Essay On Probability and Statistics Discussion

Theoretical probability: If a person was to flip a coin, he/she could have a 1 in 2 or a 50/50 chance of landing on heads or tails. Another good example would be I used to play role playing games when I was in High School that used a 20 sided die. That would mean I would have had a 1/20 chance of getting any one of the numbers. This is theoretical because I do not need to observe the outcome to determine the results. I can just do the math. Relative frequency probability: I eat breakfast every morning. Four out of seven mornings I like to have an orange juice with my breakfast. The other three mornings I like to have an energy drink. The relative frequency of my orange juice mornings is 4/7 and my energy drinks 3/7. It is relative because there are a total of 7 days and the rate is founded on the entire figure of each of the drinks used in that week. Subjective probability: If I make good grades in school, I will be successful. This is subjective because it is based upon what I know; good grades can get anyone a good job. Maybe that is why I like making good grades. Another good example would be, while I am at work - if I complete all my tasks successfully, I will get a raise.
b) The topic is rising gas prices and can the president do something to change them.
c) The sample is 957 adults nationwide with a margin of error of +-3.d) There are 3 responses in the study. One, "Can do a lot about", of which 48% answered. Two, "Beyond control", of which 43% of the people answered. Three, "Unsure", 9% of people answered. So a majority of people believe that the president can do something about gas prices.
e) I do not think there is a weighty variance in the responses.
f) If the size of the sample is small, the outcomes may not be as accurate. I think a larger sampling size would have proved more accurate in what total population think about the president’s influence on gas prices. As weighty as it is in making sure all the numbers are being accurately recorded, it does seem like the sampling development, is by far the utmost significant variable in the equation. Sampling can skew the results, determine the outcome of results, and even sway readers into believing in something just because the statistics say one thing.
Q3.
Depending on where one takes the sample, the results could easily lean one way or the other. Let us say your sample was from San Francisco. I am sure the mainstream would be in approval of the same sex marriage. What if your sample was from Salt Lake City? I am sure it would be quite the opposite. So you are right about biased groups affecting the numbers. Anyone could pretty much make the figures come out the way they sought just by choosing the right sample.
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