Let's actually find the line of best fit...
m=(nΣyx-ΣyΣx)/(nΣx^2-ΣxΣx)
m=(11*836-130*55)/(11*385-3025)
m=2046/1210
m=93/55
b=(Σy-93Σx/55)/n
b=(55Σy-93Σx)/(55n)
b=(7150-5115)/(55*11)
b=185/55, so the line of best fit is:
y=(93x+185)/55
A) The approximate y-intercept (the value of y when x=0) is 185/55≈3.36.
Which means that those who do not practice at all will win about 3.36 times
B) y(13)=(93x+185)/55
y(13)≈25.34
So after 13 months of practice one would expect to win about 25.34 times.
Answer:
t = 1.107
Step-by-step explanation:
Finding the solution using derivatives involves finding the lower zero of the quadratic that is the second derivative of the given function. That second derivative will be ...
f''(t) = 12(1.6714)t^2 -6(22.45)t +2(62.27)
= 20.0568t^2 -134.7t +124.54
= 20.0568(t -3.35796)² -101.619 . . . . rewrite to vertex form
Then f''(t) = 0 when ...
t ≈ 3.35796 -√(101.619/20.0568) ≈ 1.10706
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The solution is perhaps more easily found using a graphing calculator to find the peak of the first derivative. (See attached.) It tells us ...
t ≈ 1.107
1.1 years after the beginning of 1998 is about 1.2 months into 1999.
Rents were increasing most rapidly in early February of 1999.
Independent variable is the predictor variable which is the height and dependent variable is the response variable which is weight in this scenario.
The square of correlation coefficient gives the coefficient of determination. It is denoted by R² (R squared).
We are given:
R = 0.75
So,
R² = 0.75²
R² = 0.5625
R² = 56.25 %
The coefficient of determination tells how much of the trend of dependent data can be explained by the independent data using the linear regression model. So in the given case, Height can explain 56.25% of the trend in the weight.
Step-by-step explanation:
9x^3+6x^2-3x
Cannot be simplified because there are no like terms but can be factored as
=3x(3x−1)(x+1)
8.3% is ur answer hope this helps