2024 – BA 3400 Quantitative Methods II QUIZ 4 22 x 0 5 10 1 bonus point

BA-3400: Quantitative Methods – II
QUIZ-4 (22 x 0.5 = 10 +1 bonus point). Due Date: July 28, 2015

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. 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    Variables Entered    Variables Removed    Method
1    pricesb    .    Enter
a. Dependent Variable: visitfre
b. All requested variables entered.

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

ANOVAa
Model    Sum of Squares    df    Mean Square    F    Sig.
1    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 Coefficients    t    Sig.
    B    Std. Error    Beta        
1    (Constant)    2.773    .383        7.237    .000
    prices    .176    .100    .160    1.761    .081
a. Dependent Variable: visitfre

 

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    Variables Entered    Variables Removed    Method
1    entertaib    .    Enter
a. Dependent Variable: visitfre
b. All requested variables entered.

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

ANOVAa
Model    Sum of Squares    df    Mean Square    F    Sig.
1    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 Coefficients    t    Sig.
    B    Std. Error    Beta        
1    (Constant)    1.772    .335        5.294    .000
    entertai    .451    .088    .427    5.131    .000
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 Entered    Variables Removed    Method
1    beconvie, entertai, feelsafeb    .    Enter
a. Dependent Variable: visitfre
b. All requested variables entered.

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

ANOVAa
Model    Sum of Squares    df    Mean Square    F    Sig.
1    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 Coefficients    t    Sig.
    B    Std. Error    Beta        
1    (Constant)    .318    .416        .763    .447
    entertai    .345    .081    .327    4.237    .000
    feelsafe    .191    .093    .170    2.043    .043
    beconvie    .287    .075    .320    3.830    .000
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 + 0.414×1 + 0.411×2 + 0.411×3
b. y = 0.318 + 0.345×1 + 0.191×2 + 0.287×3
c. y = 0.381 + 0.099×1 + 0.079×2 + 0.411×3
d. y = 0.127 + 0.162×1 + 0.210×2 + 0.507×3

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