Question 1

The following model   is referred to as a

Accepted Answer

A) curvilinear model 
B) curvilinear model with one predictor variable 
C) simple second-order model with one predictor variable 
D) simple first-order model with one predictor variable 
A) curvilinear model 
B) curvilinear model with one predictor variable 
C) simple second-order model with one predictor variable 
D) simple first-order model with one predictor variable

Question 2

A regression model relating a dependent variable, Y, with one independent variable, X1, resulted in an SSE of 400. Another regression model with the same dependent variable, Y, and two independent variables, X1 and X2, resulted in an SSE of 320. At $\alpha$ = .05, determine if X2 contributed significantly to the model. The sample size for both models was 20.

Accepted Answer

F = 4.25; p-value is between .

Question 3

Multiple regression analysis was used to study the relationship between a dependent variable, Y, and three independent variables X1, X2 and, X3. The following is a partial result of the regression analysis involving 20 observations.   
a.Compute the coefficient of determination.
b.Perform a t test and determine whether or not $\beta$1 is significantly different from zero ($\alpha$= 0.05).
c.Perform a t test and determine whether or not $\beta$2 is significantly different from zero ($\alpha$= 0.05).
d.Perform a t test and determine whether or not $\beta$3 is significantly different from zero ($\alpha$ = 0.05).
e.At $\alpha$ = 0.05, perform an F test and determine whether or not the regression model is significant.

Accepted Answer

a.0.42857
b.t = 5; p-value < .01; reject

Question 4

Exhibit 16-2
In a regression model involving 30 observations, the following estimated regression equation was obtained.   For this model, SSR = 1,740 and SST = 2,000.
-Refer to Exhibit 16-2. The value of SSE is

Accepted Answer

A) 3,740 
B) 170 
C) 260 
D) 2000 
A) 3,740 
B) 170 
C) 260 
D) 2000

Question 5

Exhibit 16-1
In a regression analysis involving 25 observations, the following estimated regression equation was developed.   Also, the following standard errors and the sum of squares were obtained.
SS1U1B1b1S1U1B0 = 3
SS1U1B1b2S1U1B0 = 6
SS1U1B1b3S1U1B0 = 7
SST = 4,800
SSE = 1,296
-Refer to Exhibit 16-1. If we are interested in testing for the significance of the relationship among the variables (i.e., significance of the model) the critical value of F at $\alpha$ = 0.05 is

Accepted Answer

A) 2.76 
B) 2.78 
C) 3.10 
D) 3.07 
A) 2.76 
B) 2.78 
C) 3.10 
D) 3.07

Question 6

Exhibit 16-2
In a regression model involving 30 observations, the following estimated regression equation was obtained.   For this model, SSR = 1,740 and SST = 2,000.
-Refer to Exhibit 16-2. The test statistic F for testing the significance of the above model is

Accepted Answer

A) 32.12 
B) 6.69 
C) 4.8 
D) 58 
A) 32.12 
B) 6.69 
C) 4.8 
D) 58

Question 7

Exhibit 16-3
Below you are given a partial computer output based on a sample of 25 observations.  
-Refer to Exhibit 16-3. The estimated regression equation is

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)

Question 8

Consider the following data for two variables x and y.   
a.An estimated regression equation of the form   was developed for the above data and the results are shown below. Comment on the adequacy of this equation for predicting y. Let $\alpha$ = .05.   
b.A regression equation of the form   was developed for the above data and results are shown below. Comment on the adequacy of this equation for predicting y. Let
$\alpha$ = .05.   
c.Predict the value of y when x = 5.

Accepted Answer

a.Linear @#IMG-DLM& = 3.3043 + 0.2618 x r² = 0.0931

Question 9

Exhibit 16-3
Below you are given a partial computer output based on a sample of 25 observations.  
-Refer to Exhibit 16-3. We want to test whether the parameter $\beta$2 is significant. The test statistic equals

Accepted Answer

A) 4 
B) 5 
C) 3 
D) -3 
A) 4 
B) 5 
C) 3 
D) -3

Question 10

Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?

