The empirical rule also says if you go out, two standard deviations from the mean in each direction, then from here to here is 95% of the data, which would then result in 13.5% in this region and 13.5% in this region. Now because the bell is symmetric, then we could split that 68 say 34% is in here and 34% is in here now. If we go one standard deviation out in each direction, then you can count account for 68% of the data being in this range. So the empirical rule stated that as long as your data is bell shaped, we can put our average in the center. Ingram's of M and M Candies, Part C use that empirical rule to determine the percentages of Eminem's with weights between 0.79 and 0.8 point 95 with mean being 0.87 So part C. And remember, our data in this case was weights.
So in answer to Part B, we could say it is relatively bell shaped, so therefore, the empirical rule should be a good approximation of the distribution of data. It's not perfect, but it is kind of bell shaped.
And as you can see, the shape of this graph based on the data is kind of Bell shaped. Now I'm gonna have to really space it out so that you can see the shape of this graph. So if I hit graph, you're going to see the grass. So I went to second, Why equals and I turned on my hissed a gram, and I told it to use the information I have enlist one to construct that hissed a gram. So I have taken it upon myself to create a stat plot of it. So again, I'm going to bring in my graphing calculator, and it has the same exact data and we want to look at what that hissed a gram looked like. So, in part B, um, you need to rely on a previous exercise from two sections ago and it had the same exact data. It says, on the basis of the hissed a gram drawn in section 31 problem 33. Okay, Uh, if you had to do it longhand, there will be a chart with 50 lines and three columns in order to get to this same 30.4 value for part B. Standard deviation would be 20.4 and the sample standard deviation is the S ex, not the Sigma X. So you are going to see that your average would have been 0.87 ish and your standard deviation out to two decimal places.
And I'm gonna scoot over to calculate, and I'm gonna calculate one variable statistics on everything that I have placed in list one in doing. So as you can see, I have all of the data in my calculator and I'm going to hit stat. The faster way is to use your programmable calculator. You would then create a column called X minus X bar squared, and you would add up this column right here and divided by 49 before taking the square root. And in order to find that sample standard deviation, you would do the square root of the some of X minus X bar quantity square divided by n minus one, Which means then you were going to have to first calculate the average of those 50 numbers you were then going to create a column called X minus X bar. So, traditionally, we would put the data in a chart, and I'm only going to put a couple. You're trying to determine the sample standard deviation.
#RANDOM M&M GENERATOR GENERATOR#
Every day for a week, they use random number generator to select 10 M&Ms and weigh them:Īll right, You're given data that represents the weights of M and M Candies, and I have placed all of that data in my list and in part A. 94576 grams_ (Because this problem asks about probability your answer should be between and 1.)ģ months, 2 weeks ago A factory for M&M candy produces 100,000 plain M&Ms every day: The factory's quality control analyst wants to know how much variability there is in the weights of the plain M&Ms produced. If you choose an M&M from the population at random_ estimate the probability that it will weigh more than. Approximately 99 % of M&Ms in the population weigh betweenĭ. Estimate the percentage of M&Ms in the population that weigh between. (Click on a figure t0 see a larger version:)ĭescriptive Statistics: Weight in Grams of M&Msī. The Minitab output below shows the results from this sample. All plain M&Ms the factory has ever produced
All plain M&Ms the factory produced that week C. AIl 50 plain M&Ms the quality control analyst weighed B. To what population would it be most appropriate to generalize the quality control analyst's results?Ī. Every day for a week, they use random number generator to select 10 M&Ms and weigh them:Ī. SOLVED:A factory for M&M candy produces 100,000 plain M&Ms every day: The factory's quality control analyst wants to know how much variability there is in the weights of the plain M&Ms produced.