Question 1

Of the two main methods underlying nearly all of mathematical reasoning,statistics (particularly regression)is used when dealing with quantities that are certain.

Accepted Answer

A) True 
 B)False

Question 2

If the dependent variable is nominal data,then you would use Poisson regression for the regression analysis

Accepted Answer

A) True 
 B)False

Question 3

_______________ occurs when the error is not normally distributed.

Accepted Answer

A)  Non-normality 
B)  Heteroscedasticity 
C)  Autocorrelation 
D)  Multicollinearity

Question 4

-Which independent variable is,taken by itself,the best predictor of Gender?

Accepted Answer

A)  Age 
B)  Height 
C)  Weight 
D)  cannot be determined

Question 5

When using rank-ordered data,the regression model that will be used is the

Accepted Answer

A)  multinomial logit regression model 
B)  discrete choice model 
C)  exploded logit regression model 
D)  ordered probit model

Question 6

Use the regression output below to answer the following questions.
 Lingar Reperaidion Anthyis: Dep Var
 $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$
$\begin{array}{|c|c|c|c|c|c|}
\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}
\text { Std. } \
\text { Error }
\end{array} & \begin{array}{c}
\text { Std. } \
\text { Beta }
\end{array} & \begin{array}{c}
\boldsymbol{t} \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { Two Tailed }
\end{array} \
\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \
\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \
\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \
\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \
\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \
\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|}
\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \text { SE(Reg) } & \boldsymbol{n} \
\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|c|}
\hline \begin{array}{c}
\text { Source of } \
\text { Variation }
\end{array} & \begin{array}{c}
\text { Sum of } \
\text { Squares }
\end{array} & \text { df } & \begin{array}{c}
\text { Mean } \
\text { Squares }
\end{array} & \begin{array}{c}
F \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { One Tailed }
\end{array} \
\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \
\hline \text { Error } & 141282.23 & 442 & 319.643 & & \
\hline \text { Total } & 464874.47 & 447 & & & \
\hline
\end{array}$

-In using Poisson regression,the researcher also must know

Accepted Answer

A)  both the observed count and the number of opportunities 
B)  the relative percentages of each outcome 
C)  the distribution of the population 
D)  both the variance and the population mean

Question 7

Use the regression output below to answer the following questions.
 Lingar Reperaidion Anthyis: Dep Var
 $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$
$\begin{array}{|c|c|c|c|c|c|}
\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}
\text { Std. } \
\text { Error }
\end{array} & \begin{array}{c}
\text { Std. } \
\text { Beta }
\end{array} & \begin{array}{c}
\boldsymbol{t} \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { Two Tailed }
\end{array} \
\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \
\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \
\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \
\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \
\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \
\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|}
\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \text { SE(Reg) } & \boldsymbol{n} \
\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|c|}
\hline \begin{array}{c}
\text { Source of } \
\text { Variation }
\end{array} & \begin{array}{c}
\text { Sum of } \
\text { Squares }
\end{array} & \text { df } & \begin{array}{c}
\text { Mean } \
\text { Squares }
\end{array} & \begin{array}{c}
F \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { One Tailed }
\end{array} \
\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \
\hline \text { Error } & 141282.23 & 442 & 319.643 & & \
\hline \text { Total } & 464874.47 & 447 & & & \
\hline
\end{array}$

-According to the regression output above,if Age is increased by one year,what will the impact be on Weight?

Accepted Answer

A)  0.660 lb. increase 
B)  0.279 lb. increase 
C)  2.363 lb. increase 
D)  cannot be determined

Question 8

Although regression can verify a relationship between variables,it cannot quantify the nature of that relationship.

Accepted Answer

A) True 
 B)False

Question 9

One limitation to regression is that,due to latent variables,it is hard to know what variable should predict what.

Accepted Answer

A) True 
 B)False

Question 10

All of the following are limitations of regression except

Accepted Answer

A)  regression assumptions are usually violated 
B)  almost nothing is really linear 
C)  latent variables may be present 
D)  insufficient predictor variables

Question 11

The test for autocorrelation is the

Accepted Answer

A)  Durbin-Watson 
B)  Kolmogorov-Smirnov 
C)  Shapiro-Wilk 
D)  Anderson-Darling

Question 12

For multiple linear regression,researchers want to examine the r2 instead of the adjusted r2 because the r2 is not as easily fooled by additional independent variables.

Accepted Answer

A) True 
 B)False

Question 13

One of the limitations of regression is that it can be used only for linear relationships.

Accepted Answer

A) True 
 B)False

Question 14

If the error for one data point can help predict the value of the error in nearby data points,then the problem is

Accepted Answer

A)  non-normality 
B)  heteroscedasticity 
C)  autocorrelation 
D)  multicollinearity

Question 15

Substantial autocorrelation within data can be corrected for with myriad techniques,such as transformations and dummy variables.It should therefore not call into question the regression model itself.

Accepted Answer

A) True 
 B)False

Question 16

Regression is the dominant method of data analysis throughout the natural and social sciences.

Accepted Answer

A) True 
 B)False

Question 17

Regression,as a general method,is so versatile,through transformations and special cases,that it can never be overused.

Accepted Answer

A) True 
 B)False

Question 18

The error term in a binary regression using a logit model is assumed to be a

Accepted Answer

A)  normal distribution 
B)  Gumbel distribution 
C)  bivariate distribution 
D)  Poisson distribution

Question 19

Due to assumptions in linear regression about the distribution and mean of the error,the only population parameter that will need to be estimated to understand the extent of error is the

Accepted Answer

A)  autocorrelation of the error 
B)  standard deviation of the error 
C)  coefficient of determination 
D)  coefficient of variation

Question 20

If the dependent variable in a regression model is continuous interval data,the analysis should employ

Accepted Answer

A)  multiple linear regression 
B)  binary regression 
C)  Poisson or count regression 
D)  multinomial regression 
E)  ordinal regression

Exam 9: Multiple Regression: Modeling Multivariate Relationships

Of the two main methods underlying nearly all of mathematical reasoning,statistics (particularly regression)is used when dealing with quantities that are certain.

If the dependent variable is nominal data,then you would use Poisson regression for the regression analysis

_______________ occurs when the error is not normally distributed.

-Which independent variable is,taken by itself,the best predictor of Gender?

When using rank-ordered data,the regression model that will be used is the

Although regression can verify a relationship between variables,it cannot quantify the nature of that relationship.

One limitation to regression is that,due to latent variables,it is hard to know what variable should predict what.

All of the following are limitations of regression except

The test for autocorrelation is the

For multiple linear regression,researchers want to examine the r² instead of the adjusted r² because the r² is not as easily fooled by additional independent variables.

One of the limitations of regression is that it can be used only for linear relationships.

If the error for one data point can help predict the value of the error in nearby data points,then the problem is

Substantial autocorrelation within data can be corrected for with myriad techniques,such as transformations and dummy variables.It should therefore not call into question the regression model itself.

Regression is the dominant method of data analysis throughout the natural and social sciences.

Regression,as a general method,is so versatile,through transformations and special cases,that it can never be overused.

The error term in a binary regression using a logit model is assumed to be a

Due to assumptions in linear regression about the distribution and mean of the error,the only population parameter that will need to be estimated to understand the extent of error is the

If the dependent variable in a regression model is continuous interval data,the analysis should employ