Question 1

The least squares regression line is the prediction line that results in the most direct "hits." Is this true? Explain.

Accepted Answer

The statement that "the least squares re

Question 2

Generally, one can use the same regression equation for predicting Y given X as for X given Y .

Accepted Answer

A) True 
 B)False

Question 3

The symbol for the standard error of estimate when predicting Y given X is _________.

Accepted Answer

A)  r X ½ Y 
B)  sX ½ Y 
C)  r Y ½ X 
D)  s Y ½ X 
A)  r X ½ Y 
B)  sX ½ Y 
C)  r Y ½ X 
D)  s Y ½ X

Question 4

The regression coefficient b Y and the correlation coefficient r, _________.

Accepted Answer

A)  necessarily increase in magnitude as the strength of relationship increases 
B)  are both slopes of straight lines 
C)  are not related 
D)  will equal each other when the variability of the X and Y distributions are equal 
E)  are both slopes of straight lines and will equal each other when the variability of the X and Y distributions are equal 
A)  necessarily increase in magnitude as the strength of relationship increases 
B)  are both slopes of straight lines 
C)  are not related 
D)  will equal each other when the variability of the X and Y distributions are equal 
E)  are both slopes of straight lines and will equal each other when the variability of the X and Y distributions are equal

Question 5

For regression purposes,

Accepted Answer

A)  X is assigned to the variable being predicted. 
B)  Y is assigned to the variable being predicted. 
C)  It doesn't matter whether X or Y is assigned to the variable being predicted. 
D)  none of these 
A)  X is assigned to the variable being predicted. 
B)  Y is assigned to the variable being predicted. 
C)  It doesn't matter whether X or Y is assigned to the variable being predicted. 
D)  none of these

Question 6

Define Homoscedasticity.

Accepted Answer

Homoscedasticity is a statistical concep

Question 7

If s Y = s X = 1 and the value of b Y = 0.6, what will the value of r be?

Accepted Answer

A)  0.36 
B)  0.60 
C)  1.00 
D)  0.00 
A)  0.36 
B)  0.60 
C)  1.00 
D)  0.00

Question 8

There is ________ between the s Y ½ X and r .

Accepted Answer

A)  a direct relationship 
B)  an inverse relationship 
C)  no relationship 
D)  animosity 
A)  a direct relationship 
B)  an inverse relationship 
C)  no relationship 
D)  animosity

Question 9

The least-squares regression line minimizes _________.

Accepted Answer

A)  s 
B)    
C)  S ( Y -   ) 2 
D)  S ( Y - Y' ) 2 
E)  b and d 
A)  s 
B)    
C)  S ( Y -   ) 2 
D)  S ( Y - Y' ) 2 
E)  b and d

Question 10

In general one is less confident in predictions of Y when the value of X used for the prediction is outside the range of the original data used to construct the regression line.

Accepted Answer

A) True 
 B)False

Question 11

Why does the least squares regression line minimize Σ( Y - Y' ) 2 , rather than Σ( Y - Y' )?

Accepted Answer

The least squares regression line is des

Question 12

In multiple regression, will use of a second predictor variable always increase the accuracy of prediction? Explain.

Accepted Answer

In multiple regression, the use of a sec

Question 13

Define least-squares regression line.

Accepted Answer

The least-squares regression line, also

Question 14

Define regression.

Accepted Answer

Regression is a statistical method used

Question 15

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded.   The least squares regression line for predicting the amount of exercise from % fat is _________.

Accepted Answer

A)  X' = - 1.931 Y + 66.363 
B)  X' = - 0.476 Y + 33.272 
C)  X' = 1.931 Y + 66.363 
D)  X' = - 1.905 Y + 62.325 
A)  X' = - 1.931 Y + 66.363 
B)  X' = - 0.476 Y + 33.272 
C)  X' = 1.931 Y + 66.363 
D)  X' = - 1.905 Y + 62.325

Question 16

A professor wanted to predict final exam scores from midterm exam scores. He used data from several different professors teaching the same class. He obtained the following data: What are the values for each of the following?

Accepted Answer

The answer of A professor wanted to predict final exam...

Question 17

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded.   Based on the above data, if an individual exercises 20 minutes daily, his predicted % body fat would be _________.

Accepted Answer

A)  21.63 
B)  27.74 
C)  27.88 
D)  23.75 
A)  21.63 
B)  27.74 
C)  27.88 
D)  23.75

Question 18

The regression line will always go through the point   .

Accepted Answer

A) True 
 B)False

Question 19

Which of the following statement(s) is (are) an important consideration(s) in applying linear regression techniques?

Accepted Answer

A)  the relationship should be linear 
B)  both variables must be measured in the same units 
C)  predictions for Y should be within the range of the X variable in the sample 
D)  a and c 
A)  the relationship should be linear 
B)  both variables must be measured in the same units 
C)  predictions for Y should be within the range of the X variable in the sample 
D)  a and c

Question 20

If the regression equation for a set of data is Y' = 2.650 X + 11.250 then the value of Y' for X = 33 is _________.

