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Male life expectancy. Refer to Problem
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- Questions 1-30 refer to the following scenario: A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table. Why would it be a bad idea to use the linear regression line to make forecasts when looking at the data? a The linear trend line does not capture the fact that, on average, sales go up. b The linear trend line does not capture the seasonality of the data. c Both a. and b. are correct. d None of the above. Decomposition forecasting decomposes data into which two factors? a Slope and intercept b Trend and seasonality c Past and future data d Decom and position In decomposition forecasting, the calculated seasonal index for the first bi-annual period is a 0.71 b 1.29 c 0.89 d 1.41 In decomposition forecasting, the calculated seasonal index for the second bi-annual period is a 0.89 b 0.71 c 1.41 d 1.29 Using only the regression line, the…arrow_forwardQuestion 16 Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained. = 500 + 4x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is _____. $505,000 $900 $40,500 $900,000arrow_forwardQuestion 5 - what is the y-intercept of the equation of the regression line y = 36.11695 + 7.20508x A. 7.20508 B. 36.11695 C. Y D. X Question 6 - what is the slop of the equation of the regression line y = 36.11695 + 7.20508x? A. 7.20508 B. 36.11695 C . Y D . Xarrow_forward
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