Question 1

When the sample size n is large, the 90% confidence interval for ${ }^{\mu} Y$
 is

Accepted Answer

A)  $\bar { Y }$ ± 1.96SE( $\bar { Y }$ ). 
B)  $\bar { Y }$ ± 1.64SE( $\bar { Y }$ ). 
C)  $\bar { Y }$ ± 1.64 ${ } ^ { \sigma } Y$ 
D)  $\bar { Y }$ ± 1.96. 
A)  $\bar { Y }$ ± 1.96SE( $\bar { Y }$ ). 
B)  $\bar { Y }$ ± 1.64SE( $\bar { Y }$ ). 
C)  $\bar { Y }$ ± 1.64 ${ } ^ { \sigma } Y$ 
D)  $\bar { Y }$ ± 1.96.

Question 2

The p-value is defined as follows:

Accepted Answer

A) p = 0.05. 
B) PrH0 [   $\bar { Y }$ - µY,0     >   $\bar { Y }$ act - µY,0
    ]. 
C) Pr(z > 1.96). 
D) PrH0 [   $\bar { Y }$ - µY,0     act - µY,0
    ]. 
A) p = 0.05. 
B) PrH0 [   $\bar { Y }$ - µY,0     >   $\bar { Y }$ act - µY,0
    ]. 
C) Pr(z > 1.96). 
D) PrH0 [   $\bar { Y }$ - µY,0     act - µY,0
    ].

Question 3

In 2007, a study of close to 250,000 18-19 year-old Norwegian males found that first-borns have an IQ that is 2.3 points higher than those who are second-born. To see if you can find a similar evidence at your university, you collect data from 250 students, of which 140 are first-borns. After subjecting each of these individuals to an IQ test, you find that the first-borns score 108.3 with a standard deviation of 13.2, while the second borns achieve 107.1 with a standard deviation of 11.6. You hypothesize that first-borns and second-borns in a university population have identical IQs against the one-sided alternative hypothesis that first borns have higher IQs. Using a size of the test of 5%, what is your conclusion?

Accepted Answer

Given that your null hypothesis states @#IMG-DLM&_

Question 4

The formula for the sample variance is

Accepted Answer

A)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i } - \bar { Y } \right)$ 
B)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i} - \bar { Y } \right) ^ { 2 }$ 
C)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i } - { } ^ { \mu } Y \right) ^ { 2 }$ 
D)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n - 1 } \left( Y _ {i } - \bar { Y } \right) ^ { 2 }$ 
A)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i } - \bar { Y } \right)$ 
B)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i} - \bar { Y } \right) ^ { 2 }$ 
C)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \left( Y _ { i } - { } ^ { \mu } Y \right) ^ { 2 }$ 
D)  $S _ { y } ^ { 2 } = \frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n - 1 } \left( Y _ {i } - \bar { Y } \right) ^ { 2 }$

Question 5

The accompanying table lists the height (STUDHGHT)in inches and weight (WEIGHT)in pounds of five college students. Calculate the correlation coefficient.
$\begin{array}{ll}
\text { STUDHGHT}& \text { WEIGHT }\
\square\square\square\square\square\square\square\square\square&\square\square\square\square\square\
74 & 165 \
73 & 165 \
72 & 145 \
68 & 155 \
66 & 140
\end{array}$

Accepted Answer

The answer of The accompanying table lists the height (STUDHGHT)in...

When the sample size n is large, the 90% confidence interval for ${ }^{\mu} Y$ is

The p-value is defined as follows:

The formula for the sample variance is

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Hypothesis Tests and Confidence Intervals in Multiple Regression

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Assessing Studies Based on Multiple Regression

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Introduction to Time Series Regression and Forecasting

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Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

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Filters

Exam 3: Review of Statistics

When the sample size n is large, the 90% confidence interval for μY{ }^{\mu} YμY is

The p-value is defined as follows:

The formula for the sample variance is

Economic Questions and Data

Review of Probability

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Experiments and Quasi-Experiments

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters

When the sample size n is large, the 90% confidence interval for ${ }^{\mu} Y$ is