There is no day in your life when you do not consider using probability and statistics. Choosing between two options, making predictions, learning weather forecast, creating hypotheses are all important aspects of probability. Our daily routines revolve around probability, and statistics is the next significant discipline that governs and determines much of our probable results. Probability and statistics are related yet independent fields.
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While statistics is all about analysis, it uses probability distributions and theories to make any data calculations and present it via graphs, charts, pictographs. The two disciplines are learned together to receive meaningful and relevant output in a business. They can serve many purposes from analyzing huge volumes of data, organizing it to present it in the simplest graphical manner.
Whether you belong to the field of Data Science, Big data Analysis, Business Intelligence, learning statistics and probability can be of great help to improve business performance, handle and exhibit the data available and apply various logical algorithms, functions and methods on that data.
In this tutorial, we will cover a range of topics that are going to refurbish your mathematics, statistics and probability knowledge from school and college times. Further, individuals and statisticians who are willing to enhance The topics include descriptive statistics that will describe varying data through various distributions. You will also get familiar with grouped frequencies, graphical descriptions, probability distributions of discrete and continuous variables, The Normal Distribute (most important of all distributions) and Sampling and Combination of variables.
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Most topics here contain explanations relating to mathematical interest to keep up your attention and concentration towards learning. As we say, science, technology and mathematics are directly proportional to practice and practical implementation, each topic in this learning reference is thoroughly explained using real-time examples, which are easy-to-comprehend and memorize.
After learning through this tutorial, you can also enroll for our standalone Statistics and Probability training course and combo training courses with Data Science. These courses are excellently descriptive and provide deeper insights into significant topics, which can be implemented in real-time projects in your organization.
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Probability and statistics are concerned with events which occur by chance. Examples include occurrence of accidents and various games of chance, such as flipping a coin, or throwing a symmetrical six-sided die. In each case we may have some knowledge of the likelihood of various possible results, but we cannot predict with any certainty the Read More
The purpose of descriptive statistics is to present a mass of data in a more understandable form. We may summarize the data in numbers as (a) some form of average, or in some cases a proportion, (b) some measure of variability or spread, and (c) quantities such as quartiles or percentiles, which divide the data so Read More
Stem-and-Leaf Displays These simple displays are particularly suitable for exploratory analysis of fairly small sets of data. The basic ideas will be developed with an example. Example Data have been obtained on the lives of batteries of a particular type in an industrial application. Table: Shows the lives of 36 batteries recorded to Read More
Probability Functions and Distribution Functions (a) Probability Functions Say the possible values of a discrete random variable, X, are x0, x1, x2, ... xk, and the corresponding probabilities are p(x0), p(x1), p(x2) ... p(xk). Then for any choice of i, where k is the maximum possible value of i. Then p(xi) is Read More
If a variable is continuous, between any two possible values of the variable are an infinite number of other possible values, even though we cannot distinguish some of them from one another in practice. It is therefore not possible to count the number of possible values of a continuous variable. In this situation calculus provides the logical Read More
The normal distribution is the most important of all probability distributions. It is applied directly to many practical problems, and several very useful distributions are based on it. Characteristics Many empirical frequency distributions have the following characteristics: They are approximately symmetrical, and the mode is close to the centre of the distribution. The mean, median, Read More
Sampling A sample is a group of objects or readings taken from a population for counting or measurement. From the observations of the sample, we infer properties of the population. For example, the sample mean, x , is an unbiased estimate of the population mean, μ, and that the sample variance, s2, Read More
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