Practice
Resources
Contests
Online IDE
New
Free Mock
Events New Scaler
Practice
Improve your coding skills with our resources
Contests
Compete in popular contests with top coders
logo
Events
Attend free live masterclass hosted by top tech professionals
New
Scaler
Explore Offerings by SCALER

Inferential Statistics

Go to Problems

Estimation and Sampling

Estimation and Sampling

In general, estimation is the best use of information in a sample to form one of several types of estimates in the parameter’s value. These estimates help us infer what would likely be found in the population.

Broadly speaking, estimates can be categorized into 2 types:

  • Point Estimation- A single value that estimates the parameters of the population.
  • Interval Estimation - Also called confidence interval, a range of values that bracket the unknown population parameter with some specified level of probability.

 

Point Estimators

The formulas that we use to compute the sample statistics (such as the mean) are examples of point estimators. The three desirable properties of a point estimator are:

  • Unbiasedness
  • Efficiency
  • Consistency

 

Interval Estimators

A range of values, within which the actual parameter will lie with a given probability.

Confidence Interval = point estimate ± (reliability factor x standard error)

Where, 

Reliability Factor is a number based on the assumed distribution of the point estimate and the degree of confidence of the confidence interval.

Standard error of the sample statistic providing the point estimate.

 

Sampling, in general, refers to the selection of a sample from the population and data to make inferences about what would be found in the population.

We can use sample values, such as sample mean, sample standard deviation, and so on, as valid estimators of the population.

Sampling (statistics) - Wikipedia

Fig: Sampling from a population

(Source: Wikipedia)

 

Serious about Learning Data Science and Machine Learning ?

Learn this and a lot more with Scaler's Data Science industry vetted curriculum.
Hypothesis testing
Central limit theorem
Distribution analysis: multivariate
Problem Score Companies Time Status
Correlation-analysis 30
2:00
Normal random variable 30
1:24
When multivariate analysis 30
3:07
Multivariate 30
1:34
Dependent variables 30
2:21
Estimation and sampling
Problem Score Companies Time Status
Number of random samples 30
2:29
Team Selection 30
0:58
Free Mock Assessment
Fill up the details for personalised experience.
All fields are mandatory
Current Employer *
Enter company name *
Graduation Year *
Select an option *
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
Phone Number *
OTP will be sent to this number for verification
+1 *
+1
Change Number
Phone Number *
OTP will be sent to this number for verification
+1 *
+1
Change Number
Graduation Year *
Graduation Year *
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
*Enter the expected year of graduation if you're student
Current Employer *
Company Name *
Please verify your phone number
Edit
Resend OTP
By clicking on Start Test, I agree to be contacted by Scaler in the future.
Already have an account? Log in
Free Mock Assessment
Instructions from Interviewbit
Start Test