Step-by-step Answer:
One of the properties of a least-squares regression line (line of best fit) is that the line always passes through the point (xbar, ybar).
Assuming the given "line of best fit" is a least-squares line, then we are given
a slope m=1.885 passing through (x0,y0)=(3.448,12.318).
Applying the standard point-slope formula:
(y-y0) = m (x-x0)
we get
y-12.318 = 1.885(x-3.448)
Expand and simplify,
y=1.885x -1.885*3.448 + 12.318, or
y=1.885(x) + 5.81852
(numbers to be rounded as precision dictates).
<h2><u>Part A:</u></h2>
Let's denote no of seats in first row with r1 , second row with r2.....and so on.
r1=5
Since next row will have 10 additional row each time when we move to next row,
So,
r2=5+10=15
r3=15+10=25
<u>Using the terms r1,r2 and r3 , we can find explicit formula</u>
r1=5=5+0=5+0×10=5+(1-1)×10
r2=15=5+10=5+(2-1)×10
r3=25=5+20=5+(3-1)×10
<u>So for nth row,</u>
rn=5+(n-1)×10
Since 5=r1 and 10=common difference (d)
rn=r1+(n-1)d
Since 'a' is a convention term for 1st term,
<h3>
<u>⇒</u><u>rn=a+(n-1)d</u></h3>
which is an explicit formula to find no of seats in any given row.
<h2><u>Part B:</u></h2>
Using above explicit formula, we can calculate no of seats in 7th row,
r7=5+(7-1)×10
r7=5+(7-1)×10 =5+6×10
r7=5+(7-1)×10 =5+6×10 =65
which is the no of seats in 7th row.
Answer: We reject the null hypothesis, and we use Normal distribution for the test.
Step-by-step explanation:
Since we have given that
We claim that
Null hypothesis : 
Alternate hypothesis : 
There is 5% level of significance.

So, the test statistic would be

Since alternate hypothesis is left tailed test.
So, p-value = P(z≤-2.31)=0.0401
And the P-value =0.0401 is less than the given level of significance i.e. 5% 0.05.
So, we reject the null hypothesis, and we use Normal distribution for the test.
Answer:A system of linear equations usually has a single solution, but sometimes it can have no solution (parallel lines) or infinite solutions (same line).
Step-by-step explanation:
Answer:

Step-by-step explanation:

Step 1: Open the brackets

Step 2: Bring the similar variables together

Step 3: Simplify by adding/subtracting the coefficients of the similar variables

Step 4: Rearrange as required.