Answer:
1) Rizza's average speed is 27 km/h.
2) The distance between Barretto and Pampanga is 294 km.
3) Aaron's journey was 3.5 hours long.
Step-by-step explanation:
1) Time taken to cove the distance = 20 minutes
60 minutes = 1 hour
So that, 20 minutes = 
hours
= 
hours
Distance covered = 9 km
But,
speed = 
Rizza's average speed = 9 ÷
= 9 x 3
= 27 km/h
Rizza's average speed is 27 km/h.
2) Time taken = 3.5 h
the car's average speed = 84 km/h
Distance between Barretto and Pampanga = average speed x time taken
= 84 km/h x 3.5 h
= 294 km
The distance between Baretto and Pampanga is 294 km.
3) The uniform speed implies that Aaron did not accelerate during his journey.
Distance covered = 210 km
uniform speed = 60 km/h
time taken = 
= 
= 3.5 hours
Aaron's journey was 3.5 hours long.
<h3>
Answer: 5/162</h3>
===================================================
Work Shown:
A = probability of landing on an odd number
A = 5/9 because there are 5 odd numbers out of 9 total
B = probability of getting heads
B = 1/2
C = landing on 3 on the spinner
C = 1/9
Multiply the values of A,B,C
A*B*C = (5/9)*(1/2)*(1/9) = (5*1*1)/(9*2*9) = 5/162
Note: The fraction 5/162 converts to the approximate decimal 0.03086 which then converts to 3.086%, when rounding to the nearest tenth of a percent we get 3.1%
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
If she is only interested in creating a small number of clusters.The type of clustering method that would work best is: k-means clustering.
<h3>What is k-means clustering?</h3>
k-means clustering can be defined as a quantization techniques that is used to divide or seperate or data into number of groups in which each of the observation is linked to a prototype cluster or vectors.
Based on the given scenario k-means clustering method is the best choice as this will result in efficiency and make her work faster and easier.
Therefore the type of clustering method that would work best is: k-means clustering.
Learn more about k-means clustering here:brainly.com/question/17241662
Answer:
I think the answer is A from what I remember.
Step-by-step explanation:
the polynomial should be put in order based on the number in its power. it could not be added or subtracted if the power is not the same.
