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RES 341 Discussion Questions wk 1-5

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What is an example of a hypothesis that you have used in your personal and/or professional
life? Please state what the hypothesis is, how you utilized it and what the outcome of the
situation was
A hypothesis is a proposed explanation of an observable phenomenon. I have used a
hypothesis in both my personal and professional life. Since a hypothesis cannot be proven,
being “human” means to have a free will, and be able to think for oneself; hypothesizing
happens quite often throughout the day. A dream is an example of an phenomenon that I
have every time that I sleep. Why do we have dreams is the question? (My hypothesis) We
have dreams because our brains are not fully developed, and we are in or are viewing another
or alternate realities to that of our own. This may or may not be true, but when I dream I
tend to try to keep a log of what I dream about, because I want to if possible one day write a
book based on the dreams that I have. Dreams have always been a mystery to us (human
kind) because one has no way of proving if the dreams are a way of showing someone of
something to come, or simply ones deepest desires. Dreams can be researched, but for every
person that is researched I would bet that the information that is received would be different
from the next (unless each of the subjects were watching the same movie or something of
that nature and then they might have similar dreams, and even then their dreams might not be
similar).
Are hypothesis (phenomenons) ways of saying that we (humans) do not know everything and
there is a higher power?

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Week 2
What are real-world examples of a sample and a population? Compare and contrast
probability sampling with non-probability sampling, providing an example of each.
When the analysis is a based on an entire group or in other words, all the members of the group
under study then the population is in place. A sample is taken when this population is too large to
measure each of the members. Think about it from this perspective, if they were investigating
Burger King Restaurants, all the Burger King restaurants in the world would be their population
but the sample would be those restaurants that you study.
Probability sampling is similar to pulling a number out of a hat. In the Burger King scenario if
data is needed from the restaurants, one way for obtaining the information would be to draw a
sample for a survey based on the zip codes of the specific restaurants. This would be a
probability sampling. However, in non-probability sampling the researcher could simply decide
to drive to a specific Burger King restaurant and obtain the information through the survey.
Although non probability sampling is a convenient way to conduct a survey, it is not as accurate
or rigorous as probability sampling methods.
In both sampling examples, only a portion of the full population is being observed. This helps
narrow down the results.
Describe the data scales best suited for presentation in a pie chart. How effective are the pie
charts that often accompany a newspaper article in explaining the statistics used in the
article? Where could a Pareto chart be used within your company?
Pie charts should be reserved for presenting general ideas regarding data values. Additionally,
pie charts should express only about three slices of the pie and ultimately represent parts of a
whole of the issue being displayed. The pie charts used in newspaper articles are not effective in
fully explaining the statistics in the article because often times, the data in pie charts are
generalized and rounded. By using the same generalized or rounded numbers in statistical
calculations, the computations could show errors. My organization could use Pareto charts when
examining the type of activity conducted when officers are injured. For instance, if managers
learn officers are experiencing increased injuries in accidents rather than assaults on officers,
managers may wish to provide additional training on driving techniques.
Does all data have a mean, median, or mode? Why or why not? When is the mean the best
measure of central tendency? When is the median the best measure of central tendency?
All data will have a mean and median but not necessarily a mode. In a particular data set if there
is not reoccurring values, the data set would not have a mode. Comparatively, mean and median
can always be calculated within a data set. The mean is the best measure of central tendency
when the data set is a traditional example of central tendency that is there are not extreme values
within the data set. The extreme values can adversely affect the mean and either pull the mean

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down or increase the mean providing a potentially unreliable solution. Conversely, median can
be useful in measuring central tendency when there are large extreme values.
Week 3
What are some differences between discrete and continuous data?
The difference is that "continuous" can take any value (for example, between two values there
will always be infinite intermediate values). Discrete can only take certain values. For example,
the natural numbers are discrete (even when there are infinite quantities of values, between any
two values there are a finite quantity. Example: between 1 and 10 you have 9 values). The real
numbers are continuous. No matter how close are two values, you will find infinite values in the
middle (between 1 and 2 you have 1.1, 1.01, 1.001, etc).
But the measured data is always discrete because the precision is never infinite (if your caliper
has a reading of 0.01mm you will not find any "data" between 2.11 and 2.12, for example).
However, if the precision is very good compared with the variation of data, then it is usually
taken as if the data was continuous.
Is the measurement of money continuous or discrete? Explain.
Discrete.
The smallest measurement of money is a penny, and you can't divide it up any smaller. You can't
have sqrt(2) pennies. There are "jumps" between the amounts of money that you can have
Can discrete data be analyzed using a normal distribution? Why or why not?
Yes, you'll have to approximate the discrete data and treat it as continuous. The only trouble you
may have is you may not be able to find the probability of an event equal to a single value.
For example, if a die is thrown 100 times, what is the probability that a 6 shows up exactly once?
What are some differences between discrete and continuous data? Can discrete data be
analyzed using a normal distribution? Why or why not?
Discrete Data is represented by numerical data and can be further broken down into two types.
According to Doane, D. P., & Seward, L. E. (2007), “A variable with a countable number of
values that can be represented by an integer is discrete. You can recognize integer data because
their description begins with “number of.” Continuous Data on the other hand is represented by
a numerical variable that can have any value within an interval is continuous. According to
This would include things like physical measurements (e.g.,
length, weight, time, speed) and financial variables (e.g., sales, assets, price/earnings ratios,
inventory turns)”.

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