Part A.
7.8 is a rational number between 7.7 and 7.9.
It is rational because it can be written as a fraction of integers, such as 78/10.
Part B.
sqrt(60) = 7.75
sqrt(60) cannot be written as a fraction of integers. It is a decimal number that never ends and never repeats.
Area=legnth times width=31
the frame must go around the frame with equal legnth, x
so
the total area (meaning the frame +picture) is 5+x by 6+x
31=(5+x)(6+x)
expand
31=30+11x+x^2
minus 31 both sides
0=-1+11x+x^2
x^2+11x-1=0
answer is third one
Answer:
C. 12
Step-by-step explanation:
Remember 0 to 4 turn it back
5 or above give it a shove.
Since 12.444 is around 0 to 4.
We can round that to 12.
Answer:
How far should he ride on each of the four days to reach his goal?
1st day:
miles
2nd day:
miles
3rd day:
miles
4th day:
miles
Step-by-step explanation: As the problem says,
is the number of miles he rides on the first day. Let's start off with that.
1st day:
miles
He want to ride 1.5 times as far as he rode the day before... no 1.5 more, but 1.5 <em>times</em> as far as he rode the day before; you would multiply 1.5 with the previous day's length.
2nd day: 
Then you multiply
to
to get the third day's.
3rd day: 
4th day: 
----------------------------
Phew! Gavin wants to ride a total of 65 miles over these four days, so if Gavin added all the miles of the four days, he should get 65...
1st+2nd+3rd+4th=65




Yes! Now that we've got the hard part done... substitute 8 for ever single
.
1st day:
miles
2nd day:
miles
3rd day:
miles
4th day:
miles
-------------------------
Checking my answer:
Just add the miles!


✓
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Hope that helps! :D
Answer:
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Step-by-step explanation:
We are given a linear regression model that relates daily high temperature, in degrees Fahrenheit and number of lemonade cups sold.

Where y is the number of cups sold and x is the daily temperature in Fahrenheit.
Residual value:
A residual value basically shows the position of a data point with respect to the regression line.
A residual value of 0 is desired which means that the regression line best fits the data.
The Residual value is calculated by
Residual = Observed value - Predicted value
The predicted value of number of lemonade cups is obtained as

So the predicted value of number of lemonade cups is 23 and the observed value is 21 so the residual value is
Residual = Observed value - Predicted value
Residual = 21 - 23
Residual = -2
The negative residual value indicates that the data point lies below the regression line.