Archive for June 2018

What does 1 - r^2 measure?

What does 1 - r^2 measure?



a. The relative importance of all other possible predictor variables on y.
b. The percentage of points that are on the regression line.
c. The percentage of points that are off the regression line



Answer: a. The relative importCorrelation and Regression Analysisance of all other possible predictor variables on y.

Which of the following statements is not correct regarding the correlation.

Which of the following statements is not correct regarding the correlation.



a. It can range from -1 to 1.
b. Its square is the percentage of variance accounted for.
c. It measures the percent of variation explained.
d. It is a measure of the association between two variables


Answer: c. It measures the percent of variation explained.

The primary purpose of a regression equation is to

The primary purpose of a regression equation is to




a. measure the association between two variables.
b. estimate the value of the dependent variable based on the independent variable.
c. estimate the value of the independent variable based on the dependent variable.
d. estimate the percentage of variance accounted for.


Answer: b. estimate the value of the dependent variable based on the independent variable.

The ratio of SS(Regression) divided by the SS(Total) is also called the

The ratio of SS(Regression) divided by the SS(Total) is also called the




a. sum of squares due to regression
b. percentage of variance accounted for
c. standard error
d. coefficient of correlation.


Answer: b. percentage of variance accounted for

The percentage of variance accounted for

The percentage of variance accounted for




a. is the square of the coefficient of correlation.
b. cannot be negative.
c. gives the percent of the variation in the dependent variable explained by the independent variable.
d. all of the above


Answer: b. cannot be negative.

The standard error is

The standard error is



a. computed from squared deviations from the regression line.
b. may be negative
c. is given in squared units of the independent variable.
d. all of the above


Answer: a. computed from squared deviations from the regression line.

A regression equation is used to predict women's Weight (in pounds) from their Height (in inches). The correlation between Weight (W) and Height (H) turned out to be 0.70 . The average H was 64 inches and the average W was 119.6 pounds.

A regression equation is used to predict women's Weight (in pounds) from their Height (in inches). The correlation between Weight (W) and Height (H) turned out to be 0.70 . The average H was 64 inches and the average W was 119.6 pounds.


The regression equation turned out to be:

W(hat) = 30 + 1.4H

Now consider the regression equation to predict H from W using the same data set:

H(hat) = int + slope * W

where "int" and "slope" are some numbers. Find the value of "slope".



a. 0.00
b. 0.35
c. 0.55
d. 0.25
e. 0.71


Answer: b. 0.35

What is the formula for the correlation coefficient in terms of the cov?

The covariance, denoted cov, is computed from:

cov = 1/(n-1) * [E](X - X(bar))(Y - Y(bar))

What is the formula for the correlation coefficient in terms of the cov?




a. cov / s(subx)
b. cov / (s(subx) * s(suby))
c. cov / s(subx)^2
d. cov / s(suby)^2
e. (s(suby) / s(subx)) * cov


Answer: b. cov / (s(subx) * s(suby))

The coefficient of correlation was computed to be -0.60. This means

The coefficient of correlation was computed to be -0.60. This means



a. the slope and intercept of the regression line are both negative
b. as x increases, y decreases.
c. x and y are both 0.
d. the percentage of variance accounted for equals sqrt(0.6)


Answer: b. as x increases, y decreases.

We obtained the following regression equation: y(hat) = 3.5 + 2.1x.. Which of the following statements are correct?

We obtained the following regression equation: y(hat) = 3.5 + 2.1x.. Which of the following statements are correct?




a. The dependent variable is predicted to increase by 2.1 for each increase of 1 unit in X.
b. The equation crosses the y-axis at 3.5.
c. If x = 5, then y = 14.
d. if x = 5 then =14
e. (a), (b) and (d) only


Answer: e. (a), (b) and (d) only

The coefficient of correlation.

The coefficient of correlation.




a. Has the same sign as the slope
b. Can range from -1.00 to 1.00
c. Is also called the percentage of variance accounted for.
d. (a) and (b) only
e. none of the above


Answer: d. (a) and (b) only

Find the intercept and slope of the regression line to predict Y from X.

