Part II: Data Analysis- DRIVING AND GENDERS. SEE EXAMPLES BELOW BUT USE GENDER AND DRIVING TOPICYou will use a test of Chi-Square to do the data analysis. Please download the attached document for an explanation of how to do a test of Chi-Square. The example is based on 3 categories and has a total of 60 subjects.For your data analysis, which is based on 50 cars, the expected frequencies should be 25 men and 25 women drivers. The observed data are what you observed; the number of observations should total 50. You can reject the null hypothesis at p < .05 if the value you calculate is greater than 3.84; at p < .025 if the value is greater than 5.02, and at p < .01 if the value is greater than 6.63. If the Chi-Square value you calculate is less than 3.84, you will not have significant results because the number of male and female drivers you observed will not differ significantly from an expected 50%/50% split. A Chi Square Distribution Table is attached as a separate document. In other words, you need to open both attachments.The write up should consist of the methods, results, and discussion. In your results section, please include your Chi Square calculation. Don’t forget to include the number of categories (C-1) and the number of participants when you report the results. When you write the discussion section, make sure your wording is tentative. Most of you will find that more men drive. You can suggest reasons for this but that’s as far as you can take it. You are observing a behavior but you don’t know what underlies it. Don’t jump to the conclusion that men are drivings because they’re control freaks or they think women are lousy drivers. It could be that all the women in the sample you observe have broken right ankles and can’t drive but they would if they could. If you suggest a reason for the behavior you observe, make sure you use tentative language (e.g. it may be the case that, it could be, it is possible that, etc.). For This or a Similar Paper Click Here To Order Now
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