Learn Statistics Estimation Theory
5.0 (89 ratings)
719 students enrolled
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# Learn Statistics Estimation Theory

5.0 (89 ratings)
719 students enrolled
Last updated 8/2017
English
Current price: \$10 Original price: \$200 Discount: 95% off
30-Day Money-Back Guarantee
Includes:
• 1 hour on-demand video
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Learn concepts on Unbiased Estimates and Efficient Estimates
• Master Point Estimates and Interval Estimates, Reliability
• Get Practice on Confidence Intervals
• Maximum Likelihood Estimates
View Curriculum
Requirements
• Student should have basic math knowledge
• Students who are not from a mathematical background can also take this course, but they just might have to do some prework to easily understand the lectures
Description

Welcome to the course on Statistics Estimation theory. This course includes topics on Unbiased Estimates and Efficient Estimates, Point Estimates and Interval Estimates, Reliability, Confidence Interval Estimates of Population Parameters, Confidence Intervals for Means, Confidence Intervals for Proportions, Confidence Intervals for Differences and Sums, Confidence Intervals for the Variance of a Normal Distribution, Confidence Intervals for Variance Ratios and Maximum Likelihood Estimates.

Usually, estimates serve to compress information. Their job is to extract from a large set of data the pertinent pieces of information required to make a good decision. For example, the receiving circuitry of a radar gathers a very large amount of information about what objects are around it, but in a form which is too difficult for humans to process manually. The familiar graphical display produced by a radar results from processing the received signal and extracting out the features we are interested in. Even in estimating the height of a tree, this is true. The full information is the complete sequence of images our eyes see as we look up at the tree; we compress this information into a single number (we hope is) related to the height of the tree.

Initially then, there is no role for estimation theory. We have data (also commonly referred to as observations) and we wish to make an informed decision. A standard and widely applicable framework for making decisions is to determine first how to measure the goodness of a decision and then endeavour to construct a decision rule (which takes as input the available data and outputs the recommended decision to make) which can be shown, in a probabilistic framework, to make good decisions the majority of the time. A key point is that theoretically, we should use all the data available to us if we wish to make the best decision possible.  (Old habits die hard.  It is tempting to reason thus: If I knew what the temperature will be tomorrow then I know what clothes to pack, therefore, I will base my decision on my “best guess” of tomorrow’s temperature. This is not only sub-optimal, it is also ill-posed because the only way to define what a “best guess” is, is by starting with the decision problem and working backwards.)

Let's begin with the Statistics Estimation Course. Have a good learning experience!

Who is the target audience?
• Anyone who is interested in learning statistics extensively starting from basics.
• Anyone who wants build their career as a Statistician or a Data Scientist
• Anyone who wants improve their Statistic and Math knowledge so that it would be useful in whatever field they work in.
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Curriculum For This Course
10 Lectures
57:18
+
Introduction
1 Lecture 02:57
Preview 02:57
+
Statistics Estimation Theory
9 Lectures 54:21
Preview 06:13

Preview 03:14

Confidence Interval Estimates of Population Parameters
08:44

Confidence Intervals for Means
10:29

Confidence Intervals for Proportions
04:32

Confidence Intervals for Differences and Sums
05:55

Confidence Intervals for the Variance of a Normal Distribution
04:33

Confidence Intervals for Variance Ratios
05:56

Maximum Likelihood Estimates
04:45
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