<span>C' x + 1 < 5 ; x < 4 ...............................................................................................</span>
Answer:
I think its would be d
Step-by-step explanation:
the distributive property
Hello.
First off, you can factor this equation to find the missing x-intercept.
y = x² + 8x + 12
y = (x + 6)(x + 2)
Then, you can use the zero product property to find the x values.
x + 6 = 0 x + 2 = 0
- 6 - 6 - 2 - 2
x = -6 x = -2
As an ordered pair: (-6, 0) and (-2, 0)
Thus, your answer is x = -6, -2.
X= number of adult tickets
y=number of child tickets.
we can suggest this system of equations:
x+y=118 ⇒x=118-y
7.5x+3y=696
we can solve this system of equations by substitution method:
7.5(118-y)+3y=696
885-7.5y+3y=696
-4.5y=-189
y=-189/-4.5
y=42
x=118-y
x=118-42
x=76
Answer: the number of adult tickets sold was 76.
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:
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<em>
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<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.
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<em>3. The value of correlation takes place between -1 and +1. Conversely, the value of covariance lies between -∞ and +∞.
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<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
