Answer: E
Conclusion E is most appropriate
Step-by-step explanation:
Rebounds and wins are positively correlated, but we cannot conclude that getting more rebounds causes more wins, on average.
Because if a scatterplot shows a strong, positive, linear relationship between two variables, then the two variables are positively correlated but there is no causation between them.
- She divided by 3 instead of the GCF.
- She didn’t complete step 4 and use the distributive property to be sure the expressions are equivalent.
- She didn’t undistribute the GCF from the original expression.
Explanation:
I just did it lol
Answer:
The relationship between x, Danielle's total sales for the month, and y, her monthly income is as shown;
y=0.06 x+500
Step-by-step explanation:
Step 1: Express the monthly income
The monthly income can be expressed as follows;
y=F+(R×x)
where;
y=her monthly income
F=fixed income per month
R=commission rate
x=total number of houses she sells
In our case;
Monthly income=y
F=$500 a month
R=6%=6/100=0.06
total number of houses she sell=x
replacing;
y=500+(6% of x)
y=500+(0.06×x)
y=500+0.06 x
The relationship between x, Danielle's total sales for the month, and y, her monthly income is as shown;
y=0.06 x+500
Cross multiply and divide
Put 191 over 238 (like a fraction) the put another fraction bar right next to your first fraction and put 100 on bottom (this equals a whole in % and it's right across from the total of 238 free throws) then multiply 191 by 100 then divide by 238 and that answer should be your the percent you need.
Answer:
It is C -14r+6p
Step-by-step explanation:
If you want the explanation just tell me
