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RM3 Multiple regression

Sinead Sherman 10 questions

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Questions in this quiz (10)

  1. 1In the study described, the Independent Variables are [blank_start][a][blank_end] and [blank_start][b][blank_end] , and the Dependent Variable is [blank_start][c].[blank_end]

    1. AMoney_leaflets
    2. BLeaflet_quality
    3. CBeauty_treatment_sold
  2. 2What proportion of the variance in Salon Sales (Beauty_treatment_sold) are predicted by the two IV’s? [blank_start](x)[blank_end]

    1. A43.40%
  3. 3Fill in the blanks below for the F-statement for the model as a whole.

    F([blank_start][a][blank_end])=[[blank_start]b[blank_end]], p[[blank_start]c[blank_end]][[blank_start]d[blank_end]]
    1. A2,159
    2. B60.86
    3. C<
    4. D.001
  4. 4What is the Standardised Beta value for Money spent (Money_leaflets) [blank_start](X)[blank_end]

    1. A.58
  5. 5What is the Unstandardized Beta value for quality of Quality of Leaflet? [blank_start](X)[blank_end]

    1. A15.90
  6. 6Which of the following is true about the difference between the the Standardised and Unstandardised Beta values?

    1. AThe Unstandardised values are not in the original unit of measurement
    2. BWhen using the regression equation to predict Beauty_Treatments_Sold, you should always use the Unstandardised values.
    3. CThere is no difference between the standardied and unstandardized values- only that one has been multiplied by 100 to produce a percentage.
    4. DThe Standardised values are in the original unit of measurement
  7. 7When looking at the coefficients table, which of the following is true?

    1. AThe Standardised Beta values can be compared across predictors because they no longer appear in the original unit of measurement
    2. BThe value produced for the constant is meaningless when interpreting a Multiple Regression analysis
    3. CThe Standardised Beta values should not be compared across predictors because of the issue of the unit of measurement
    4. DThe Unstandardized Beta values can be compared across predictors because they are in the same unit of measurement
  8. 8If a salon spent £60 on their advertising leaflets (Money_leaflets), and the leaflets they produced had a quality rating of 2 (Leaflet_quality), what would their predicted salon sales (Beauty_treatment_sold) be? [blank_start](X)[blank_end]

    1. A73.37
  9. 9If a salon spent £1500 on their advertising leaflets (Money_leaflets), and the leaflets they produced had a quality rating of 8 (Leaflet_quality), what would their predicted salon sales (Beauty_treatment_sold) be? [blank_start](X)[blank_end]
    1. A312.79
  10. 10Please check the assumption of Multicollinearity in your data. What conclusions can you draw from this part of the analysis?
    1. AAs we have a VIF above 10, and a tolerance statistic below .2 we can conclude that our predictors are not too highly correlated, and we have failed this assumption.
    2. BAs we have a VIF above 10, and a tolerance statistic below .2 we can conclude that our predictors are too highly correlated, and we have passed this assumption.
    3. CAs we have a VIF below 10, and a tolerance statistic greater than .2 we can conclude that our predictors are too highly correlated, and we have failed this assumption.
    4. DAs we have a VIF below 10, and a tolerance statistic greater than .2 we can conclude that our predictors are not highly correlated, and we have passed this assumption.

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