Number 4 I need help please Simulation Example: Denise has a bad habit of sleeping through her alarm in the mornings. She oversleepsrd before the deadline.and is late for her 8am class 10% of the time.1. We want to simulate 15 days of 8 am classes for Denise. Which of the following digit assignments wouldwork for this simulation? (only one correct answer)a),0 to 8 = on time 9= late to class00 to 49 on time, 50 to 99 = late to class0 to 4 = on time, 5 to 9 = late to class00 to 90 = on time, 91 to 99 = late to class%3D%3D%3DUse the random digit assignment from the previous question, start at line 104 from the random number tableA (attached to the back) to simulate the 15 class days.Line i04 5271l givesLine io s 9 5592° give.qcaskLine 10c4 68417 gives eDay1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th15thRandomNumberWas she ontime orlate?Late3. What percent of days was Denise late in this simulation?Days Cate Sor ClassTotal # DaySl6i667%4. How does this compare to the 10% probability we were given at the beginning? If there is a difference, whyare they be different?Latel95592Sでの%h9 UOontimesa634lon fine 73199

Since we have used the random digit assignment for this simulation activity According to the solution given in the part 3 of Question, percent of days Denise late in this simulation = 115 = 6.67% But 10% probability of getting late is given in the beginning of question. There is a difference (error) of 3.33% because this simulation is repeated for 15 days (n-sample size) , as the number of days of simulations increases, percent of days Denise late in this simulation will get closer to 10% As we know that : SE = Standard Error, σ = variance and n = number of days in simulation or sample size The size (n) of a statistical sample affects the standard error for that sample. Because n is in the denominator of the standard error formula, the standard error decreases as n increases. It makes sense that having more data gives less variation (and more precision) in your results. Therefore, as sample size increases, the standard error decreases Standard error increases when standard deviation, i.e. the variance of the population, increases. Standard error decreases when sample size increases – as the sample size gets closer to the true size of the population, the sample means cluster more and more around the true population mean.