The Centers for Disease Control and Prevention Office on Smoking and Health (OSH) is the lead...

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The Centers for Disease Control and Prevention Office on Smokingand Health (OSH) is the lead federal agency responsible forcomprehensive tobacco prevention and control. OSH was establishedin 1965 to reduce the death and disease caused by tobacco use andexposure to secondhand smoke. One of the many responsibilities ofthe OSH is to collect data on tobacco use. The following data showthe percentage of U.S. adults who were users of tobacco for arecent 11-year period

YearPercentage of Adults Who Smoke
122.7
221.9
321.2
420.8
520.8
620.2
719.3
820.6
920.6
1019.8
1118.9
  1. Use simple linear regression analysis to find the parametersfor the line that minimizes MSE for this time series. Do not roundyour interim computations and round your final answers to threedecimal places. For subtractive or negative numbers use a minussign. (Example: -300)

    y-intercept, b0 =

    Slope, b1 =

    MSE =
  2. One of OSH’s goals is to cut the percentage of U.S. adults whowere users of tobacco to 12% or less within nine years of the lastyear of these data. Does your regression model from part (b)suggest that OSH is on target to meet this goal?



    Use your model from part (b) to estimate the number of years thatmust pass after these data have been collected before OSH willachieve this goal. Round your answer to the nearest whole number.Yes or No

    years=

Answer & Explanation Solved by verified expert
3.7 Ratings (556 Votes)

(a)y-intercept, b0=22.4146

slope, b1=-0.2827

MSE=0.3471

(b) here slope=-0.2827, which change in dependent variable when unit change is done in independent variable.

so each year there will decrease in smoking by 0.2827%

so in 9 years there would decrease =9*0.2827=2.5443%, so  regression model from part (b) does not suggest that OSH is on target to meet this goal

required number of years=12/0.2827=42.4877 ( required number of 42 years, nearest whole number)

Regression Statistics
Multiple R 0.858998
R Square 0.737878
Adjusted R Square 0.708753
Standard Error 0.589118
Observations 11
ANOVA
df SS MS F Significance F
Regression 1 8.792818 8.792818 25.33511 0.000706
Residual 9 3.123545 0.347061
Total 10 11.91636
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 22.31455 0.380965 58.57372 6.21E-13 21.45274 23.17635
X Variable 1 -0.28273 0.05617 -5.0334 0.000706 -0.40979 -0.15566

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