Practice Questions 9.1 (Start of test 3) [NOT ON FINAL]

In Chapter 09, what characteristic of columns of data values is being measured?

A) The movement between two columns of data values.
B) The variances of two columns of data values.
C) The means of two columns of data values.
D) The difference between two columns of data values.

What is needed to measure the movement between two columns of data values?

A) Two columns of data values in the long view.
B) Two columns of independent data values.
C) Three columns of dependent data values.
D)  Two columns of dependent data values.

Are two columns of both the Height and Weight of individuals appropriate to measure movement?

A)  Yes, because both height and weight come from one individual.
B) Yes, because both height and weight can be put into the long view.
C) No, because two columns of independent data values are needed.
D) No, because Height and Weight do not have the same units of measurement.

Select the two important points to remember when interpreting statistics of movement.

A) Hand calculate to confirm the statistics.
B) Always look at the data first.
C) Movement is not causation.
D) Statistics are always correct.

Where does a lurking variable lurk?

A) In a dark alleyway.
B) In the missing data of a data set.
C) Between two columns of data values.
D) Outside of the data values collected.

Why is a lurking variable important?

A)  Because it affects the statistical results without our knowledge.
B) Because when it exists, it is important.
C) Because it is the most important variable in the study.
D) Because a good statistical method should include the lurking variable.

What is movement between two columns of data values?

A) How the mean of one variable moves in relation to the mean of the other variable.
B) How the variances of the two variables adjust toward each other.
C)  How the value of one variable moves in relation to the value of the other variable.
D) How the values of the two variables move along the real number line.

Select all choices that are the statistical methods used to measure variable relationship.

A) A scatterplot.
B) Linear correlation.
C) A stem-and-leaf plot. 
D) Linear regression.
E) Linear hypothesis test.

What type of relationship between two variables can a scatterplot detect?

A)  Any type of relationship.
B) Only a curvilinear relationship.
C) Only if there is no relationship.
D) Only a linear relationship.

Please match each variable with where it is plotted in a scatterplot

A. The response variable
B. The predictor variable

__ Plotted on the x-axis
__ Plotted on the y-axis

In the thought concept of variable relationship, please match each variable with when it changes.

A. The predictor variable
B. The response variable

__ Changes before the response variable changes.
__ Changes after the predictor variable changes.

What type of relationship between two variables can linear correlation detect?

A) Only no relationship.
B) Only a curvilinear relationship.
C)  Only a linear relationship.
D) Any type of relationship.

Can linear correlation measure a linear relationship, as well as just detect it?

A)  Yes, it can measure both the direction and the strength of the linear relationship.
B) No, it can detect a linear relationship, but it cannot measure the relationship.
C) Yes, it can measure a linear relationship, but it cannot detect the relationship.
D) Linear correlation is not an appropriate statistical method to use for relationship.

What is the range of values for linear correlation measure?

A) -1 to +1, not including zero ( [-1,0), (0,+1] ).
B)  -1 to +1, inclusive ( [-1,+1] ).
C) -1 to +1, exclusive ( (-1,+1) ).
D) 0 to +1, inclusive ( [0,+1] ).

What information does linear regression give that linear correlation does not give?

A) A p-value for the linear relationship.
B) The units of information (degrees of freedom) in the linear relationship.
C) The magnitude of the effect of the linear relationship.
D) The appropriateness of the measured linear relationship.

Conceptually, how is the information about a linear relationship summarized in linear regression?

A) By the intersection of the two lines of best fit in the middle of the scatterplot.
B)  By the line of best fit through the middle of the scatterplot.
C) By the circle of best fit around all the points in the scatterplot.
D) By the line of least fit on the side of the scatterplot.

What statistical method will this book use to calculate the equation of the line in linear regression?

A) The least-squares method of linear regression.
B) The maximum likelihood method of linear regression.
C) The equation of a line method of linear regression.
D) The linear correlational method of linear regression.