9 is 9600 cause if you divide 48 by 5 you get 9.6 then times 9.6 times 1000 you get 9600
i cant solve number 10 its really blurry and hard to see
Answer:
d≈7.07
Step-by-step explanation:
Using the formulas
P=4a
d= \sqrt{2} a
Solving ford
d= \sqrt{2} P/4=2·20
/4≈7.07107
Answer:
<em>D. 30</em>
Step-by-step explanation:
6 ÷ 1/5 <em>-given expression</em>
6 x 5 <em>-use the </em><em>keep, change, flip </em><em>method, where you</em><em> keep</em><em> the </em><u><em>first </em></u><em>number, </em><em>change</em><em> the </em><u><em>division sign</em></u><em> to a </em><u><em>multiplication sign</em></u><em>, and </em><em>flip</em><em> the f</em><u><em>raction</em></u><em> that comes after the symbol.</em>
<em>6 x 5 = 30</em>
<em>30 is the answer</em>
Answer:
Step-by-step explanation:
<em>Key Differences Between Covariance and Correlation
</em>
<em>The following points are noteworthy so far as the difference between covariance and correlation is concerned:
</em>
<em>
</em>
<em>1. A measure used to indicate the extent to which two random variables change in tandem is known as covariance. A measure used to represent how strongly two random variables are related known as correlation.
</em>
<em>2. Covariance is nothing but a measure of correlation. On the contrary, correlation refers to the scaled form of covariance.
</em>
<em>3. The value of correlation takes place between -1 and +1. Conversely, the value of covariance lies between -∞ and +∞.
</em>
<em>4. Covariance is affected by the change in scale, i.e. if all the value of one variable is multiplied by a constant and all the value of another variable are multiplied, by a similar or different constant, then the covariance is changed. As against this, correlation is not influenced by the change in scale.
</em>
<em>5. Correlation is dimensionless, i.e. it is a unit-free measure of the relationship between variables. Unlike covariance, where the value is obtained by the product of the units of the two variables.
</em>
You can find more here: http://keydifferences.com/difference-between-covariance-and-correlation.html#ixzz4qg5YbiGj