HW6_skeleton

7.2

• 1. Standard normal distribution
• 2. a
• 3. .6915
• 4. .4207
• 1. .0071

  2. .3336

  3. .9115

  4. .9998

• 1. .9987

  2. .9441

  3. .0375

  4. .0009

• 15. 0.68
• 17. -2.57, 2.57
• 18. -1.88, 1.88
• 23. .9838

shadenorm(mu=50, sig=7, above = 35,col = "blue",dens = 150)

• 24. .0162

shadenorm(mu=50, sig=7, above = 65,col = "blue",dens = 150)

• 25. .2420

shadenorm(mu=50, sig=7, below = 45,col = "blue",dens = 150)

• 1. .8658

  2. .0132

  3. .6985

  4. .1230

  5. 96th percentile

  6. 4th percentile if you round up

8.1

• 1. Sampling distribution
• 2. The sample has the same mean as the population and the standard deviation is the standard deviation of population/(squareroot of the number of sample)
• 3. Standard error of the mean
• 4. True
• 5. False
• 6. False
• 7. Normal, 30, 2.529
• 8. No, because the sample size is bigger that 30 and the central limit theorem states that the distribution is normal as samples are independent, identical distributed. The distribution of Xbar is: Normal, 50, 0.632.
• 9. Normal, 80, 2
• 10. Normal, 64, 3
• 1. Normal, 80, 2

  2. 0.0668

  3. 0.0179

  4. 0.7969

• 1. .3557

  2. Normal, 266, 3.577

  3. .0465

  4. .004

  5. One thing you could conclude could be that the sample maybe was not truly random, but there has been a bias. It could also be that the numbers we are given for the population is not the numbers that represent the real population and our sample therefor shows a different mean.

  6. .9844