Let
x-----------> the distances measured in miles
<span> Abby method
1)2x
2)2x-20%------> 2x-0.4x
</span>algebraic expression<span>-----> [2x-0.4x]---------> 1.6x
</span> Renato method
1)x/5
2)(x/5)*8-------> 8x/5
algebraic expression----->[8x/5]---------------> 1.6x<span>
Part B)
</span>[2x-0.4x]=[8x/5]---------> 1.6x=8x/5--------> 5*(1.6x)=8x-------> 8x=8x
therefore
<span>the two methods give the same answer
the answer is 1.6x</span>
Answer: There is not a good prediction for the height of the tree when it is 100 years old because the prediction given by the trend line produced by the regression calculator probably is not valid that far in the future.
Step-by-step explanation:
Years since tree was planted (x) - - - - height (y)
2 - - - - 17
3 - - - - 25
5 - - - 42
6 - - - - 47
7 - - - 54
9 - - - 69
Using a regression calculator :
The height of tree can be modeled by the equation : ŷ = 7.36X + 3.08
With y being the predicted variable; 7.36 being the slope and 3.08 as the intercept.
X is the independent variable which is used in calculating the value of y.
Predicted height when years since tree was planted(x) = 100
ŷ = 7.36X + 3.08
ŷ = 7.36(100) + 3.08
y = 736 + 3.08
y = 739.08
Forward prediction of 100 years produced by the trendline would probably give an invalid value because the trendline only models a range of 9 years prediction. However, a linear regression equation isn't the best for making prediction that far in into the future.
Answer:
isoselses!
Step-by-step explanation:
i hope that helps
Answer:
The correct options are:
Interquartile ranges are not significantly impacted by outliers.
Lower and upper quartiles are needed to find the interquartile range.
The data values should be listed in order before trying to find the interquartile range.
The option Subtract the lowest and highest values to find the interquartile range is incorrect because the difference between lowest and highest values will give us range.
The option A small interquartile range means the data is spread far away from the median is incorrect because a small interquartile means data is nor spread far away from the median