Accepted Answer

A) a t test 
B) a Z test 
C) an F test 
D) a chi-square test 
A) a t test 
B) a Z test 
C) an F test 
D) a chi-square test

Question 11

Exhibit 16-3
Below you are given a partial computer output based on a sample of 25 observations.  
-Refer to Exhibit 16-3. The critical t value obtained from the table to test an individual parameter at the 5% level is

Accepted Answer

A) 2.06 
B) 2.069 
C) 2.074 
D) 2.080 
A) 2.06 
B) 2.069 
C) 2.074 
D) 2.080

Question 12

A regression analysis relating a company's sales, their advertising expenditure, price, and time resulted in the following.   
a.At 95% confidence, determine whether or not the regression model is significant. Fully explain how you arrived at your conclusion (give numerical reasoning) and what your answer indicates.
b.At 95% confidence determine which variables are significant and which are not. Explain how you arrived at your conclusion (Give numerical reasoning).
c.Fully explain the meaning of R-square, which is given in this model. Be very specific and give numerical explanation.

Accepted Answer

a.Since significance F = 0.0000 < @#LAT-DLM& = 0.0

Question 13

Consider the following data.   
a.Draw a scatter diagram. Does the relationship between X and Y appear to be linear?
b.Assume the relationship between X and Y can best be given by   Estimate the parameters of this curvilinear function.

Accepted Answer

The answer of Consider the following data.   
a.Draw...

Question 14

A model in the form of y = $\beta$0 + $\beta$1z1 + $\beta$2z2 + . . . +$\beta$pzp + $\beta$ where each independent variable zj (for j = 1, 2, . . ., p) is a function of xj . xj is known as the

Accepted Answer

A) general linear model 
B) general curvilinear model 
C) multiplicative model 
D) multiplicative curvilinear model 
A) general linear model 
B) general curvilinear model 
C) multiplicative model 
D) multiplicative curvilinear model

Question 15

Exhibit 16-4
In a laboratory experiment, data were gathered on the life span (Y in months) of 33 rats, units of daily protein intake (XS1U1B11S1U1B0), and whether or not agent XS1U1B12S1U1B0 (a proposed life extending agent) was added to the rats diet (XS1U1B12S1U1B0 = 0 if agent XS1U1B12S1U1B0 was not added, and XS1U1B12S1U1B0 = 1 if agent was added.) From the results of the experiment, the following regression model was developed.   Also provided are SSR = 60 and SST = 180.
-Refer to Exhibit 16-4. The multiple coefficient of determination is

Accepted Answer

A) 0.2 
B) 0.5 
C) 0.333 
D) 5 
A) 0.2 
B) 0.5 
C) 0.333 
D) 5

Question 16

A researcher is trying to decide whether or not to add another variable to his model. He has estimated the following model from a sample of 28 observations.   SSE = 1,425
SSR = 1,326
He has also estimated the model with an additional variable X3. The results are   SSE = 1,300
SSR = 1,451
What advice would you give this researcher? Use a .05 level of significance.

Accepted Answer

F = 2.308; p-value i

Question 17

A data set consisting of 7 observations of a dependent variable y and two independent variables x1 and x2 was used in a regression analysis. Using (x1) as the only independent variable, the following function is provided.   = 0.408 + 1.338x1
The SSE for the above model is 39.535.
Using both x1 and x2 as independent variables yields the following function.   = 0.805 + 0.498x1 - 0.477x2
The SSE for this latter function is 1.015.
Use an F test and determine if x2 contributes significantly to the model. Let $\alpha$ = 0.05.

Accepted Answer

F = 151.8 and F.05 = 7.71, sin

Question 18

Exhibit 16-2
In a regression model involving 30 observations, the following estimated regression equation was obtained.   For this model, SSR = 1,740 and SST = 2,000.
-Refer to Exhibit 16-2. The coefficient of determination for this model is

Accepted Answer

A) 0.6923 
B) 0.1494 
C) 0.1300 
D) 0.8700 
A) 0.6923 
B) 0.1494 
C) 0.1300 
D) 0.8700

Question 19

Forty-eight observations of a dependent variable (Y) and five independent variables resulted in an SSE of 438. When two additional independent variables were added to the model, the SSE was reduced to 375. At 95% confidence, determine whether or not the two additional independent variables contribute significantly to the model.

