Broiler Chicken Production. The graph and data that form the basis of this probl

Question

Broiler Chicken Production. The graph and data that form the basis of this problem are from the National ick en Council. Broiler chicken production is given on a yearly basis from 1960-2016 (estimated). The units r chicken production are in millions of pounds on a ready-to-cook basis. It includes federally inspected plus non-federally inspected/less condemnation. That sounds like a lot of chicken, but if 322 million U.S. citizens ate pound of chicken per week, that IS a lot of chicken. Some of the broilers are exported, and others turn up in strange places, like pet food. 1. At the end of this packet, find data on U.s. Broiler Production from the National Chicken Council. Examine the scatterplot of the data using the years 1960 through 2002 ONLY a. Inspect the scatterplot. Does the data look linear or exponential? b. What is the meaning of the y-intercept? Be sure to include units of neasure. Use technology to find the linear regression line through the original (x, y) data for the years 1960 2. through 2002 ONLY a. What is the equation of the linear regression line? Y- b. What is r? Is the line a good fit? 3. Use technology to find the exponential regression line through the original (x, y) data for the years 1960 through 2002 ONLY a. What is the equation of the exponential regression line? Y- b. What is r? Is the line a good fit? c. Which is the better model, linear or exponential? d. How do you know? e. ACCURATELY plot the line-of-best-fit onto the scatterplot. 4. Use the line-of-best-fit (from 3-c, above) to predict broiler production in 2016. a. What is the predicted broiler production in 2016? b. How well does the model match the data value given in the table? 5. Extend the scaterplot through 2016. Plot the data points from 2003 through 2016 onto the scatterplot you were provided 6. Recalculate the line-of-best-fit, using all the data points from 1960 through 2016. a. Which is a better model, linear or exponential? what is that equation? Y . a. What is r? Is this a good fit? b. How does this new line compare to the one you found earlier? c. Use your new equation to predict the broiler production in 2016. What does it predict? d. How well did the new model match the data value given in the table? e. using a different color, ACCURATELY plot the line-of-best-fit onto the scatterplot. Over

Explanation / Answer

DATA USED:

Solution1:

From scatterplot we can see

exponential increase.

Data look linear

there is meaning of y intercept

Solution2:

Go to data>Dataanalysis in regression

regression equation is

production=-1261748+644.1305*year

that is y=-1261748+644.1305*x

R sq=0.9164

91.64% variation in production is explained by model.

Good fit.

Year Production(Million pounds ) 1960 4,335 1961 4,944 1962 4,997 1963 5,269 1964 5,444 1965 5,877 1966 6,437 1967 6,552 1968 6,653 1969 7,175 1970 7,687 1971 7,724 1972 8,147 1973 7,962 1974 8,034 1975 8,020 1976 9,012 1977 9,279 1978 9,902 1979 10,926 1980 11,252 1981 11,868 1982 11,996 1983 12,326 1984 12,921 1985 13,520 1986 14,180 1987 15,413 1988 16,007 1989 17,227 1990 18,430 1991 19,591 1992 20,904 1993 22,015 1994 23,666 1995 24,827 1996 26,124 1997 27,041 1998 27,612 1999 29,468 2000 30,209 2001 30,938 2002 31,895

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