Scores turned in by an amateur golfer at the Bonita Fairways Golf Course in Bonita Springs, Florida, during and are as follows: Season: 7377787674727476 Season: 7069747684797078a. Calculate the mean (to the nearest whole number) and the standard deviation (to decimals) of the golfer's scores, for both years. MeanStandard deviationMeanStandard deviationb. What is the primary difference in performance between and

Complete question is;

Scores turned in by an amateur golfer at the Bonita Fairways Golf Course in Bonita Springs, Florida, during 2005 and 2006 are as follows:

2005 Season: 73 77 78 76 74 72 74 76

2006 Season: 70 69 74 76 84 79 70 78

A) Calculate the mean (to the nearest whole number) and the standard deviation (to decimals) of the golfer's scores, for both years.

B) What is the primary difference in performance between 2005 and 2006? What improvement,

if any, can be seen in the 2006 scores?

A) 2006 mean = 75

2005 mean = 75

2006 standard deviation = 5.2644

2005 standard deviation = 2.0702

B)The primary difference is that variation is higher in the 2006 season than the 2005 season.

Step-by-step explanation:

A) Mean is the sum of all scores divided by the number of scores.

Thus;

μ_2005 = (73 + 77 + 78 + 76 + 74 + 72 +74 + 76)/8 = 75

Similarly;

μ_2006 = (70 + 69 + 74 + 76 + 84 + 79 + 70 + 78)/8 = 75

Now, variance is calculated by the sum of the square of mean deviations divided by (n - 1)

Thus;

2005 Variance = ((73-75)² + (77-75)² + (78-75)² + (76-75)² + (74-75)² + (72-75)² + (74-75)² + (76-75)²)/(8-1) = 4.2857

2006 Variance = ((70-75)² + (69-75)² + (74-75)² + (76-75)² + (84-75)² + (79-75)² + (70-75)² + (78-75)²)/(8 - 1) = 27.7143

Now, standard deviation is the square root of variance.

Thus;

2005 standard deviation = √4.2857 = 2.0702

2006 standard deviation = √27.7143 = 5.2644

B) The primary difference is that variation is higher in the 2006 season than the 2005 season.

Also,

add an image

step-by-step explanation:

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )