Skip to main content

Posts

Showing posts from September, 2019

EXPERIMENT CONCERNING BERNOULLI-LIKE PROCESS

The Bernoulli process must possess the following properties: 1. The experiment consists of n repeated trials.  [Each trial is called a Bernoulli trial.] 2. Each trial results in an outcome that may be classified as a success or a failure. 3. The probability of success, denoted by p, remains constant from trial to trial. 4. The repeated trials are independent. Now we are going to do an experiment approximating to a Bernoulli process, i.e. Bernoulli-like process. (If you have a deep understanding about the Bernoulli process, you know why the experiment is not an exact Bernoulli process, but it is only an approximation to the process.) We are going to do 20 Bernoulli-like process. In each process, there are 4 Bernoulli trials (n = 4). Ideally, the same single dice is rolled 4 times by the same person. But for the sake of time efficiency, instead of rolling a dice 4 times in each process, each of the 4 students in a group roll a dice simultaneously. Let’s define that a succe...

CONSTRUCTING THE FREQUENCY DISTRIBUTION TABLE

The frequency distribution table is a table that divides data into groups (classes) and shows how many data values occur in each group/class. Below is an example of frequency distribution table. Now we are learning how to create a frequency distribution table. Suppose we have a collection of ungrouped data on last year’s advertising expenditures of 40 logistics companies, recorded in millions Rupiahs. To construct a frequency distribution table of the ungrouped data, apply the following steps. Step 1: Find the range of the data The range (R) is defined as the difference between the largest data and the smallest data. In this case, R = 307 - 242 = 65. Step 2: Determine the number of categories/classes (k) Applying Sturges rule (k = 1 + 3,322 log n, where n = the number of data), we have: $k = 1 + 3.322 \: log \: 40 \approx 6.32$ As the value of k must be a natural number, 6.32 is rounded up to 7, so k = 7. Step 3: Determine the class width (c) To find c, use $...

THE QUARTILES AND MEDIAN OF GROUPED DATA

In this post,  we will learn how to determine the quartiles when some quantitative data are presented in a frequency distribution table. For example, we have the following data, showing Flesch Readability Score of 80 monthly bulletin articles published by Britt and Co. Ltd. Find the quartiles of these readability scores. To answer this, first augment the table with a new column to the right of the frequency column, namely Data Numbers column. There are 5 data in the first class, so the class contains data no. 1 to  no. 5. There are 7 data in the second class, so the class contains data no. 6 to no. 12. There are 13 data in the third class, so the class contains data no. 13 to no. 25. Continuing this way, we get the following: In this case, finding the first quartile means finding the  20 th  data, after the data have been ordered from the smallest to the highest (20 = ¼ x 80). Note that the  20 th  data is in the third class (20 is ...