Producing Dat Designing Samples

Experimental probability is the fraction of times an event actually occurs and theoretical probability is the fraction of times we would expect an event to occur. I did not know these terms until after I read the article. The article shown me that there are so many ways to approach probability. It gave me an idea of using the line chart as an assessment of understanding probability. The number cube game gave me an idea of teaching probability by playing Yahtzee

10. A professor at Kaplan University claims that the average age of all Kaplan students is 36 years old. Use a 95% confidence interval to test the professor 's claim. Is the professor 's claim reasonable or not? Explain.

A listing of all possible outcomes of an experiment and the corresponding probability is called:

When conducting a study, it is impossible to collect data from the whole population, therefore it is important to select a representative sample because sampling makes it possible to select a representative for study and discover things that apply to many more people who are not studies (Maxfield & Babbie, 2012). The purpose of sampling is to generate a set of individuals or other entitles that give us a valid picture of all such individual or other entitles. It is important to generalise from a sample to unobserved population the sample in intended to represent. Therefore, when selecting a group of subjects for study, it is important to ensure that we represent some larger population.

A statistical hypothesis itself is an assumption for a set of data which may or may not be true. The null and alternate hypotheses would be:

This means that for every 5 people who didn’t drink coffee and got sick, there would only be 4 who got sick in the coffee drinking group. This is very significant because this means that if you had a group of 100,000 people get sick. You could have 20,000 of them not get sick, all because they were drinking coffee! WOW! Just because of their choice in drinks! But the question is if we can trust these numbers. To see if we can trust these numbers they tested them with a “confidence interval”. A confidence interval is a measure of how right an answer is given any case where its claims can be tested. They tested everything they found to a 95% confidence interval. This means that if their information or answers weren’t correct in at least 95 of 100 cases they tried, then they didn’t trust the information and didn’t use it. This is important because it means that the information (across 9 years of observation) is very accurate as it was tested over and over again to make sure it works. So over all, they found that drinking coffee can help reduce your chances of getting sick. This isn’t true for every kind of sickness, of course, but as a general trend it is shown to be helpful for people to drink coffee. This is important to you because drinking coffee is shown to improve your health. it’s an easy thing to do, it’s been researched with a large, long, and accurate study

This is an important problem and is addressed by the Central Limit Theorem. For now, let us not bother about what this theorem states but we will look at how it could help us in answering our question.

When determining a confidence interval for this sample/population, it is necessary to remember that the interval must be one sided. Everyone to the right of a given vertical line on the distribution will be included in the result, while everyone to the left would be excluded. The vertical line represents a selected price for the

However, the statistical definition of “confidence” is much different than the conversational definition. In statistics, confidence does not judge how “correct” a

This is an important problem and is addressed by the Central Limit Theorem. For now, let us not bother about what this theorem states but we will look at how it could help us in answering our question.

-The sampling distribution is a distribution of the results if all possible samples are selected, where as a population distribution is the distribution of a particular variable of interest.

The difference between a hypothesis and prediction is that a hypothesis is an explanation to a question or problem in nature, while predictions are guesses of what could happen.

According to Hair et al. (2003), in the research, the sampling process enables identifying, developing and understanding an interested object that need to be determined (p.333). Hence, in order for the researcher to carry out the sampling appropriately, advantages and disadvantages of the various sampling methods should be considered along with the theoretical component of the study (Hair et al. 2003, p. 368 f). Theoretically, the sampling procedure is divided into two major types which consist of probability and nonprobability sampling. In probability sampling, individuals have a known chance of being selected. While, in non-probability sampling, individuals do not have a known possibility to be selected (Sekaran 2003, p. 269 f). Also, the different sampling methods provide different advantages and disadvantages. Hence, the researcher should consider this point before choosing the sampling method for the

They are just guessing if it is a random event, many people have a gut feeling, but unless you are using probabilities based on something such as math or data then it is just a guess. However, you can calculate the chances of an events happening by multiplying the chances when you are observing probabilities.

• Second, decide the methods and design of the sample you’re going to draw and the specific attributes or concepts you’re trying to measure.