Given the following information: r = 0.60
Mean Standard Deviation
X 40 4

Y 45 6
Find the intercept and slope of the regression line to predict Y from X.




a. intercept=9.0 slope=0.9
b. intercept=0.9 slope=9.0
c. intercept=9.0 slope=9.0
d. intercept=0.9 slope=0.9


Answer: a. intercept=9.0 slope=0.9

If all the points are on the regression line, then

If all the points are on the regression line, then



a. the value of the slope is 0.
b. the value of the intercept is 0.
c. the correlation coefficient is 0.
d. the standard error is 0.
e. both (c) and (d)


Answer: d. the standard error is 0.

A correlation of 0.02 would indicate:

A correlation of 0.02 would indicate:



a. a very strong direct relationship
b. a very weak direct relationship
c. a very strong inverse relationship
d. a very weak inverse relationship
e. a computational error had been made.


Answer: b. a very weak direct relationship

Here is an Excel printout of a regression problem. Use this for the following 4 questions.

Here is an Excel printout of a regression problem. Use this for the following 4 questions.

Regression Statistics
Multiple R 0.2288
R Square 0.0524
Adjusted R Square 0.0415
Standard Error 2.5166
Observations 89
ANOVA
df SS MS F p
Regress. 1 30.45 30.45 4.81 0.0310
Residual 87 550.99 6.33
Total 88 581.44
Coefficients Standard Error t Stat P-value
Intercept 33.12 27.6000 1.2000 0.2334
X -2.56 1.1675 -2.1927 0.0310


Find the "percentage of variance accounted for".


a. 22.88%
b. 5.24%
c. 4.15%
d. 2.5166%


Answer: b. 5.24%


What is the value of r?

a. -0.2288
b. 0.2288
c. 0.0524
d. -0.0524
e. 1.0000


Answer: a. -0.2288

What is the predicted value for X=10?

a. 25.60
b. 31.20
c. 7.52
d. 58.72


Answer: c. 7.52

Find a 95% C.L.I. for the answer to the previous problem, where x=10. (y(hat) denotes the answer to previous question.)


a. y(hat) +/- 2.5166
b. y(hat) +/- 1.960
c. y(hat) +/- 16.07
d. y(hat) +/- 4.933


Answer: d. y(hat) +/- 4.933

What is the difference between (beta)1 and b1 ?

What is the difference between (beta)1 and b1 ?




a. none; exactly the same; slope of regression line.
b. (beta)1 is the unknown population value, while b1 is its estimate from the data.
c. b1 is the unknown population value, while (beta)1 is its estimate from the data.


Answer: b. (beta)1 is the unknown population value, while b1 is its estimate from the data.

If the p-value (for the slope) on a regression printout = 0.00001 then

If the p-value (for the slope) on a regression printout = 0.00001 then




a. p<0 .05="" a="" and="" at="" being="" can="" conclude="" correlation="" error="" good="" have="" in="" is="" it="" least="" like="" line.="" looks="" not="" of="" p="" predictor="" regression="" s="" safely="" sampling="" shown="" slope="" so="" that="" the="" we="" y="">b. p<0 .05="" a="" have="" it="" like="" looks="" of="" p="" poor="" predictor="" so="" we="" y.="">

Answer: a. p<0 .05="" a="" and="" at="" being="" can="" conclude="" correlation="" error="" good="" have="" in="" is="" it="" least="" like="" line.="" looks="" not="" of="" p="" predictor="" regression="" s="" safely="" sampling="" shown="" slope="" so="" that="" the="" we="" y="">

I want to predict the sales on Saturday August 16 and I want a CLI interval prediction. What formula?

I want to use a regression line to predict the Sales (Y) of ice cream cones at the Baskin-Robbins in Galveston on Saturdays in summer. The X variable I will use will be the High Temperature for the day. In this way I can see what portion of the sales are due to fluctuations in the temperature. Certainly sales will fluctuate for other reasons, like if there is some special event in town or if it rains a lot. So I collect data: Sales and High Temperature for each of the 8 Saturdays in the summer so far.

