Ajay Devgan says, “By getting into distribution and production, I have actually widened my base” (Distribution Quotes, 2016). Distributions are all around us in the fields of programming, medical, the sciences in many of today colleges. For me, distributions occurred in the aspect of programming, childhood and motherhood. As a single mother with a son, distribution appears in many activities, such as college and taking care of my child. As a student in Trine University, statistics class has many great concepts and applications, thus I will discuss distributions and variables.
Different statistics variables such as random, discrete, and expected are helpful in the study of statistics. These different types of distributions use these variables to help explain theorem, proposition, and the proofs. Random variables are the first type of process that one will need to understand. Random variables are values that multiple numbers depending upon a frequency distribution. Second, discrete variables can take on a finite number of values or fixed number of values. And lastly, expected values are values that the overall sum of all numbers and then multiply by the probability. These variables are my guiding points in my journey into statistics distributions. Statistical distributions are separated into two types of distributions: discrete and continuous. Binomial Probability, Hypergeometric, Negative Binomial, and Poisson Probability Distributions are examples of discrete, neither less; Normal, Exponential, Gamma, Weibull, Lognormal, and Beta Distributions examples of continuous. Primarily, Binomial Probability Distribution (BPD) is discussed in chapter 3 section 4 on pages 114-121. BPD is the measurement of successes (S) within the overall set of experiments. For instance, how many milestones have Marshall learns on his own that were successes, and which are failures. Below is a summary of Marshall Skills: • Sitting up Success • Walking Failure • Holding Head Up Success • Picking Items Up Success • Sleep In Crib All Night Success • Crawl Failure By chance, N=6 with a total of 14 possible outcomes. The possible outcomes are SSSSSS, FFSSSS, SFFSSS, SSFFSS, SSSFFS, SSSSFF, FSSSSF, and FFFFFF. Earlier time, counting was the practical purpose, thus I realize that education …show more content…
HD is the exact number of “S” in a sample. Calculations were the earliest use; and I use the same example as above. According to scientists, “the representation H (x; n, M, N) is the proper way to show this distribution. In contrast, NBD is the amount of failures per each random trial, with successes being fixed. Today, measurement, counting, and data are some of the ways they are used. The number of students that a tutor helped to receive either a pass or fail is my example. Nb (x; r, p) represented the Negative Binomial …show more content…
Along my journey, I encountered the following distributions; Normal, Exponential and Gamma. Additional continuous distributions are Weibull, Lognormal, and Beta. Normal Distribution (ND) is the first discovered landmark. ND is the most common distribution with regard to the central limit theorem which defines the averages of the variables from independent distributions. Karl Friedrich Gauss discovered the Gaussian distribution, meanwhile James Dalton Bell discovered the Bell Curve. The Gaussian distribution and the bell curve are known names for the Normal distribution. Speaking of Normal Distribution, measurements were the earliest use, but today, height, weights, telling time, numerical numbers, and the sciences are practical uses. Random weight of pregnant women in the month of January is an excellent example and it can be represented as F (x; u,
Different statistics variables such as random, discrete, and expected are helpful in the study of statistics. These different types of distributions use these variables to help explain theorem, proposition, and the proofs. Random variables are the first type of process that one will need to understand. Random variables are values that multiple numbers depending upon a frequency distribution. Second, discrete variables can take on a finite number of values or fixed number of values. And lastly, expected values are values that the overall sum of all numbers and then multiply by the probability. These variables are my guiding points in my journey into statistics distributions. Statistical distributions are separated into two types of distributions: discrete and continuous. Binomial Probability, Hypergeometric, Negative Binomial, and Poisson Probability Distributions are examples of discrete, neither less; Normal, Exponential, Gamma, Weibull, Lognormal, and Beta Distributions examples of continuous. Primarily, Binomial Probability Distribution (BPD) is discussed in chapter 3 section 4 on pages 114-121. BPD is the measurement of successes (S) within the overall set of experiments. For instance, how many milestones have Marshall learns on his own that were successes, and which are failures. Below is a summary of Marshall Skills: • Sitting up Success • Walking Failure • Holding Head Up Success • Picking Items Up Success • Sleep In Crib All Night Success • Crawl Failure By chance, N=6 with a total of 14 possible outcomes. The possible outcomes are SSSSSS, FFSSSS, SFFSSS, SSFFSS, SSSFFS, SSSSFF, FSSSSF, and FFFFFF. Earlier time, counting was the practical purpose, thus I realize that education …show more content…
HD is the exact number of “S” in a sample. Calculations were the earliest use; and I use the same example as above. According to scientists, “the representation H (x; n, M, N) is the proper way to show this distribution. In contrast, NBD is the amount of failures per each random trial, with successes being fixed. Today, measurement, counting, and data are some of the ways they are used. The number of students that a tutor helped to receive either a pass or fail is my example. Nb (x; r, p) represented the Negative Binomial …show more content…
Along my journey, I encountered the following distributions; Normal, Exponential and Gamma. Additional continuous distributions are Weibull, Lognormal, and Beta. Normal Distribution (ND) is the first discovered landmark. ND is the most common distribution with regard to the central limit theorem which defines the averages of the variables from independent distributions. Karl Friedrich Gauss discovered the Gaussian distribution, meanwhile James Dalton Bell discovered the Bell Curve. The Gaussian distribution and the bell curve are known names for the Normal distribution. Speaking of Normal Distribution, measurements were the earliest use, but today, height, weights, telling time, numerical numbers, and the sciences are practical uses. Random weight of pregnant women in the month of January is an excellent example and it can be represented as F (x; u,