1. A data point (observation) that does not fit the trend shown by the remaining data is called a(n) _____.   Outlier
2. A procedure used for finding the equation of a straight line that provides the best approximation for the relationship between the independent and dependent variables is   the least squares method
3. A regression analysis between demand (y in 1000 units) and price (x in dollars) resulted in the following equation:ŷ = 9 − 3xThe above equation implies that if the price is increased by $1, the demand is expected to   decrease by 3,000 units
4. A regression analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x).
   
   n = 10
   Σx = 55
   Σy = 55
   Σx2 = 385
   Σy2 = 385
   Σxy = 220
   
   Refer to Exhibit 14-1. The least squares estimate of b1 equals __   -1
5. A regression analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x).
   
   n = 10
   Σx = 55
   Σy = 55
   Σx2 = 385
   Σy2 = 385
   Σxy = 220
   
   Refer to Exhibit 14-1. The coefficient of determination equals _____.  1
6. An observation that has a strong effect on the regression results is called a(n) _  influential observation.
7. Data points having high leverage are often ___   influential
8. If the coefficient of correlation is .4, the percentage of variation in the dependent variable explained by the estimated regression equation __   is 16%
9. In a regression analysis, if SSE = 200 and SSR = 300, then the coefficient of determination is ___  600
10. In a regression analysis, the variable that is used to predict the dependent variable ___   is the independent variable
11. In regression analysis, the independent variable is typically plotted on the _____.  x-axis of a scatter diagram
12. In regression and correlation analysis, if SSE and SST are known, then with this information the _   coefficient of determination can be computed
13. In simple linear regression, r2 is the __   coefficient of determination
14. It is possible for the coefficient of determination to be _   less than 1
15. Regression analysis is a statistical procedure for developing a mathematical equation that describes how ___   one dependent and one or more independent variables are related
16. Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained.ŷ = 500 + 4xBased on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is   $900,000
17. Regression analysis was applied between sales data (in $1000s) and advertising data (in $100s), and the following information was obtained.
    ŷ = 12 + 1.8x
    
    n = 17
    SSR = 225
    SSE = 75
    Sb1 = 0.2683
    
    Refer to Exhibit 14-3. Based on the above estimated regression equation, if advertising is $3,000, then the point estimate for sales (in dollars) is _____. $66,000
18. Regression analysis was applied between sales data (in $1000s) and advertising data (in $100s), and the following information was obtained.
    ŷ = 12 + 1.8x
    
    n = 17
    SSR = 225
    SSE = 75
    Sb1 = 0.2683
    
    Refer to Exhibit 14-3. The t statistic for testing the significance of the slope is _____   6.709
19. Regression analysis was applied between sales data (in $1000s) and advertising data (in $100s), and the following information was obtained.
    ŷ = 12 + 1.8x
    
    n = 17
    SSR = 225
    SSE = 75
    Sb1 = 0.2683
    
    Refer to Exhibit 14-3. Using α = .05, the critical t value for testing the significance of the slope is __   2.131
20. The difference between the observed value of the dependent variable and the value predicted by using the estimated regression equation is called _____.   Residual
21. The equation that describes how the dependent variable (y) is related to the independent variable (x) is called ____   The regression model
22. The following information regarding a dependent variable (y) and an independent variable (x) is provided.
    x y
    2 4
    1 3
    4 4
    3 6
    5 8
    SSE = 6
    SST = 16
    
    Refer to Exhibit 14-4. The coefficient of determination is    .625
23. The following information regarding a dependent variable (y) and an independent variable (x) is provided.
    x y
    2 4
    1 3
    4 4
    3 6
    5 8
    SSE = 6
    SST = 16
    
    Refer to Exhibit 14-4. The MSE is _____.  2
24. The least squares criterion is _____   min Σ(yi – ŷi)2
25. The numerical value of the coefficient of determination   can be larger or smaller than the coefficient of correlation
26. The primary tool or measure for determining whether the assumed regression model is appropriate is _____   residual analysis
27. The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the _____.   Coefficient of determination
28. The standardized residual is provided by dividing each residual by its   standard deviation

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