Great Learning via Great Learning Academy
The Statistical Methods for Decision Making course aims to give you the knowledge to understand sampling, normal distributions, hypothesis testing, and its different types, type 1 and type 2 errors, chi-square testing, and ANOVA. The course will fix the concepts in your mind through demonstrated examples and solved samples for the aforesaid concepts. You will have to take up the quiz/assessment at the end of the course to test your skills and evaluate your gains to secure the certificate.
Upon completing this free, self-paced, intermediate's guide to Statistical Methods for Decision Making, you can embark on your Data Science and Business Analytics career with a professional Post Graduate certificate and learn various concepts with millions of aspirants worldwide!
You will learn and understand edge detection and sharpening in the sampling technique of Computer Vision with example to represent an image in this section.
This section explains the central theorem and then later looks at how samples and data are distributed. We shall also look at how supervised learning classifies the data later in this section, after which we will understand future predictions for EV sales.
We shall understand how to assume characteristics of a population and later understand Null and Alternative hypotheses concepts in this section.
This section discusses errors rejecting and not rejecting null hypotheses at the right instances. We shall look into the causes of why the actions are not performed right and also understand how to bring them back in the flow.
We shall understand single/dual/multiple samples, one/two-tailed, and mean-variance, or proportion tests in this section. This section picks up multiple examples stating null and alternative hypotheses to understand the concept better.
At the beginning of this section, we shall understand what confidence intervals are and then continue with learning to represent “reject null hypothesis”, “fail to reject the null hypothesis”, and tailing depending upon the alternate hypothesis.
We shall understand working with hypothesis testing by solving various examples, analyzing and representing them in this section.
This section begins with defining what the Chi-Square test is and then continues with briefing when it is used. We shall also understand the properties associated with the Chi-Square test later in this section.
Analysis of Variance explains how not the mean of all the population is equal. This section shall help us discover the mean that is unequal amongst the given population and work with that data to derive desired inferences.
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Great Learning is in Tier 4: commercial training companies and platform-made courses of our institution ranking (58/100).
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