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[answered] BA-3400: Quantitative Methods - II QUIZ-4 (22 x 0.5 = 10 +1


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BA-3400: Quantitative Methods - II

 

QUIZ-4 (22 x 0.5 = 10 +1 bonus point). Due Date: Wed, Dec 7, 2016

 

Instructions:

 

This quiz has a total of 22 multiple choice questions (MCQ) for half point each including two

 

bonus questions. All questions are compulsory. Some questions may have up to 5 alternatives,

 

but there is only one correct answer. Consider all alternatives before selecting the best answer. It

 

is advised you first complete the quiz below at your convenience & time, select the responses

 

and then attempt to submit the responses on line. Some questions are based on SPSS table

 

outputs of the ?Linear Regression Analysis? (both Bivariate and Multivariate). Good

 

Luck!

 

1. How would you define ?the best fit line? in scatter plot of regression analysis?

 

a. An imaginary line drawn in such a way that the total variance of distance for each data point

 

from this line is minimized.

 

b. An imaginary line drawn in such a way that the total variance of distance for each data point

 

from this line is maximized.

 

c. An imaginary line drawn in such a way that the total variance of distance for each data point

 

from this line remains constant.

 

d. All of the above.

 

2. In order to run a linear regression, the following assumption(s) have to be met:

 

a. Data fits the straight line model

 

b. Both, the DV and IV(s) have to be continuous variables

 

c. Both ?a? and ?b?

 

d. None of the above

 

3. R Square is a measure of

 

a. Residual?s in DV explained by the IV(s) in regression analysis

 

b. Regression?s in DV explained by the IV(s) in regression analysis

 

c. Variance explained in DV by the IV(s) in regression analysis

 

d. All of the above

 

4. If the ?F? value in ANOVA table of Linear Regression Analysis is significant (p < .05),

 

what does it mean?

 

a. The data does not fit the straight line model

 

b. The data fits the curvilinear model

 

c. The data does not fit the curvilinear model

 

d. The data fits the straight line model

 

e. All of the above 5. If the ?F? value in ANOVA table of Linear Regression Analysis is not significant (p >

 

.05), what does it mean?

 

a. The data does not fit the straight line model

 

b. We cannot proceed with Regression Analysis

 

c. The data fits the straight line model

 

d. We can proceed with Regression Analysis

 

e. Both ?a? and ?b?

 

6. In the equation for a straight line y = a + bx, the intercept ?a? is

 

a. the dependent variable

 

b. the variable used to predict the dependent variable

 

c. the change in y for any unit change in x

 

d. the distance from origin to the point where the straight line cuts the y axis, at x = 0

 

7. In the formula for a straight line y = a + bx, the slope ?b? is defined as

 

a. the change in y for a unit change in x

 

b. where the line cuts the y axis when x = 0

 

c. the variable used to predict the dependent variable

 

d. the dependent variable

 

8. If the intercept (constant) is found to be 2 and the slope is found to be 5, for any

 

independent variable X in a Bivariate Regression coefficient table results, and are found to

 

be significant, then the equation will be given by (where Y is the dependent variable):

 

a. Y = 2 + 3X

 

b. Y = 2 + 5X

 

c. Y = 5 + 2X

 

d. Y = 3 + 2X

 

9. In a multiple regression, the relative importance of the independent variables in

 

predicting/explaining the dependent variable is determined by examining the:

 

a. R2 values

 

b. F-values

 

c. unstandardized (B) values

 

d. p values

 

10. In Multiple Regression analysis there is/are ________ independent variable(s) and

 

_____ dependant variable(s).

 

a. One: more than one

 

b. More than one: one

 

c. Nonmetric-scaled: metric scaled

 

d. Multiple: multiple

 

e. One: multiple SPSS Tables Set-1

 

Regression (visitfre(DV), prices(IV)

 

Variables Entered/Removeda

 

Model 1 Variables Variables Entered Removed pricesb . Method Enter a. Dependent Variable: visitfre

 

b. All requested variables entered.

 

Model Summary

 

Model 1 R .160a R Square .026 Adjusted R Std. Error of the Square Estimate .017 1.088 a. Predictors: (Constant), prices ANOVAa

 

Model 1 Sum of Squares df Mean Square F Sig. Regression 3.668 1 3.668 3.099 .081b Residual 139.657 118 1.184 Total 143.325 119 a. Dependent Variable: visitfre

 

b. Predictors: (Constant), prices

 

Coefficientsa

 

Model Unstandardized Coefficients Standardized t Sig. 7.237 .000 1.761 .081 Coefficients

 

B Std. Error (Constant) 2.773 .383 prices .176 .100 Beta 1

 

a. Dependent Variable: visitfre .160 11. In the SPSS Tables Set-1 for the Regression Analysis, what is the dependent variable

 

and what is/are the independent variable(s) respectively:

 

a. visitfre and prices

 

b. price and visitfre

 

c. visitfre and location

 

d. location and prices 12. In the SPSS Tables Set-1 for the Regression Analysis, what % of variance in visitfre is

 

explained by prices. (Hint- Look for R-Square value as a % of 1)

 

a. 16.0%

 

b. 2.6%

 

c. 8.1%

 

d. 10.0 %

 

13. In the SPSS Tables Set-1 for the Regression Analysis, does the data fits the straight line

 

model (Hint- look at the significance of ?F? value in ANOVA table).

