STT 200 Exam 2

Which of the following gives a correct interpretation of the intercept? A. A student who scored 0 on the midterm would be predicted to score 50 on the final exam B. A student who scored 0 on the final exam would be predicted to score 50 on the midterm exam C. A student who scored 2 points higher than another student on the midterm would be predicted to score 1 point higher than the other student on the final exam D. None of the interpretations above are correct interpretations of the intercept E. All of the interpretations above are correct interpretations of the intercept

A. A student who scored 0 on the midterm would be predicted to score 50 on the final exam

When using the midterm exam scores to predict a student's final grade in a class, the student would prefer to have a A. Positive residual, because that means the student's final grade is higher than what we would predict with the model B. Positive residual, because that means the student's final grade is lower than what we would predict with the model C. Residual equal to zero, because that means the student's grade is exactly what we would predict with the model D. Negative residual, because that means the student's final grade is higher than what we predict with the model E. None of the above

A. Positive residual, because that means the student's final grade is higher than what we predict with the model

For which one of these relationships could we use a regression analysis? Only one choice is correct A. Relationship between weight and height B. Relationship between political party membership and opinion about abortion C. Relationship between gender and whether person has a tattoo D. Relationship between eye color (blue, brown, etc.) and hair color (blond, etc.) E. All of the above

A. Relationship between weight and height

The correlation between two variables is given by r=0.0. what does this mean? A. The best straight line through the data is horizontal B. There is a perfect positive relationship between the two variables C. There is a perfect negative relationship between the two variables D. All of the points must fall exactly on a horizontal straight line E. None of the above

A. The best straight line through the data is horizontal

You wish to describe the relationship between exam grades and the amount of time students watch the Discovery Channel. The correlation turns out to be r=+0.30. What does this mean? A. The more a student watches the Discovery Channel, the higher his or her exam grades tend to be B. The more a student watches the Discovery Channel, the lower his or her exam grades tend to be C. In order to increase your exam grades, it is recommended that you spend more time watching the Discovery Channel D. 30% of the variation in exam grades is explained by the linear relationship with time spent watching the Discovery Channel

A. The more a student watches the Discovery Channel, the higher his or her exam grades tend to be

In the simple linear regression equation, the symbol ŷ represents the A. average of predicted response B. estimated intercept C. estimated slope D. explanatory variable

A. average of predicted response

Which of the following correlation values indicated the strongest linear relationship between two quantitative variables? A. r=-0.65 B. r=-0.30 C. r=0.00 D. r=0.50 E. None of the above

A. r=-0.65

Two variable have a positive association when A. the values of one variable tend to increase as the values of the other variable increase B. the values of one variable tend to decrease as the values of the other variable increase C. The values of one variable tend to increase regardless of how the values of the other variable change D. The values of both variables are always positive

A. the values of one variable tend to increase as the values of the other variable increase

A regression line can be used to estimate the average value of y at any specified value of x A. true B. false

A. true

The equation of a regression line is called the regression equation A. true B. false

A. true

A regression line is a straight line that describes how values of a quantitative explanatory variable (y) are related, on average, to values of a quantitative response variable (x). A. true B. false

B. false

A regression line can be used to predict the unknown value of x for an individual, given that individual's y value A. true B. false

B. false

A basketball coach of a youth team wishes to predict the number of points the players will score in their first season as a junior (y) based on the number of points they scored in their last season as youth players (x). The average number of points the team scored as youth players was X=7.9 and the average number of points they scored in their first year as junior players was y=10.2. The slope is b1=0.79. What is the predicted number of goals for a player who scored 7 goals in his last season as a youth player? A. 7.78 B. 9.49 C 11.46 D. 15.58 E. 10.2

B. 9.49

Which one of the following is not appropriate for studying the relationship between two quantitative variables? A. Scatterplot B. Bar chart C. Correlation D. Regression

B. Bar chart

Which one of the following is a variable that we usually put on the horizontal axis of a scatterplot? A. y variable B. explanatory variable C. response variable D. Dependent variable

B. Explanatory variable

In the simple linear regression equation, b1 represents the slope of the regression line. Which of the following gives the best interpretation of the slope? A. It is an estimate of the average value of y at any specified value of x B. It indicates how much of a change there is for the predicted or average value of y when x increases by one unit C. It is the value of y, when x=0 D. It is the value of x, when y=0

B. It indicates how much of a change there is for the predicted or average value of y when x increases by one unit

Which of the following statements involving correlation is possible and reasonable? A. The correlation between hair color and eye color is 0.80 B. The correlation between the height of a father and the height of his first son is 0.6 C. The correlation between left foot length and right foot length is 2.35 D. The correlation between hair color and age is positive E. B and C above

B. The correlation between the height of a father and the height of his first son is 0.6

Correlation and regression are concerned with: A. The relationship between two categorical variables B. The relationship between two quantitative variables C. The relationship between a quantitative explanatory variable and a categorical response variable D. The relationship between a categorical explanatory variable and a quantitative response variable E. C and D above

B. The relationship between two quantitative variables

In the simple linear regression equation, the term b0 represents A. estimated or predicted response B. estimated intercept C. estimated slope D. explanatory variable

B. estimated intercept

All but one of the following statements below contains a mistake. which on could be true? A. The correlation between height and weight is 0.568 inches per pound B. The correlation between height and weight is 0.568 C. The correlation between the breed of a dog and its weight is 0.435 D. The correlation between gender and age is -0.171 E. The correlation between blood alcohol level and reaction time is 0.73, then the correlation between reaction time and blood alcohol level is -0.73

B. the correlation between height and weight is 0.568

Which of the following is a possible value of r^2, and indicates the strongest linear relationship between two quantitative variables? A. -90% B. 0% C. 80% D. 120%