Accepted Answer

F = 3.36; p-value is between .

Question 20

Exhibit 16-2
In a regression model involving 30 observations, the following estimated regression equation was obtained.   For this model, SSR = 1,740 and SST = 2,000.
-Refer to Exhibit 16-2. The degrees of freedom associated with SSE are

Accepted Answer

A) 24 
B) 6 
C) 19 
D) 5 
A) 24 
B) 6 
C) 19 
D) 5

The following model is referred to as a

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The value of SSE is

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The test statistic F for testing the significance of the above model is

Exhibit 16-3 Below you are given a partial computer output based on a sample of 25 observations. -Refer to Exhibit 16-3. The estimated regression equation is

Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?

Exhibit 16-3 Below you are given a partial computer output based on a sample of 25 observations. -Refer to Exhibit 16-3. The critical t value obtained from the table to test an individual parameter at the 5% level is

Consider the following data. a.Draw a scatter diagram. Does the relationship between X and Y appear to be linear? b.Assume the relationship between X and Y can best be given by Estimate the parameters of this curvilinear function.

A model in the form of y = $\beta$ ₀ + $\beta$ ₁z₁ + $\beta$ ₂z₂ + . . . + $\beta$ _pz_p + $\beta$ where each independent variable z_j (for j = 1, 2, . . ., p) is a function of x_j . x_j is known as the

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The coefficient of determination for this model is

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The degrees of freedom associated with SSE are

Data and Statistics

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Descriptive Statistics: Numerical Measures

Introduction to Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling and Sampling Distributions

Interval Estimation

Hypothesis Tests

Inference About Means and Proportions With Two Populations

Inferences About Population Variances

Comparing Multiple Proportions, Test of Independence and Goodness of Fit

Experimental Design and Analysis of Variance

Simple Linear Regression

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Exam 16: Regression Analysis: Model Building

The following model is referred to as a

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The value of SSE is

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The test statistic F for testing the significance of the above model is

Exhibit 16-3 Below you are given a partial computer output based on a sample of 25 observations. -Refer to Exhibit 16-3. The estimated regression equation is

Exhibit 16-3 Below you are given a partial computer output based on a sample of 25 observations. -Refer to Exhibit 16-3. We want to test whether the parameter β\betaβ 2 is significant. The test statistic equals

Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?

Exhibit 16-3 Below you are given a partial computer output based on a sample of 25 observations. -Refer to Exhibit 16-3. The critical t value obtained from the table to test an individual parameter at the 5% level is

Consider the following data. a.Draw a scatter diagram. Does the relationship between X and Y appear to be linear? b.Assume the relationship between X and Y can best be given by Estimate the parameters of this curvilinear function.

A model in the form of y = β\betaβ 0 + β\betaβ 1z1 + β\betaβ 2z2 + . . . + β\betaβ pzp + β\betaβ where each independent variable zj (for j = 1, 2, . . ., p) is a function of xj . xj is known as the

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The coefficient of determination for this model is

Exhibit 16-2 In a regression model involving 30 observations, the following estimated regression equation was obtained. For this model, SSR = 1,740 and SST = 2,000. -Refer to Exhibit 16-2. The degrees of freedom associated with SSE are

Data and Statistics

Descriptive Statistics: Tabular and Graphical Displays

Descriptive Statistics: Numerical Measures

Introduction to Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling and Sampling Distributions

Interval Estimation

Hypothesis Tests

Inference About Means and Proportions With Two Populations

Inferences About Population Variances

Comparing Multiple Proportions, Test of Independence and Goodness of Fit

Experimental Design and Analysis of Variance

Simple Linear Regression

Multiple Regression

Time Series Analysis and Forecasting

Nonparametric Methods

Statistical Methods for Quality Control

Index Numbers

Decision Analysis on Website

Sample Survey on Website

Filters

A model in the form of y = $\beta$ ₀ + $\beta$ ₁z₁ + $\beta$ ₂z₂ + . . . + $\beta$ _pz_p + $\beta$ where each independent variable z_j (for j = 1, 2, . . ., p) is a function of x_j . x_j is known as the