 

a. Yes, we can proceed with regression analysis

 

b. No, we cannot proceed with regression analysis SPSS Tables Set-2

 

Regression (visitfre(DV), entertai(IV)

 

Variables Entered/Removeda

 

Model 1 Variables Variables Entered Removed entertaib . Method Enter a. Dependent Variable: visitfre

 

b. All requested variables entered. Model Summary

 

Model 1 R .427a R Square .182 a. Predictors: (Constant), entertai Adjusted R Std. Error of the Square Estimate .175 .997 ANOVAa

 

Model 1 Sum of Squares df Mean Square F Sig. Regression 26.145 1 26.145 26.328 .000b Residual 117.180 118 .993 Total 143.325 119 a. Dependent Variable: visitfre

 

b. Predictors: (Constant), entertai Coefficientsa

 

Model Unstandardized Coefficients Standardized t Sig. 5.294 .000 5.131 .000 Coefficients

 

B Std. Error (Constant) 1.772 .335 entertai .451 .088 Beta 1

 

.427 a. Dependent Variable: visitfre 14. In the SPSS Tables Set-2 for the Regression Analysis, what is the dependent variable

 

and what is/are the independent variable(s) respectively:

 

a. visitfre and prices

 

b. price and visitfre

 

c. visitfre and entertai

 

d. location and prices

 

15. In the SPSS Tables Set-2 for the Regression Analysis, what % of variance in visitfre is

 

explained by entertai. (Hint- Look for R-Square value as a % of 1)

 

a. 16.0%

 

b. 2.6%

 

c. 18.2%

 

d. 10.0 %

 

16. In the SPSS Tables Set-2 for the Regression Analysis, does the data fits the straight line

 

model (Hint- look at the significance of ?F? value in ANOVA table).

 

a. Yes, we can proceed with regression analysis

 

b. No, we cannot proceed with regression analysis SPSS Tables Set-3

 

Variables Entered/Removeda

 

Model Variables Variables Entered Removed Method beconvie,

 

1 entertai, . Enter feelsafeb

 

a. Dependent Variable: visitfre

 

b. All requested variables entered.

 

Model Summary

 

Model R 1 .591a R Square .350 Adjusted R Std. Error of the Square Estimate .333 .896 a. Predictors: (Constant), beconvie, entertai, feelsafe

 

ANOVAa

 

Model 1 Sum of Squares df Mean Square F Sig. Regression 50.114 3 16.705 20.788 .000b Residual 93.211 116 .804 Total 143.325 119 a. Dependent Variable: visitfre

 

b. Predictors: (Constant), beconvie, entertai, feelsafe Coefficientsa

 

Model Unstandardized Coefficients Standardized t Sig. .763 .447 Coefficients

 

B Std. Error Beta (Constant) .318 .416 entertai .345 .081 .327 4.237 .000 feelsafe .191 .093 .170 2.043 .043 beconvie .287 .075 .320 3.830 .000 1 a. Dependent Variable: visitfre 17. In the SPSS Tables Set-3 for the Regression Analysis, what is the dependent variable

 

and what is/are the independent variable(s) respectively:

 

a. visitfre and prices, location, foodtype

 

b. price and visitfre, entertain, beconvie

 

c. visitfre and entertain, feelsafe, beconvie

 

d. location and prices, entertain, feelsafe

 

18. In the SPSS Tables Set-3 for the Regression Analysis, what % of variance in DV

 

(visitfre) is explained by the IV(s). (Hint- Look for R-Square value as a % of 1)

 

a. 59.1%

 

b. 2.6%

 

c. 18.2%

 

d. 35.0 %

 

19. In the SPSS Tables Set-3 for the Regression Analysis, does the data fits the straight line

 

model (Hint- look at the significance of ?F? value in ANOVA table).

 

a. Yes, we can proceed with regression analysis

 

b. No, we cannot proceed with regression analysis

 

20. In the SPSS Tables Set-3 for the Regression Analysis, when running a linear regression

 

to find out the impact of ?entertain?, ?feelsafe?, and ?beconvie? (ALL TAKEN

 

TOGETHER) on ?visitfre? , what is the regression equation in form of y = c + m1x1 + m2x2

 

+ m3x3 (where y-visitfre, c-constant, x1-entertai, m1-slope of entertai, x2-feelsafe, m2-slope of

 

feelsafe, x3-beconvie, and m3- slope of beconvie) depicting this relationship. (Hint- Look at

 

respective values of unstandardized coefficients in the coefficients table)

 

a. y = 1.792 + 0.414x1 + 0.411x2 + 0.411x3

 

b. y = 0.318 + 0.345x1 + 0.191x2 + 0.287x3

 

c. y = 0.381 + 0.099x1 + 0.079x2 + 0.411x3

 

d. y = 0.127 + 0.162x1 + 0.210x2 + 0.507x3

 

21. The coefficient of correlation ranges from -1 to +1, what is the range of

 

?unstandardized B weight? or the slope for any independent variable in regression

 

analysis?

 

a. -1 to +1

 

b. -10 to +10

 

c. -? to +?

 

d. -100 to +100 22. Assuming any two variables? have a perfect positive CORRELATION of +1 and the

 

SLOPE of the best fit line in Bivariate Regression for some other set of a DV and an IV is

 

found to be +1. What happens to the value of correlation and slope if, by including some

 

more data, the angle of inclination of the lines in correlation and regression increases from

 

the prior 45 degrees.

 

a. Correlation Decreases / Slope Increases

 

b. Correlation Increases / Slope Increases

 

c. Correlation Decreases / Slope Decreases

 

d. Correlation Increases / Slope Decreases

 


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