Answer:

Step-by-step explanation:
Given,
The number of litres consumed by car in (x+20) km = 6,
So, number of litres consumed by car in 1 km =
,
Now, number of litres consumed by car in 100 km = 100 × litres consumed by car in 1 km


Hence, the rate of fuel used by his car would be
litres per 100 km.
Answer:
see explanation
Step-by-step explanation:
In an arithmetic sequence the common difference d is
d = a₂ - a₁ = 10 - 8 = 2
To obtain the next term in the sequence add d to the previous term, that is
a₅ = 14 + 2 = 16
a₆ = 16 + 2 = 18
a₇ = 18 + 2 = 20
The next 3 terms in the sequence are 16, 18, 20
The n th term equation for an arithmetic sequence is
= a₁ + (n - 1)d
where a₁ is the first term and d the common difference
Here a₁ = 8 and d = 2, thus
= 8 + 2(n - 1) = 8 + 2n - 2 = 2n + 6
Answer:
The translation for the above map points is 8 units up and 5 units left.
Step-by-step explanation:

The translation for the above map points is 8 units up and 5 units left.
Step 1: 8 units up
(Add 8 to y co-ordinate) 
Step 2: 5 units left
(subtract 5 from x-co-ordinate) 
The equation of the regression line is
=9.1006+(-0.2157)x and the point prediction of ammonium concentration for 25ml/h is
=3.7081
<h3>What is meant by regression line?</h3>
A regression line is an approximation of the line that describes the true, but unknown, linear connection between two variables. The regression line equation is used to predict (or estimate) the value of the response variable from a given value of the explanatory variable.
In statistical modeling, regression analysis is a collection of statistical techniques for estimating the associations between a dependent variable and one or more independent variables.
a)
=β₀+β₁x
Where,
β₁=
/
-------(1)
Where,
= -341.959231
1585.230769
By substituting these values in equation 1 we get,
β₁= -0.2157
β₀=
-β₁x
Where,
=∑
/n
∑
=52.8
n=13
Then,
=4.0615
=∑
/n
∑
=303.7
n=13
=23.3615
β₀=4.0615-(-0.2157)(23.3615)
=9.1006
Therefore, β₀=9.1006
The regression equation is
=9.1006+(-0.2157)x
If x=25,
=9.1006+(-0.2157)(25)
=3.7081
To know more about regression line, visit:
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