Hypothesis validation is really a methodical use of statistics to establish the probability of claims or ideas that are true or not. We select sample data to study about its behavior in a set of given population data is called hypothesis testing. Further, we establish the criteria for hypothesis decision by setting 5% level of significance to reject or accept the null hypothesis. The criteria are "low barometric pressure and temperature causing breathing problems" meaning the probability of having a sample mean with in the bell curve that is beyond 1.96 standard deviation from the population mean is less than a 5%. We also determine probability 'p ', which is obtaining a sample mean as claimed value in the null hypothesis testing. We*…show more content…*

As we can see from the scattered plot, there are patterns of all the 3 parameters happened in only very few instances and this correlation has no unsuspected correlations. It does not look like there is a strong correlation. An intelligent correlation analysis can lead to a greater understanding of data, but we do not have much correlation. Null Hypothesis

In this paper, we validate the hypothesis with the help of data gathered and the statistical calculation to support or invalidate the premise statement. The stated null hypothesis is that humans cannot hold the breath not more than 25 seconds counted in severe weather condition, and the alternate hypothesis is that they can hold the breath more than 25 seconds count. The mean is what we collected for three people over 20 days and referred as a sample mean. For our hypothesis test, we will use 5% significance level. Our source data confirm standard normal distribution, so we calculate z score and find out how much standard error deviated from the mean. We use z-table to find out since the sample from the population is distributed as a bell curve (Sharpe, Veaux and Velleman 2015).

First step is data collection, we have breath data collected, the ¯X = 25 and the total number of samples are 60.

Now we make assumptions that the population distribution follows bell curve and the center of the curve is zero mean or

As we can see from the scattered plot, there are patterns of all the 3 parameters happened in only very few instances and this correlation has no unsuspected correlations. It does not look like there is a strong correlation. An intelligent correlation analysis can lead to a greater understanding of data, but we do not have much correlation. Null Hypothesis

In this paper, we validate the hypothesis with the help of data gathered and the statistical calculation to support or invalidate the premise statement. The stated null hypothesis is that humans cannot hold the breath not more than 25 seconds counted in severe weather condition, and the alternate hypothesis is that they can hold the breath more than 25 seconds count. The mean is what we collected for three people over 20 days and referred as a sample mean. For our hypothesis test, we will use 5% significance level. Our source data confirm standard normal distribution, so we calculate z score and find out how much standard error deviated from the mean. We use z-table to find out since the sample from the population is distributed as a bell curve (Sharpe, Veaux and Velleman 2015).

First step is data collection, we have breath data collected, the ¯X = 25 and the total number of samples are 60.

Now we make assumptions that the population distribution follows bell curve and the center of the curve is zero mean or

Related

- Better Essays
## Big five Personality Traits

- 7561 Words
- 31 Pages

------------------------------------------------- Big Five personality traits From Wikipedia, the free

- 7561 Words
- 31 Pages

Better Essays - Better Essays
## Operational Risk Management

- 50825 Words
- 204 Pages

≈√ F M A G u i d e l i n e s on Operational Risk Management These guidelines were prepared

- 50825 Words
- 204 Pages

Better Essays