Question 1

One use of VIF in multiple regression is deciding which variable to include in a model.

Accepted Answer

A) True 
 B)False

Question 2

Collinearity is present if the dependent variable is linearly related to one of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 3

The Variance Inflationary Factor (VIF)measures the

Accepted Answer

A)  standard deviation of the slope. 
B)  contribution of each X variable with the Y variable after all other X variables are included in the model. 
C)  correlation of the X variables with the Y variable. 
D)  correlation of the X variables with each other. 
A)  standard deviation of the slope. 
B)  contribution of each X variable with the Y variable after all other X variables are included in the model. 
C)  correlation of the X variables with the Y variable. 
D)  correlation of the X variables with each other.

Question 4

A certain type of rare gem serves as a status symbol for many of its owners. In theory, for low prices, the demand decreases as the price of the gem increases. However, experts hypothesise that when the gem is valued at very high prices, the demand increases with price due to the status owners believe they gain in obtaining the gem. Thus, the model proposed to best explain the demand for the gem by its price is the quadratic model:
 $$Y = B _ { 0 } + B _ { 1 } X + B_ { 2 } X ^ { 2 } + \varepsilon$$ 
where Y = demand (in thousands) and X = retail price per carat.
 $$\begin{array} { | l | l | l | l | l | l | } 
\hline \text { SUMMARY } & \text { OUTPUT } & & & & \
\hline \text { Regression } & \text { Statistics } & & & & \
\hline \text { Multiple R } & & 0.994 & & & \
\hline \text { R Square } & & 0.988 & & & \
\hline \text { Std. Error } & & 12.42 & & & \
\hline \text { Observations } & & 12 & & & \
\hline & & & & & \
\hline \text { ANOVA } & & & & & \
\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Signif F } \
\hline \text { Regression } & 2 & 115,145 & 57,573 & 373 & 0.0001 \
\hline \text { Residual } & 9 & 1,388 & 154 & & \
\hline \text { Total } & 11 & 116,533 & & & \
\hline & & & & & \
\hline & \text { Coeff } & \text { Std. Error } & \text { t Stat } & P \text {-Value } & \
\hline \text { Intercept } & 286.42 & 9.66 & 29.64 & 0.0001 & \
\hline \text { Price } & - 0.31 & 0.06 & - 5.14 & 0.0006 & \
\hline \text { Price Sq } & 0.000067 & 0.00007 & 0.95 & 0.3647 & \
\hline
\end{array}$$ This model was fit to data collected for a sample of 12 rare gems of this type. A portion of the computer analysis obtained from Microsoft Excel is shown below:
-Referring to Instruction 16-1,what is the value of the test statistic for testing whether the quadratic term is necessary in fitting in the response curve relating the demand (Y)and the price (X)?

Accepted Answer

A)  -5.14 
B)  0.95 
C)  373 
D)  None of the above 
A)  -5.14 
B)  0.95 
C)  373 
D)  None of the above

Question 5

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,the variable X2 should be dropped to remove collinearity.

Accepted Answer

A) True 
 B)False

Question 6

A real estate developer wishes to determine how house size (House)is influenced by family income (Income),family size (Size),and education of the head of household (School).House size is measured in hundreds of square metres,income is measured in thousands of dollars and education is in years.The developer randomly selected 50 families and developed a multiple regression model.The business literature involving human capital shows that education influences an individual's annual income.Combined,these may influence family size.With this in mind,what should the real estate developer be particularly concerned with when analysing the multiple regression model?

Accepted Answer

A)  Normality of residuals 
B)  Collinearity 
C)  Missing observations 
D)  Randomness of error terms 
A)  Normality of residuals 
B)  Collinearity 
C)  Missing observations 
D)  Randomness of error terms

Question 7

Instruction 16-2
A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of the drug and recorded the time to relief (Y) measured in seconds for each. She fit a quadratic model to this data. The results obtained by Microsoft Excel follow.
 $\begin{array}{|c|c|c|c|c|c|}
\hline & \text { OUTPU? } & & & & \
\hline \text { Regression } & \text { Statistics } & & & & \
\hline \text { Multiple R } & & 0.747 & & & \
\hline \text { R Square } & & 0.558 & & & \
\hline \text { Adj. R Square } & & 0.478 & & & \
\hline \text { Std. Error } & & 863.1 & & & \
\hline \text { Observations } & & 14 & & & \
\hline & & & & & \
\hline \text { ANOVA } & & & & & \
\hline & d f & \text { Ss } & \text { MS } & F & \text { Signif F } \
\hline \text { Regression } & 2 & 10,344,797 & 5,172,399 & 6.94 & 0.0110 \
\hline \text { Residual } & 11 & 8,193,929 & 744,903 & & \
\hline \text { Total } & 13 & 18,538,726 & & & \
\hline & & & & & \
\hline & \text { Coeff } & \text { Std. Error } & \text { t stat } & P \text {-value } & \
\hline \text { Intercept } & 1283.0 & 352.0 & 3.65 & 0.0040 & \
\hline \text { CenDose } & 25.228 & 8.631 & 2.92 & 0.0140 & \
\hline \text { CenDoseSq } & 0.8604 & 0.3722 & 2.31 & 0.0410 & \
\hline
\end{array}$
 Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 16-2,suppose the chemist decides to use an F test to determine if there is a significant quadratic relationship between time and dose.If she chooses to use a level of significance of 0.05,she would decide that there is a significant curvilinear relationship.

