwise. Source Model Residual Total approved employed _cons SS 37.0161774 125.285046 162.301223 df approved employed priordefault 37.0161774 1 652 .192154978 653 Coef. Std. Err. MS .480806 .0346418 .2513369 .0226668 .248547049 t P>|t| 13.88 0.000 11.09 0.000 corr approved employed priordefault (obs=654) Number of obs F(1, 652) Prob > F R-squared Adj R-squared Root MSE approved employed priord~t 1.0000 0.4776 1.0000 0.7190 0.4467 1.0000 = = = = A. What is the effect of being employed on approval odds? (PERCENT or PERCENTAGE POINTS) Round to the nearest whole number. First box is the value, second box is either PERCENT or PERCENTAGE POINTS. B. Is this effect statistically significant at the 99% level of confidence? Answer either "YES" or "NO" = C. Your friend says that this is wrong, that the real regression is missing whether or not applicants have defaulted in the past or not (that is, they failed to pay their debts). Let priordefault be a dummy variable = 1 if the applicant defaulted in the past and O otherwise. You want to run this regression: approved Bo + B₁ employed + B₂priordefault + u You are also given this information about the relationship between the variables: 654 192.64 0.0000 0.2281 0.2269 .43835 [95% Conf. Interval] .4127831 .5488288 .2068282 .2958456 If you omitted priordefault, does your original model in part (A) suffer from omitted variable bias for your estimate of B₁? Answer "YES" or "NO". yes D. Which direction is this bias? Answer either "UPWARD" or "DOWNWARD" upward
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