Notice that form 3 pm to 6:30 pm 3.5 hours have passed.
Since the function
represent the average number of cars that pass through an intersection x hours after 3:00 p.m, we are going to replace
with 3.5 to find <span>the average number of cars that pass through the intersection at 6:30 p.m
</span>
We can conclude that the <span>average number of cars that pass through an intersection at 6:30 pm is approximately 117. </span>
The given polynomial is
As this is a fourth degree polynomial, we can not directly use any formula, So here we will use hit and trial method
check whether x=-1 is a zero of the polynomial or not
Hence x=-1 is not a zero.
Now check whether x=-2 is a zero of the polynomial or not
Hence x=-2 is a zero of the polynomial.
Hence we can re-write the polynomial as
Hence the required binomial is (x+2)
All the interior angles always sum up to 180. It doesn’t matter if they are all acute angles.
The very first thing to do in every correlation activity is to plot the gathered data points in a scatter plot. It is better to use software tools like MS Excel because they have a feature there that uses linear regression like that one shown in the picture.
Once you plot the data points, make a trendline. You are given with options. If you want a linear function, then you will have a linear model with a function equation of y = 0.2907x + 2.2643. It has a correlation coefficient of 0.9595. That's a strong correlation already. The R² value tells how good your model fits the data points. If you want to increase the R², a better model would be a quadratic function with the equation, y = -0.0209x²+0.506x+2.0232. As you can see the R² increase even more to 0.9992.
Answer:
a. Since 10 out of 70 had a tag we can write the ratio 10:70. We need to solve for x in 90:x. x = 630 so the answer is 630.
b. An assumption he could have made is that the number of deer that had a tag and the total amount of deer were directly proportional.
