Jasmin is concerned about how much energy she uses to heat her home, so she keeps a record of the natural gas her furnace

consumes for each month from October to May. Because the months are not equally long, she divides each month's

consumption by the number of days in the month. She wants to see if there is a relationship between x = average temperature (in

degrees Fahrenheit) and y = average gas consumption (in cubic feet per day) for each month. Here is a scatterplot along with the

regression line y=1425 - 19. 87x.

Suppose that the average temperature in the current month is 10 degrees warmer than the previous month. About how much less gas should jasmine expect to use this month than the previous month?

In statistics, linear regression is a method for modeling the relationship between a scalar response and one or more explanatory variables. Simple linear regression is used when there is only one explanatory variable; multiple linear regression is used when there are multiple explanatory variables.

We have,

regression line y = 1425 - 19. 87x.

The average temperature in the current month is 10 degrees warmer than the previous month.

so, the average temperature x = x + 10,

then, the average gas consumption (in cubic feet per day) for each month

y = 1425 - 19. 87(x+10).

y = 1425 - 19. 87x - 198.7

y = 1,226.3 - 19. 87x

Hence, the less gas should jasmine expect to use this month than the previous month y = 1,226.3 - 19. 87x.

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