# Hypothesis Test on Data

I recently completed a survey concerning whether or not cell phone usage on airplanes will lead to air rage or not. I have the results from the survey in excel fomat but I am not sure how to complete a one tailed t test for the questions. I am trying to show the surveyed people say that they wii become irritated by prolonged cell phone usage.

The raw data is 268 people were surveyed and the data is in the excel book. I have it listed by numbers and percentages in the book.

I would like to have the summary of the data analysed in order to determine the percentage of how many people will be irritated by cell phone usage. I need some form of statistical analysis so that I can report my findings in my paper.

© BrainMass Inc. brainmass.com June 3, 2020, 7:24 pm ad1c9bdddfhttps://brainmass.com/statistics/hypothesis-testing/hypothesis-test-data-97212

#### Solution Preview

I don't think that a one-sided t-test would be an appropriate test to do. First of all, you have categorical data (yes or no), and secondly, the numerical data you do have are proportions. So, let's look at the statistical methods you can use...

Let's say you want to find out if the majority of people think cell phones are a threat.

The null hypothesis would be that 50% or less of people think that cell phones are a threat, and the alternative hypothesis would be that more than 50% of people think that cell phones are a threat.

A 95% confidence interval is calculated using the following formula:

where p-hat is your percentage (0.65), n is the number of cases (268), and 1.96 is the critical value of the z-distribution.

Your survey found that 65% of people answer yes and 35% answer no to that question.

So, the 95% confidence interval for the true percentage of people in the general population who would ...

#### Solution Summary

The solution states the null and alternative hypotheses that will be tested, chooses the appropriate statistical test, explains the hypothesis test, and states the conclusions that can be drawn. Two different analyses are done, and both analyses reach a similar conclusion.