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Lesson 7 of 15

Parameter Estimation

In statistics, statistical inference refers to drawing conclusion based on the data. Statistical inferences are drawn in two broad ways, namely, hypothesis testing, and parameter estimation.

In hypothesis testing, we make a hypothesis and then we determine whether the sample data supports the hypothesis or does not support it. The hypothesis could be something like - Population mean is equal to 10. Then based on our sample data, we either accept or reject the hypothesis.

In contrast with hypothesis testing, under parameter estimation we try to estimate the population parameter by making use of the information available in the sample.

There are two types of parameter estimators: point estimates and confidence interval estimates.

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Point Estimates

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Sampling and Estimation

15 lessons

Lessons

1
Simple Random Sampling and Sampling Distribution
2
Sampling Error
3
Stratified Random Sampling
4
Time Series and Cross Sectional Data
5
Central Limit Theorem
6
Standard Error of the Sample Mean
7
Parameter Estimation
8
Point Estimates
9
Confidence Interval Estimates
10
Confidence Interval for a Population mean, with a known Population Variance
11
Confidence Interval for a Population mean, with an Unknown Population Variance
12
Confidence Interval for a Population Mean, when the Distribution is Non-normal
13
Student’s t Distribution
14
How to Read Student’s t Table
15
Biases in Sampling

Quizzes

Sampling and Estimation
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