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Sampling distributions, point estimate & confidence interval

person icon Hemakumar Kasala

4.4

Sampling distributions, point estimate & confidence interval

Essential statistics for Data Science

updated on icon Updated on Jun, 2024

language icon Language - English

person icon Hemakumar Kasala

category icon Data Science,Machine Learning,Probability & Statistics

Lectures -6

Duration -1.5 hours

4.4

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Course Description

Statistical studies are often about understanding, estimating various population characteristics.

Since it is often not feasible to undertake the study on the entire population the statistical study is often carried out on a representative sample drawn out of the population

Therefore the said sample has to meet certain expectations in order to be a fairly good representative of the population.

This course starts with the topic that explain the factors to be understood and implemented as part of the sampling process

From the sample a sample statistic representing a population characteristic of interest is derived. Population characteristic of interest is often one among the following two {Population proportion of success, Population mean}

A sample statistic is a single sample point estimate of the population characteristic. Therefore the sample statistic exhibits a probability distribution as sampling and sample statistic are derived repeatedly over large number of cycles. This course explains the characteristics of such a sample statistic distribution (central limiting theorem) for both kinds of sample statistics namely { Sample proportion of success, Sample mean}. Explains the mean and standard deviations for these distributions and how they are related to population characteristics and sampling sizes.

The course further explains how to use the sample statistic (single sample Point estimate) to get an idea of the population characteristic using an additional estimate called as the confidence interval. Point estimate and confidence interval together identify the interval that captures the true unknown population characteristic.

The course also explains how to apply the Z standard normal statistic , t-statistic and their respective Z/T statistic tables in the above process to arrive at the estimates for the population characteristics.

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Goals

sampling methods, Point estimates and confidence intervals form the basis in various statistical studies and  machine learning algorithms.

Hence it is important to understand these concepts thoroughly so that one has a better understanding on the results of the above studies and algorithms in order to make right decisions and choices during the process.

This course provides you with in depth understanding on sampling methods, sample statistic distributions, point estimates and confidence intervals everything required to understand and implement advance methods discussed above.


Prerequisites

Basic knowledge on Probability, random variables and probability distributions would help understand this course better.

You can follow my course on Random variables and Probability distributions to have the above mentioned understanding

Sampling distributions, point estimate & confidence interval

Curriculum

Check out the detailed breakdown of what’s inside the course

Course overview
1 Lectures
  • play icon Course Overview 04:19 04:19
Sampling: Bias and sampling methods
1 Lectures
Tutorialspoint
Sample mean distribution & central limiting theorem
1 Lectures
Tutorialspoint
Sampling distribution of sample proportion
1 Lectures
Tutorialspoint
Point estimate & Confidence interval - Part I
1 Lectures
Tutorialspoint
Point estimate & Confidence interval - Part II
1 Lectures
Tutorialspoint

Instructor Details

Hemakumar Kasala

Hemakumar Kasala

Hi I am Hemakumar Kasala. 

A passionate instructor who chose to train people in the area of Statistics, Data Science and Machine learning

A post graduate in Electrical Engg., from IIT Kharagpur India, 

I have over 30 years of corporate experience working with multinationals like Robert Bosch, Philips and TPVision (subsidiary of TPV)

One of my primary responsibilities during my tenure with Philips/TPVision is to train people across sites worldwide on various technologies that we ventured into.

Currently I am on my own and my activities involve offering training courses on Statistics, Data science and machine learning.

Would love to engage with you and part my knowledge to the best of my understanding on the above subjects. 

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