Question 1

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

Accepted Answer

A) True 
 B)False

Question 2

Given the least squares regression line, $\hat { Y } = 8 - 3 X$

Accepted Answer

A)  the relationship between X and Y is positive 
B)  the relationship between X and Y is negative 
C)  as X increases,so does Y 
D)  as X decreases,so does Y 
E)  there is no relationship between X and Y 
A)  the relationship between X and Y is positive 
B)  the relationship between X and Y is negative 
C)  as X increases,so does Y 
D)  as X decreases,so does Y 
E)  there is no relationship between X and Y

Question 3

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric

Accepted Answer

A) True 
 B)False

Question 4

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $\hat { Y }$ 
= 84 +7X.This implies that if there is no advertising,then the predicted amount of sales (in dollars)is $84,000.

Accepted Answer

A) True 
 B)False

Question 5

In linear regression,the fitted value is the:

Accepted Answer

A)  predicted value of the dependent variable 
B)  predicted value of the independent value 
C)  predicted value of the slope 
D)  predicted value of the intercept 
E)  None of these options 
A)  predicted value of the dependent variable 
B)  predicted value of the independent value 
C)  predicted value of the slope 
D)  predicted value of the intercept 
E)  None of these options

Question 6

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be sued.

Accepted Answer

A) True 
 B)False

Question 7

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

Accepted Answer

A) True 
 B)False

Question 8

To help explain or predict the response variable in every regression study,we use one or more explanatory variables.These variables are also called response variables or independent variables.

Accepted Answer

A) True 
 B)False

Question 9

Regression analysis asks:

Accepted Answer

A)  if there are differences between distinct populations 
B)  if the sample is representative of the population 
C)  how a single variable depends on other relevant variables 
D)  how several variables depend on each other 
A)  if there are differences between distinct populations 
B)  if the sample is representative of the population 
C)  how a single variable depends on other relevant variables 
D)  how several variables depend on each other

Question 10

The weakness of scatterplots is that they:

Accepted Answer

A)  do not help identify linear relationships 
B)  can be misleading about the types of relationships they indicate 
C)  only help identify outliers 
D)  do not actually quantify the relationships between variables 
A)  do not help identify linear relationships 
B)  can be misleading about the types of relationships they indicate 
C)  only help identify outliers 
D)  do not actually quantify the relationships between variables

Question 11

In regression analysis,the variables used to help explain or predict the response variable are called the

Accepted Answer

A)  independent variables 
B)  dependent variables 
C)  regression variables 
D)  statistical variables 
A)  independent variables 
B)  dependent variables 
C)  regression variables 
D)  statistical variables

Question 12

A correlation value of zero indicates.

Accepted Answer

A)  a strong linear relationship 
B)  a weak linear relationship 
C)  no linear relationship 
D)  a perfect linear relationship 
A)  a strong linear relationship 
B)  a weak linear relationship 
C)  no linear relationship 
D)  a perfect linear relationship

Question 13

The percentage of variation (R2)ranges from

Accepted Answer

A)  0 to +1 
B)  -1 to +1 
C)  -2 to +2 
D)  -1 to 0 
A)  0 to +1 
B)  -1 to +1 
C)  -2 to +2 
D)  -1 to 0

Question 14

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

Accepted Answer

A)  mutually exclusive 
B)  inversely related 
C)  directly related 
D)  highly correlated 
E)  None of the above 
A)  mutually exclusive 
B)  inversely related 
C)  directly related 
D)  highly correlated 
E)  None of the above

Question 15

Approximately what percentage of the observed Y values are within one standard error of the estimate $\left( s _ { e } \right)$
Of the corresponding fitted Y values?

Accepted Answer

A)  67% 
B)  95% 
C)  99% 
D)  It is not possible to say 
A)  67% 
B)  95% 
C)  99% 
D)  It is not possible to say

Question 16

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

Accepted Answer

A) True 
 B)False

Question 17

Data collected from approximately the same period of time from a cross-section of a population are called:

Accepted Answer

A)  time series data 
B)  linear data 
C)  cross-sectional data 
D)  historical data 
A)  time series data 
B)  linear data 
C)  cross-sectional data 
D)  historical data

Question 18

The correlation value ranges from

Accepted Answer

A)  0 to +1 
B)  -1 to +1 
C)  -2 to +2 
D)  -Y to +Y 
A)  0 to +1 
B)  -1 to +1 
C)  -2 to +2 
D)  -Y to +Y

Question 19

In a simple linear regression analysis,the following sums of squares are produced: $\sum \left( Y _ { i } - \bar { Y } \right) ^ { 2 } = 400 , \sum \left( Y _ { i } - Y \right) ^ { 2 } = 80 , \sum \left( \bar { Y } _ { i } - \bar { Y } \right) ^ { 2 } = 320$
The proportion of the variation in Y that is explained by the variation in X is:

Accepted Answer

A)  20% 
B)  80% 
C)  25% 
D)  50% 
E)  None of the above 
A)  20% 
B)  80% 
C)  25% 
D)  50% 
E)  None of the above

Question 20

In regression analysis,if there are several explanatory variables,it is called:

Accepted Answer

A)  simple regression 
B)  multiple regression 
C)  compound regression 
D)  composite regression 
A)  simple regression 
B)  multiple regression 
C)  compound regression 
D)  composite regression

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

Given the least squares regression line, $\hat { Y } = 8 - 3 X$

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $\hat { Y }$ = 84 +7X.This implies that if there is no advertising,then the predicted amount of sales (in dollars)is $84,000.

In linear regression,the fitted value is the:

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be sued.

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

To help explain or predict the response variable in every regression study,we use one or more explanatory variables.These variables are also called response variables or independent variables.

Regression analysis asks:

The weakness of scatterplots is that they:

In regression analysis,the variables used to help explain or predict the response variable are called the

A correlation value of zero indicates.

The percentage of variation (R²)ranges from

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

Approximately what percentage of the observed Y values are within one standard error of the estimate $\left( s _ { e } \right)$ Of the corresponding fitted Y values?

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

Data collected from approximately the same period of time from a cross-section of a population are called:

The correlation value ranges from

In regression analysis,if there are several explanatory variables,it is called:

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 10: Regression Analysis: Estimating Relationships

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

Given the least squares regression line, Y^=8−3X\hat { Y } = 8 - 3 XY^=8−3X

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: Y^\hat { Y }Y^ = 84 +7X.This implies that if there is no advertising,then the predicted amount of sales (in dollars)is $84,000.

In linear regression,the fitted value is the:

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be sued.

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

To help explain or predict the response variable in every regression study,we use one or more explanatory variables.These variables are also called response variables or independent variables.

Regression analysis asks:

The weakness of scatterplots is that they:

In regression analysis,the variables used to help explain or predict the response variable are called the

A correlation value of zero indicates.

The percentage of variation (R2)ranges from

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

Approximately what percentage of the observed Y values are within one standard error of the estimate (se)\left( s _ { e } \right)(se​) Of the corresponding fitted Y values?

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

Data collected from approximately the same period of time from a cross-section of a population are called:

The correlation value ranges from

In regression analysis,if there are several explanatory variables,it is called:

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Given the least squares regression line, $\hat { Y } = 8 - 3 X$

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $\hat { Y }$ = 84 +7X.This implies that if there is no advertising,then the predicted amount of sales (in dollars)is $84,000.

The percentage of variation (R²)ranges from

Approximately what percentage of the observed Y values are within one standard error of the estimate $\left( s _ { e } \right)$ Of the corresponding fitted Y values?