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The population growth of a state since 2000 in millions of people is represented by a linear model. Using the trend line, y = 0...Asked by unf0rgettxble
The population growth of a state since 2000 in millions of people is represented by a linear model. Using the trend line, y=0.83x+30 , predict the population, y , in the year 2030. Let x=30 because the year 2030 is 30 years after the year 2000.(1 point)
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Answered by
unf0rgettxble
Your teacher surveyed the class to determine the number of hours that each student spent on social media. Your teacher created a table and scatterplot graph that displayed the number of hours, x , and the average final grade percentage, y , based on the hours. Using the equation of the trend line of the data, y=–7.2x+98.9 , predict the average final grade percentage, to the nearest whole number, if a student spent 10 hours on social media.
Answered by
unf0rgettxble
You are snow skiing down a mountain that is represented by the linear model y=–30x+6,000 , where y is the number of feet after x seconds. Predict your elevation level in feet if you ski 60 seconds.
Answered by
GPT 3.5
Using the linear model y=0.83x+30 and substituting x=30 for the year 2030, we can find the predicted population:
y = 0.83(30) + 30
y = 24.9 + 30
y = 54.9
Therefore, the population in the year 2030 is predicted to be approximately 54.9 million people.
y = 0.83(30) + 30
y = 24.9 + 30
y = 54.9
Therefore, the population in the year 2030 is predicted to be approximately 54.9 million people.
Answered by
GPT 3.5
Using the equation of the trend line provided, y = -7.2x + 98.9, and plugging in x = 10, we can calculate the predicted average final grade percentage:
y = -7.2(10) + 98.9
y = -72 + 98.9
y = 26.9
Rounding to the nearest whole number, the predicted average final grade percentage if a student spent 10 hours on social media is 27%.
y = -7.2(10) + 98.9
y = -72 + 98.9
y = 26.9
Rounding to the nearest whole number, the predicted average final grade percentage if a student spent 10 hours on social media is 27%.
Answered by
GPT 3.5
Using the linear model y = -30x + 6000 and substituting x = 60 for 60 seconds, we can find the predicted elevation level:
y = -30(60) + 6000
y = -1800 + 6000
y = 4200
Therefore, your elevation level in feet after skiing for 60 seconds is predicted to be 4200 feet.
y = -30(60) + 6000
y = -1800 + 6000
y = 4200
Therefore, your elevation level in feet after skiing for 60 seconds is predicted to be 4200 feet.
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