Answer:
Step-by-step explanation:
<em>Key Differences Between Covariance and Correlation
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<em>The following points are noteworthy so far as the difference between covariance and correlation is concerned:
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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.
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<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.
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<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.
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You can find more here: http://keydifferences.com/difference-between-covariance-and-correlation.html#ixzz4qg5YbiGj
Use the distributive property for the left side first
3.2=1/2q+5.65
subtract 5.65 from both sides
-2.45=1/2q
divide both sides by 1/2
q=-4.9
You can just put the 10 over 1 and multiply across. Then simplify! Hope this helps (:
So first y do is is add 5+7 and add 3/10 + 7/10 equal 10/10 so 5+7 + 1 is 13 she used 13 bags of flour the second week she used 1 more than last week
