Question

This is a graded discussion: 25 points possible due Nov 17 Discussion Five Math 2 Discussion Five: (17 points) Give me a real-life example of a data distribution that you think is approximately normal, but with a large spread (ie large standard deviation). Use Google or other research methods to find the mean and standard deviation to support your claim. What reasons or conditions attribute to the large variation? (Here's my example: income-some people make minimum wage, some people make $134M over 4 years. Think of another example and do some research to find the standard deviation, and answer the question about reasons/conditions.) a data distribution that you think is approximately normal, but with a very small spread (ie, small standard deviation). Use Google or other research methods to find the mean and standard deviation to support your daim What limitations prevent the data from a larger variation? (Here's my example: body temperature - too high or too low and you are dead. Think of other examples and answer the question on limitation.) (Note: You must post before seeing other posts.) (8 points) Respond thoughtfully and substantively to TWO classmates post. What do you think about their daims - are they valid or invalid, what other reasoning can you think of that cause the variations of their claims? Search entries or author Unread Subscribed & Ranh
it has to include the mean and standard deviation

Answer 1

Normal distribution with small variance -

Birth weight of new born babies -

Frequency 100 N = 3,226 Mean = 3.39kg SD = 0.55 kg 0.00 1.00 2.00 5.00 6.00 7.00 3.00 4.00 Birthweight (Kilograms)

A report suggests that the average weight of the newly born babies is 3.55 and standard deviation from the mean is 0.55

If you look at the graph, the spread is small and peaked, that is when the data is less varied or more clustered around the mean.

Normal distribution with large variance -

Nifty daily returns -

You might always have heard of the phrase - "stock market is volatile". here by volatility we mean the expectation can go far either direction. The variance of the returns are high, standard deviation from the mean returns is high. Lets look at a graph to find out -

-4.17% -3.85% -3.54% -3.22% -2.90% -2.59% -2.27% -1.95% -1.64% -1.32% -1.01% -0.69% -0.37% -0.06% 0.26% 0.58% 0.89% 1.21% 1.52% 1.84% 2.16% 2.47% 2.79% 3.11% 3.42% 3.74% ) Nifty Daily Returns

We have return values till -4.17% and 3.74%. That shows how volatile or varied the daily returns are.

Here, mean = 0.04%

standard deviation = 1.046%