Statistical Treatment Of Data Thesis Formula

Statistical Treatment Of Data Thesis Formula-73
In this example, you have 100 people saying they attended one session, 50 people for four sessions, 100 people for five sessions, etc. In the table above, we would locate the number of sessions where 500 people were to the left of the number and 500 to the right. This can help you eliminate the influence of outliers, which may adversely affect your data. When it comes to reporting on survey results, think about the story the data tells. (Maybe the conference was held in Chicago in January and it was too cold for anyone to go outside!So, you multiply all of these pairs together, sum them up, and divide by the total number of people. ) That is part of the story right there — great conference overall, lousy choice of locations.Miami or San Diego might be a better choice for a winter conference.

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At least when it comes to gender, you’re feeling pretty good if men make up 15% of survey respondents in this example.

If your survey sample is a random selection from a known population, statistical significance can be calculated in a straightforward manner. Suppose 50 of the 1,000 people who attended your conference replied to the survey.

The average reported here is the mean, the kind of average that’s probably most familiar to you. 260 survey participants attended six sessions, more than attended any other number of sessions.

To determine the mean you add up the data and divide that by the number of figures you added. Means–and other types of averages–can also be used if your results were based on Likert scales. The data show that attendees gave very high ratings to almost all the aspects of your conference — the sessions and classes, the social events, and the hotel — but they really disliked the city chosen for the conference.

Using a filter is another useful tool for modeling data.

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Filtering means narrowing your focus to one particular subgroup, and filtering out the others.

It’s important to pay attention to the quality of your data and to understand the components of statistical significance.

In everyday conversation, the word “significant” means important or meaningful.

Recall that when you set a goal for your survey and developed your analysis plan, you thought about what subgroups you were going to analyze and compare. For example, say you wanted to see how teachers, students, and administrators compared to one another in answering the question about next year’s conference.

To figure this out, you want to delve into response rates by means of cross tabulation, where you show the results of the conference question by subgroup: From this table you see that a large majority of the students (86%) and teachers (80%) plan to come back next year.


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