1. Screenshot: parent population (include mean and standard deviation figures on the left) mean= 15.17 Parent population (can be changed with the mouse) median= 15.00 sd= 9.17 skew= 0.15 kurtosis= -1.00 1 132 2. Screenshot 10,000+ repetitions (include mean and standard deviation figures on the left) Reps= 10102 Distribution of Means, N=2 mean= 15.28 768 - median= 15.00 640 - sd= 6.52 $12 - skew= 0.13 384 - kurtosis= -0.48 256 - 128 Reps= 10102 Distribution of Means, N=25 mean= 15.16 2100 - median= 15.00 1750 - sd= 1.86 1400 - skew= 0.05 1050 kurtosis= 0.12 700 350 TT 1 32Screenshot 100,000+ repetitions [include mean and standard deviation gures on the left) Reps= mean: median mean= median Sampling Distribution Parameter 110102 15.17 15.00 6149 0.12 0.48 110102 15.16 15.00 184 0.05 0.10 Distribution of Me Distribution of Me ans, N=2 IlI ans, N=25 z 110000 - Trials Sample Size observed predicted difference observed predicted difference N=2 15.28 15.28 0 6.52 6.48 0.04 10,000 N=25 15.16 15.16 0 1.86 1.83 0.03 N=2 15.17 15.17 0 6.49 6.48 110,000 0.01 N=25 15.16 15.16 0 1.84 1.83 0.01 1. The Central Limit Theorem predicts that, particularly, for larger sample sizes, the probability distribution of the sample means will approximate a normal distribution no matter the shape of the underlying distribution. Does this data bear this out? Explain why or why not.#| [Type Answer Here] 2. Statistics also predicts that, as the number of trials increases, the mean of the sampling distribution of sample means approaches the mean of the original population. Does this data bear this out? Explain why or why not. [Type Answer Here] 3. Statistics also predicts that, as the number of trials increases, the standard deviation of the sampling distribution of sample means approaches the square root of the original population divided by the square root of the sample size. Does this data bear this out? Explain why or why not. [Type Answer, Here]