Regression analysis was applied between sales (in AUD1000) and advertising (in AUD100) and the following regression function was obtained: [Y = 500 + 4 X]. Based on the above estimated regression line if advertising is $10,000, then the point estimate for sales (in dollars) is
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Regression analysis was applied between sales (Y in $1000) and advertising (X in $10,000), and the following estimated regression equation was obtained Based on the above estimated regression line if advertising is $10,000, then the point estimated for sales (in dollars) is a.503 b. 5030 c. 50,300 d.503,000 Ÿ=500+ 3x
Regression analysis was applied between sales (in $1,000) and advertising (in $100), and the following regression function was obtained. ^y = 80 + 6.2x Based on the above-estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is $62,080 $142,000 $700 $700,000
2. Regression analysis was applied between sales (in $1,000) and advertising (in $100), and the following regression function was obtained Y=80+6.2x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is According to my quiz the answer is 700,000 , please help me figure out how.
#1 In simple linear regression, r is the: a) coefficient of determination. b) mean square error. c) correlation coefficient. d) squared residual. #2 In regression analysis, with the model in the form y = β0 + β1x + ε, x is the a) estimated regression equation. b) y-intercept. c) slope. d) independent variable. #3 A regression analysis between sales (y in $1,000s) and advertising (x in dollars) resulted in the following equation. ŷ = 40,000 + 3x The above equation...
1. If an estimated regression line has a y-intercept of 10 and a slope of 4, then when x = 2 the actual value ofy is: а. 18 b. 15 с. 14 d. unknown 2. Given the least squares regression line v = 5- 2x: a. the relationship between x and y is positive b. the relationship between x and y is negative. c. asx decreases, so does y. d. None of these choices. 3. A regression analysis between weight...
Regression analysis was applied between sales data (y in $1000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8x n = 17 SSR = 225 SSE = 75 sb1= .2683 The point estimate of the population slope β1 is 1.8. options: True or False
A regression analysis between sales (Y in $100) and advertising (X in dollars) resulted in the following equation = 30,000 + 6 X The above equation implies that an a. increase of $1 in advertising is associated with an increase of $600 in sales b. increase of $1 in advertising is associated with an increase of $6 in sales c. increase of $1 in advertising is associated with an increase of $6,000 in sales d. None of the above
NARRBEGIN: Exhibit 12-04 Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. Y 12+1.8x SSR 225 SSE 75 Se-0.2683 NARREND 80. Refer to Exhibit 14-4. To perform an F test, the p-value is a. less than.01 b. ibetween.01 and.025 c. between.025 and.05 d jbetween.05 and 0.1 PTS: 1 TOP: Regression Analysis ANS: D
b Multiple Choice 14-019 A regression analysis between sales u in $tooo) and advertising (x in dollars) resulted in the following equation: Y = 30,000 + 4x The above equation implies that an O a. increase of $1 in advertising is associated with an increase of $4 in sales. O b. increase of $1 in advertising is associated with an increase of $4000 in sales. O c. increase of $4 in advertising is associated with an increase of $4000 in...
1. In regression analysis, the Sum of Squares Total (SST) is a. The total variation of the dependent variable b. The total variation of the independent variable c. The variation of the dependent variable that is explained by the regression line d. The variation of the dependent variable that is unexplained by the regression line Question 2 In regression analysis, the Sum of Squares Regression (SSR) is A. The total variation of the dependent variable B. The total variation of the independent variable...