Accepted Answer

A) True 
 B)False

Question 8

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,the variable X6 should be dropped to remove collinearity.

Accepted Answer

A) True 
 B)False

Question 9

The logarithm transformation can be used

Accepted Answer

A)  to test for possible violations to the autocorrelation assumption. 
B)  to overcome violations to the autocorrelation assumption. 
C)  to overcome violations to the homoscedasticity assumption. 
D)  to test for possible violations to the homoscedasticity assumption. 
A)  to test for possible violations to the autocorrelation assumption. 
B)  to overcome violations to the autocorrelation assumption. 
C)  to overcome violations to the homoscedasticity assumption. 
D)  to test for possible violations to the homoscedasticity assumption.

Question 10

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,what is the value of the variance inflationary factor of Manager?

Accepted Answer

The answer of Instruction 16-6
Given below are results from the...

Question 11

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,what is the value of the Mallow's Cp statistic for the model that includes X1,X3,X5 and X6?

Accepted Answer

Question 12

The stepwise regression approach takes into consideration all possible models.

Accepted Answer

A) True 
 B)False

Question 13

Instruction 16-2
A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of the drug and recorded the time to relief (Y) measured in seconds for each. She fit a quadratic model to this data. The results obtained by Microsoft Excel follow.
 $\begin{array}{|c|c|c|c|c|c|}
\hline & \text { OUTPU? } & & & & \
\hline \text { Regression } & \text { Statistics } & & & & \
\hline \text { Multiple R } & & 0.747 & & & \
\hline \text { R Square } & & 0.558 & & & \
\hline \text { Adj. R Square } & & 0.478 & & & \
\hline \text { Std. Error } & & 863.1 & & & \
\hline \text { Observations } & & 14 & & & \
\hline & & & & & \
\hline \text { ANOVA } & & & & & \
\hline & d f & \text { Ss } & \text { MS } & F & \text { Signif F } \
\hline \text { Regression } & 2 & 10,344,797 & 5,172,399 & 6.94 & 0.0110 \
\hline \text { Residual } & 11 & 8,193,929 & 744,903 & & \
\hline \text { Total } & 13 & 18,538,726 & & & \
\hline & & & & & \
\hline & \text { Coeff } & \text { Std. Error } & \text { t stat } & P \text {-value } & \
\hline \text { Intercept } & 1283.0 & 352.0 & 3.65 & 0.0040 & \
\hline \text { CenDose } & 25.228 & 8.631 & 2.92 & 0.0140 & \
\hline \text { CenDoseSq } & 0.8604 & 0.3722 & 2.31 & 0.0410 & \
\hline
\end{array}$
 Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 16-2,suppose the chemist decides to use a t test to determine if there is a significant difference between a linear model and a quadratic model that includes a linear term.If she used a level of significance of 0.01,she would decide that the linear model is sufficient.

Accepted Answer

A) True 
 B)False

Question 14

A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and records information in millions of dollars.A statistical analyst discovers that capital spending by corporations has a significant inverse relationship with wage spending.What should the microeconomist who developed this multiple regression model be particularly concerned with?

Accepted Answer

A)  Normality of residual 
B)  Randomness of error terms 
C)  Missing observations 
D)  Collinearity 
A)  Normality of residual 
B)  Randomness of error terms 
C)  Missing observations 
D)  Collinearity

Question 15

It is unnecessary to examine several alternative models using best-subsets regression.

Accepted Answer

A) True 
 B)False

Question 16

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,the model that includes X1,X5 and X6 should be selected using the adjusted r2 statistic.

Accepted Answer

A) True 
 B)False

Question 17

Instruction 16-2
A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of the drug and recorded the time to relief (Y) measured in seconds for each. She fit a quadratic model to this data. The results obtained by Microsoft Excel follow.
 $\begin{array}{|c|c|c|c|c|c|}
\hline & \text { OUTPU? } & & & & \
\hline \text { Regression } & \text { Statistics } & & & & \
\hline \text { Multiple R } & & 0.747 & & & \
\hline \text { R Square } & & 0.558 & & & \
\hline \text { Adj. R Square } & & 0.478 & & & \
\hline \text { Std. Error } & & 863.1 & & & \
\hline \text { Observations } & & 14 & & & \
\hline & & & & & \
\hline \text { ANOVA } & & & & & \
\hline & d f & \text { Ss } & \text { MS } & F & \text { Signif F } \
\hline \text { Regression } & 2 & 10,344,797 & 5,172,399 & 6.94 & 0.0110 \
\hline \text { Residual } & 11 & 8,193,929 & 744,903 & & \
\hline \text { Total } & 13 & 18,538,726 & & & \
\hline & & & & & \
\hline & \text { Coeff } & \text { Std. Error } & \text { t stat } & P \text {-value } & \
\hline \text { Intercept } & 1283.0 & 352.0 & 3.65 & 0.0040 & \
\hline \text { CenDose } & 25.228 & 8.631 & 2.92 & 0.0140 & \
\hline \text { CenDoseSq } & 0.8604 & 0.3722 & 2.31 & 0.0410 & \
\hline
\end{array}$
 Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 16-2,the prediction of time to relief for a person receiving a dose of the drug 10 units above the average dose is _______.

