A random sample of n, - 16 communities in westem Kansas gave the folloning information for people under 2s years of age. *: Rate of hay fever per 1000 population for people under 25 100 90 121 129 94 123 112 93 125 95 125 117 97 122 127 88 A random sample of n, - 14 regions in western Kansas gave the following information for people over 50 years old. Rate of hay fever per 1000 population for people over 50 96 108 99 97 113 8 11o 79 115 100 89 114 85 96 ) Use a calculator ta calculate x. 5. and s (Round your answers to two decimal places.) -2143 (H) Assume that the hay fever rate in each age group has an approximately normal distribution. Da the data indicate that the age group over 50 has a lower rate of hay fever? Use e- 0.05. (a) What is the level of significance? laos State the null and alternate hypotheses. O H H a D) What sampling distribution will you use? What assumptions are you making? • The Student's t. We assume that batn population distributions are approximately normal with unknown standard deviations. O The standard normal. We assume that beth population distributions are approximately narmal with known standard deviations. O The Student's t. We assume that bath population distributions are approximately normal with known standard deviations. O The standard normal. We assume that beth population distributions are approximately narmal with unknown standard deviations What is the value of the sample test statistic? (Test the diference - Round your answer to three decimal places.)

Q: WOW THANKS! Can you help with question 18 too? I realized I already answered 20 after i sent it

A: Here is the solution:

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A: We find linear function using table and then find f(0).

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A: Given a 1211220021112347451112 b 1202231110240000

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A: The answer is correct as per my knowledge. Please post if you have any specific doubts

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Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.