Accepted Answer

A)  87.45 
B)  371.25 
C)  98.70 
D)  76.20 
A)  87.45 
B)  371.25 
C)  98.70 
D)  76.20

The least squares regression line is the prediction line that results in the most direct "hits." Is this true? Explain.

Generally, one can use the same regression equation for predicting Y given X as for X given Y .

The symbol for the standard error of estimate when predicting Y given X is _________.

The regression coefficient b _Y and the correlation coefficient r, _________.

For regression purposes,

Define Homoscedasticity.

If s Y = s X = 1 and the value of b Y = 0.6, what will the value of r be?

There is ________ between the s _{Y ½ X}and r .

The least-squares regression line minimizes _________.

In general one is less confident in predictions of Y when the value of X used for the prediction is outside the range of the original data used to construct the regression line.

Why does the least squares regression line minimize Σ( Y - Y' ) ² , rather than Σ( Y - Y' )?

In multiple regression, will use of a second predictor variable always increase the accuracy of prediction? Explain.

Define least-squares regression line.

Define regression.

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded. The least squares regression line for predicting the amount of exercise from % fat is _________.

A professor wanted to predict final exam scores from midterm exam scores. He used data from several different professors teaching the same class. He obtained the following data: What are the values for each of the following?

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded. Based on the above data, if an individual exercises 20 minutes daily, his predicted % body fat would be _________.

The regression line will always go through the point .

Which of the following statement(s) is (are) an important consideration(s) in applying linear regression techniques?

If the regression equation for a set of data is Y' = 2.650 X + 11.250 then the value of Y' for X = 33 is _________.

Statistics and Scientific Method

Basic Mathematical and Measurement Concepts

Frequency Distributions

Measures of Central Tendency and Variability

The Normal Curve and Standard Scores

Correlation

Random Sampling and Probability

Binomial Distribution

Introduction to Hypothesis Testing: Using the Sign Test

Power

Sampling Distributions, Sampling Distribution of the Mean: the Normal Deviate Z Test

Students T Test for Single Samples

Students T Test for Correlated and Independent Groups

Introduction to the Analysis of Variance

Introduction to the Two-Way Analysis of Variance

Chi-Square and Other Nonparametric Tests

Filters

Exam 7: Linear Regression

The least squares regression line is the prediction line that results in the most direct "hits." Is this true? Explain.

Generally, one can use the same regression equation for predicting Y given X as for X given Y .

The symbol for the standard error of estimate when predicting Y given X is _________.

The regression coefficient b Y and the correlation coefficient r, _________.

For regression purposes,

Define Homoscedasticity.

If s Y = s X = 1 and the value of b Y = 0.6, what will the value of r be?

There is ________ between the s Y ½ X and r .

The least-squares regression line minimizes _________.

In general one is less confident in predictions of Y when the value of X used for the prediction is outside the range of the original data used to construct the regression line.

Why does the least squares regression line minimize Σ( Y - Y' ) 2 , rather than Σ( Y - Y' )?

In multiple regression, will use of a second predictor variable always increase the accuracy of prediction? Explain.

Define least-squares regression line.

Define regression.

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded. The least squares regression line for predicting the amount of exercise from % fat is _________.

A professor wanted to predict final exam scores from midterm exam scores. He used data from several different professors teaching the same class. He obtained the following data: What are the values for each of the following?

A researcher collects data on the relationship between the amount of daily exercise an individual gets and the percent body fat of the individual. The following scores are recorded. Based on the above data, if an individual exercises 20 minutes daily, his predicted % body fat would be _________.

The regression line will always go through the point .

Which of the following statement(s) is (are) an important consideration(s) in applying linear regression techniques?

If the regression equation for a set of data is Y' = 2.650 X + 11.250 then the value of Y' for X = 33 is _________.

Statistics and Scientific Method

Basic Mathematical and Measurement Concepts

Frequency Distributions

Measures of Central Tendency and Variability

The Normal Curve and Standard Scores

Correlation

Random Sampling and Probability

Binomial Distribution

Introduction to Hypothesis Testing: Using the Sign Test

Power

Sampling Distributions, Sampling Distribution of the Mean: the Normal Deviate Z Test

Students T Test for Single Samples

Students T Test for Correlated and Independent Groups

Introduction to the Analysis of Variance

Introduction to the Two-Way Analysis of Variance

Chi-Square and Other Nonparametric Tests

Filters

The regression coefficient b _Y and the correlation coefficient r, _________.

There is ________ between the s _{Y ½ X}and r .

Why does the least squares regression line minimize Σ( Y - Y' ) ² , rather than Σ( Y - Y' )?