C. 80%

Which one of the following cannot be determined from a scatterplot? A. A positive relationship B. A negative relationship C. A cause and effect relationship D. A curvilinear relationship

C. A cause and effect relationship

Which of the following gives a correct interpretation of the slope? A. A student who scored 0 on the midterm would be predicted to score 50 on the final exam B. A student who scored 0 on the final exam would be predicted to score 50 on the midterm exam C. A student who scored 2 points higher than another student on the midterm would be predicted to score 1 point higher than the other student on the final exam D. None of the interpretations above are correct interpretations of the slope E. All of the interpretations above are correct interpretations of the slope

C. A student who scored 2 points higher than another student on the midterm would be predicted to score 1 point higher than the other student on the final exam

What is the effect of an outlier on the value of a correlation coefficient? A. An outlier will always decrease a correlation coefficient B. An outlier will always increase a correlation coefficient C. An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to other points D. An outlier will have no effect on a correlation coefficient E. A and B above

C. An outlier might either decrease of increase a correlation coefficient, depending on where it is in relation to the other points

A residual plot is useful because (I) it will help us to see whether a linear model makes sense; (II) it might show a pattern in the data that was hard to see in the original scatterplot A. I only B. II only C. I and II D. Neither I nor II E. None of the above

C. I and II

Which statement about residual plot is true? (I) a curved pattern indicates nonlinear association between the variables (II) A pattern of increasing spread indicates the predicted values become less reliable as the explanatory variable increases (III) If all of the residuals are very small, the model will predict accurately A. I only B. II only C. I and II only D. I and III only E. I, II and III

C. I and II only

A regression analysis of students' college grade point averages (GPAs) and their high school GPAs found the coefficient of determination, R^2=0.311. which of these is true? I. High school GPA accounts for 31.1% of college GPA II. 31.1% of college GPAs can be correctly predicted with this model III. 31.1% of the variance in college GPA can be accounted for by the model A. I only B. II only C. III only D. I and II only E. None of the above

C. III only

The value of a correlation is reported by a researcher to be r=-0.5. Which of the following statements is correct? A. the x-variable explains 50% of the variability in the y-variable B. The x-variable explains -50% of the variability in the y-variable C. The x-variable explains 25% of the variability in the y-variable D. The x-variable explains -25% of the variability in the y-variable E. None of the above

C. The x-variable explains 25% of the variability in the y-variable

A correlation of zero between two quantitative variable means that: A. We have done something wrong in our calculation of r B. There is no association between the two variables C. There is no linear association between the two variables D. Re-expressing the data will guarantee a linear association between the two variables E. None of the above

C. There is no linear association between the two variables

In the simple linear regression equation, the term b1 represents A. estimated or predicted response B. estimated intercept C. estimated slope D. explanatory variable

C. estimated slope

which of the following sets of variables is most likely to have a negative association? A. the number of bedrooms and the number of bathrooms in a house B. the number of rooms in a house and the time it takes to vacuum the house C. the age of a house and the cleanliness of the carpets inside D. The size of a house and its selling price

C. the age of a house and the cleanliness of the carpets inside

If the correlation coefficient between two quantitative variables is positive, what conclusion can we draw? A. High values on the first variable are associated with high values on the second variable B. Low values on the first variable are associated with low values on the second variable C. High values on the first variable are associated with low values on the second variable D. Both a and b are correct E. None of the above

D. Both a and b are correct

A scatter plot of number of teachers and number of people with college degrees for cities in Pennsylvania reveals a positive association. The most likely explanation for this positive association is: A. Teachers encourage people to get college degrees, so an increase in the number of teachers is causing an increase in the number of people with college degrees B. Teaching is a common profession for people with college degrees, so an increase in the number of people with college degrees causes an increase in the number of teachers C. Cities with higher incomes tend to have more teachers and more people going to college, so income is a confounding variable, making causation between number of teachers and number of people with college degrees difficult to prove D. Larger cities tend to have both more teachers and more people with college degrees, so the association is explained by a third variable, the size of the city

D. Larger cities tend to have both more teachers and more people with college degrees, so the association is explained by a third variable, the size of the city

Extrapolation is: A. Okay to do as long as we are making predictions into the future B. Okay to do is we tell people we are assuming the linear relationship will hold outside of the range of the data C. Okay to do as long as there were no outliers in the original data D. Not okay to do E. None of the above

D. Not okay to do

which of the following sets of variables is most likely to have a negative association? A. the height of the son and the height of the father B. the age of the wife and the age of the husband C. the age of the mother and the number of children in the family D. the age of the mother and the ability to have children

D. The age of the mother and the ability to have children

A scatter plot and regression line can be used for all of the following except A. To determine if any (x,y) pairs of outliers B. To predict y at a specific value of x C. To estimate the average y at a specific value of x D. To determine if a change in x causes a change in y E. None of the above

D. To determine if a change in x causes a change in y

In the simple linear regression equation, the term x represents A. estimated or predicted response B. estimated intercept C. estimated slope D. explanatory variable

D. explanatory variable

A researcher would like to study the relationship between y=graduating GPA of students at a university and x=major field of study (1=science, 2=humanities, etc.). What is a possible difficulty with using a regression line to analyze these data? A. Presence of one or more outliers B. Inappropriately combining groups C. Curvilinear data D. Explanatory variable is not quantitative E. The values of the response variable are extrapolated values

D. explanatory variable is not quantitative

A scatterplot is a A. one-dimensional graph of a randomly scattered data B. two-dimensional graph of a straight line C. two-dimensional graph of a curved line D. two-dimensional graph of data values

D. two-dimensional graph of data values

which of the following is not a source of caution in regression analysis between two variables? A. Extrapolation B. Separate subgroups C. A lurking variable D. An outlier E. Re-expressing data

E. re-expressing data