I want to predict the sales on Saturday August 16 and I want a CLI interval prediction. What formula? (The subscript in the following is the number of degrees of freedom.)



a. y(hat) +- t(sub6) (std error)
b. y(hat) +- t(sub7) (std error)
c. y(hat) +- t(sub8) (std error)


Answer: a. y(hat) +- t(sub6) (std error)

If the manager decides to spend $3000 on advertising, based on the simple linear regression results given above, the estimated sales are:

Regression analysis
r 0.873
r squared 0.762
Standard error 11.547
n 7
ANOVA
SS
Regression 2.133.3333
Residual 666.6667
Total 2,800,000
Regression output p-value
Intercept 63.3333 .0005
Advertising 6.667 .0103
The local grocery store wants to predict the daily sales in dollars. The manager believes that the amount of newspaper advertising significantly affects the store sales. He randomly selects 7 days of data consisting of daily grocery store sales (in thousands of dollars) and advertising expenditures (in thousands of dollars). The Excel output given above summarizes the results of the regression model.

If the manager decides to spend $3000 on advertising, based on the simple linear regression results given above, the estimated sales are:



A. 83,333
B. 70,000
C. 68,333
D. 20,064,333
E. 20,063.33


Answer: A. 83,333

Be sure to use the number 3 instead of 3000.

The following results were obtained as a part of simple regression analysis:

The following results were obtained as a part of simple regression analysis:

r2 = .9162
p-value = .000
The null hypothesis of no linear relationship between the dependent variable and the independent variable



A. is not an appropriate null hypothesis for this situation
B. is rejected
C. is not rejected
D. cannot be tested with the given information


Answer: B. Is rejected

In simple regression analysis the quantity that gives the amount by which Y (dependent variable) changes for a unit change in X (independent variable) is called the __________

In simple regression analysis the quantity that gives the amount by which Y (dependent variable) changes for a unit change in X (independent variable) is called the __________



A. Coefficient of determination
B. Y intercept of the regression line
C. Slope of the regression line
D. Standard Error
E. Correlation Coefficient


Answer: C. Slope of the regression line

When using simple regression analysis, if there is a strong correlation between the independent and dependent variable, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable.

When using simple regression analysis, if there is a strong correlation between the independent and dependent variable, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable.



Answer: False

In the accompanying figure, the sequence structure is the completion of ____.

In the accompanying figure, the sequence structure is the completion of ____.



a. one or more process steps based on the results of a test or condition
b. steps in a chronological order, one after another
c. a process step that is repeated until a specific condition changes
d. a specific condition that is repeated until a process changes


Answer: B

The data dictionary usually records and describes a default value, which is the ____.

The data dictionary usually records and describes a default value, which is the ____.




a. specification of the set of values permitted for the data element
b. identification of the user(s) responsible for changing values for the data element
c. specification for the origination point for the data element's value
d. value for the data element if a value otherwise is not entered for it



Answer: D

Balancing ____.

Balancing ____.




a. uses a series of increasingly detailed DFDs to describe an information system
b. ensures that the input and output data flows of the parent DFD are maintained on the child DFD
c. uses a series of increasingly sketchy DFDs to describe an information system
d. ensures that the input and output data flows of the child DFD are maintained on the parent DFD



Answer: B

Leveling ____.

Leveling ____.




a. uses a series of increasingly detailed DFDs to describe an information system
b. ensures that the input and output data flows of the parent DFD are maintained on the child DFD
c. uses a series of increasingly sketchy DFDs to describe an information system
d. ensures that the input and output data flows of the child DFD are maintained on the parent DFD


Answer: A

A gray hole is a process that has ____.

A gray hole is a process that has ____.




a. no input
b. at least one output and one input, but the output obviously is insufficient to generate the input shown
c. no output
d. at least one input and one output, but the input obviously is insufficient to generate the output shown


Answer: D

A black hole is a process that has ____.

A black hole is a process that has ____.




a. no input
b. at least one output and one input, but the output obviously is insufficient to generate the input shown
c. no output
d. at least one input and one output, but the input obviously is insufficient to generate the output shown


Answer: C

A spontaneous generation process is a process that has ____.

A spontaneous generation process is a process that has ____.




a. no input
b. at least one output and one input, but the output obviously is insufficient to generate the input shown
c. no output
d. at least one input and one output, but the input obviously is insufficient to generate the output shown



Answer: A

A DFD shows ____.

A DFD shows ____.




a. how data are related
b. what key fields are stored in the system
c. how a system transforms input data into useful information
d. what data is stored in the system


Answer: C
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