Answer:y=7
Step-by-step explanation:x=2+7=8
Answer:
Although, my life can't end here, life has so much more to offer me. I collected the last of my strength to swim up to to surface where I can be released from the horrors of the deep waters. I swallowed gulps of the chlorine water and my eyes ached from being open for to long. I wrestled the clenching fists of the water that urged me to stay with them. I swam, and swam, swam, until I reached my hands out for help, and that's when I knew I made it. I could feel hands from above grasping me and helping me climb out of the bitter cold pool. I ran into my mother's arms, as a gush of relief spread through my body. The relief of staying in my mothers arm, that helped me forget the isolated pits of the dark waters.
Sorry if it's not that good! I'm not the best writer, and you don't have to use this ending paragraph if you don't want to!!
Answer:
they are complementary
Step-by-step explanation:
Answer:
50 grams
Step-by-step explanation:
Let the amount of cheese required by the recipe be "x"
Ann increased 60% from original amount and then used up 80 grams. Thus:
<em>Original, increased by 60%, became 80</em>
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This translated to algebraic equation would be:
x + 0.6x = 80
<u>Note:</u> 60% = 60/100 = 0.6
So we can solve the above equation for "x" and get our answer. Shown below:
Hence,
the recipe required 50 grams of cheese
Answer:
II. The sum of the residuals is always 0.
Step-by-step explanation:
A least squares regression line is a standard technique in regression analysis used to make the vertical distance obtained from the data points running to the regression line to become very minimal or as small as possible.
For any least-squares regression line, the sum of the residuals is always zero.
Basically, residuals are used to measure or determine whether or not the line of regression is a good fit or match for the data by subtracting the difference between them i.e the predicted y value and the actual y value, for the x value respectively.
Hence, the statement about residuals which is true for the least-squares regression line is that the sum of the residuals is always zero (0).