Accepted Answer

The answer of Instruction 16-2
A chemist employed by a pharmaceutical...

Question 18

Instruction 16-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are the age of the worker (XS1U1B11S1U1B0), the number of years of education received (XS1U1B12S1U1B0), the number of years at the previous job (XS1U1B13S1U1B0), a dummy variable for marital status (XS1U1B14S1U1B0: 1 = married, 0 = otherwise), a dummy variable for head of household (XS1U1B15S1U1B0: 1 = yes, 0 = no) and a dummy variable for management position (XS1U1B16S1U1B0: 1 = yes, 0 = no).
The coefficient of multiple determination (RS1U1P12S1S1P0S1U1B1jS1U1B0) the regression model using each of the 6 variables XS1U1B1j S1U1B0as the dependent variable and all other X variables as independent variables are, respectively, 0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.
The partial results from best-subset regression are given below:
 $$\begin{array} { | l | l | l | l | } 
\hline \text { Model } & \text { R Square } & \text { Adj. R Square } & \text { Std. Error } \
\hline X _ { 1 } X _ { 5 } X _ { 6 } & 0.4568 & 0.4116 & 18.3534 \
\hline X _ { 1 } X _ { 2 } X _ { 5 } X _ { 6 } & 0.4697 & 0.4091 & 18.3919 \
\hline X _ { 1 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4691 & 0.4084 & 18.4023 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 5 } X _ { 6 } & 0.4877 & 0.4123 & 18.3416 \
\hline X _ { 1 } X _ { 2 } X _ { 3 } X _ { 4 } X _ { 5 } X _ { 6 } & 0.4949 & 0.4030 & 18.4861 \
\hline
\end{array}$$
-Referring to Instruction 16-6,what is the value of the Mallow's Cp statistic for the model that includes X1,X2,X5 and X6?

Accepted Answer

Question 19

If a person uses a multiple regression model when it is clear that model assumptions have not been met,this is a violation of ethics.

Accepted Answer

A) True 
 B)False

Question 20

Which of the following will NOT change a nonlinear model into a linear model?

Accepted Answer

A)  Logarithmic transformation 
B)  Square root transformation 
C)  Variance inflationary factor 
D)  Quadratic regression model 
A)  Logarithmic transformation 
B)  Square root transformation 
C)  Variance inflationary factor 
D)  Quadratic regression model

One use of VIF in multiple regression is deciding which variable to include in a model.

Collinearity is present if the dependent variable is linearly related to one of the explanatory variables.

The Variance Inflationary Factor (VIF)measures the

The logarithm transformation can be used

The stepwise regression approach takes into consideration all possible models.

It is unnecessary to examine several alternative models using best-subsets regression.

If a person uses a multiple regression model when it is clear that model assumptions have not been met,this is a violation of ethics.

Which of the following will NOT change a nonlinear model into a linear model?

Defining and Collecting Data

Organising and Visualising Data

Numerical Descriptive Measures

Basic Probability

Some Important Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Hypothesis Testing: Two-Sample Tests

Analysis of Variance

Simple Linear Regression

Introduction to Multiple Regression

Time-Series Forecasting and Index Numbers

Chi-Square Tests

Decision Making

Statistical Applications in Quality Management

Further Non-Parametric Tests

Filters

Exam 16: Multiple Regression Model Building

One use of VIF in multiple regression is deciding which variable to include in a model.

Collinearity is present if the dependent variable is linearly related to one of the explanatory variables.

The Variance Inflationary Factor (VIF)measures the

The logarithm transformation can be used

The stepwise regression approach takes into consideration all possible models.

It is unnecessary to examine several alternative models using best-subsets regression.

If a person uses a multiple regression model when it is clear that model assumptions have not been met,this is a violation of ethics.

Which of the following will NOT change a nonlinear model into a linear model?

Defining and Collecting Data

Organising and Visualising Data

Numerical Descriptive Measures

Basic Probability

Some Important Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Hypothesis Testing: Two-Sample Tests

Analysis of Variance

Simple Linear Regression

Introduction to Multiple Regression

Time-Series Forecasting and Index Numbers

Chi-Square Tests

Decision Making

Statistical Applications in Quality Management

Further Non-Parametric Tests

Filters