Answer:
The running time is quadratic (O(n²) )
Step-by-step explanation:
For the set up, we have a constant running time of C. The, a log-linearsorting is called, thus, its execution time, denoted by T(n), is O(n*log(n)). Then, we call n times a linear iteration, with a running time of an+b, for certain constants a and b, thus, the running time of the algorithm is
C + T(n) + n*(a*n+b) = an²+bn + T + C
Since T(n) is O(n*log(n)) and n² is asymptotically bigger than n*log(n), then the running time of the algorith is quadratic, therefore, it is O(n²).
Your answer would be 75x96 which gets you Justin bieber + Selena gomez which gets you 6900
This is simple division.
You know 2 bottles cost a total of $7.96. You want to find how much each individual bottle costs on it's own. You take 7.96, and divide it by 2 in order to find this. 7.96/2=3.98
Each bottle costs $3.98
The third choice is correct
~Hope this helps!
Answer: 7/6 or 1 1/6
Step-by-step explanation:
First, convert all the fractions so that they have like denominators, using LCM, or Least Common Multiple. This means that you must find the least common multiple that the two denominators share, which in this case is 6. 6 will become the denominator for both fractions At this point, to find the numerators, I use my own sort of mental method to get the answer. I first take the LCM and divide it by the denominator of one of the fractions (let's do 1/3) which would give me 2. Then multiply this number by the numerator of the same fraction you started with, which is 2 again. So, your new fraction is 2/6. Since 5/6 already has a denominator of 6, you don't have to do anything, but if it did, you would just repeat the same method. At this point, the new problem is 5/6 + 2/6. Add like normal, and leave in either improper form (7/6) or mixed numbers (1 1/6) based on instructions.
Answer:
Kindly check explanation
Step-by-step explanation:
Given that :
Correlation Coefficient (r) = 0.989
alph=0.05
Number of observations (n) = 8
determine if there is a linear correlation between chest size and weight.
Yes, there exists a linear relationship between chest size and weight as the value of the correlation Coefficient exceeds the critical value.
What proportion of the variation in weight can be explained by the linear relationship between weight and chest size?
To determine the the proportion of variation in weight that can be explained by the linear regression line between weight and chest size, we need to obtain the Coefficient of determination(r^2) of the model.
r^2 = square of the correlation Coefficient
r^2 = 0.989^2 = 0.978121
Hence, about 0.978 (97.8%) of the variation in weight can be explained by the linear relationship between weight and chest size.
