Answer:
<span>y=<span><span><span>log<span>(x)</span></span>−<span>log<span>(50000)</span></span></span><span>log<span>(0.8)</span></span></span></span>
Explanation:
write as : <span>y=50000<span><span>(0.8)</span>x</span></span>
Taking logs:
<span><span>log<span>(y)</span></span>=<span>log<span>(50000)</span></span>+<span>log<span>(.<span><span>(0.8)</span>x</span>.)</span></span></span>
But <span>log<span>(.<span><span>(0.8)</span>x</span>.)</span></span> is the same as <span>x<span>log<span>(0.8)</span></span></span>
Thus
<span>x=<span><span><span>log<span>(y)</span></span>−<span>log<span>(50000)</span></span></span><span>log<span>(0.8<span>)
</span></span></span></span></span>Now swap the x'x and the y's giving:<span><span><span><span><span>
</span></span></span></span></span>
<span>y=<span><span><span>log<span>(x)</span></span>−<span>log<span>(50000)</span></span></span><span>log<span>(0.8<span>)
my teacher helped a little bit
</span></span></span></span></span>
Answer:
53
Step-by-step explanation:
To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.
Solve (2x - 1)(3x^2 - 9x - 4)
=
Answer: 0.6763 cm∧2
Step-by-step explanation: Variance is one of the measures of dispersion which is the the second central moment in probability. It is defined as a measure of by how much the values in a data set differs from the mean of the values. Thus it is the average of the squares of the deviations from the mean as this ensures that both the negative and positive deviations do not cancel each other out.
The sample variance would be calculated as follows:
Population size is 7 (5.6 4.9 6.0 5.1 5.5 5.1 7.5)
Mean: Sum of samples / population size ;
(5.6 4.9 6.0 5.1 5.5 5.1 7.5) / 7 = 5.67
Applying the formula for variance: [ Summation (x - mean) ] / population size =( |5.6 - 5.67| + |4.9 - 5.67| + |6.0 - 5.67| + (|5.1 - 5.67|) * 2 + | 5.5 - 5.67| + |7.5 - 5.67|) / 7 = 0.6763 cm^2
