This document discusses statistical inference, which involves drawing conclusions about an unknown population based on a sample. There are two main types of statistical inference: parameter estimation and hypothesis testing. Parameter estimation involves obtaining numerical values of population parameters from a sample, like estimating the percentage of people aware of a product. Hypothesis testing involves making judgments about assumptions regarding population parameters based on sample data. The document also discusses point estimation, interval estimation, standard error, and provides examples of calculating confidence intervals.
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
Statistical inference: Hypothesis Testing and t-testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Statistical inference: Probability and DistributionEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 1 (probability) and week 2 (distribution).
Meaning is not delivered to us on silver platters. We must be equipped as readers, writers, viewer, listeners, investigators to excavate it...dig it out. The cornerstone tool in our metacognitive arsenal is the ability to make inferences.
linearity concept of significance, standard deviation, chi square test, stude...KavyasriPuttamreddy
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Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn.
Make use of the PPT to have a better understanding of Inferential statistics.
2. The main objective of sampling is to draw conclusions about
the
unknown population from the information provided by a
sample.
This is called statistical inference.
Statistical inference may be of two kinds: parameter estimation
and Hypothesis testing.
3. PARAMETER ESTIMATION
Parameter estimation is concerned with obtaining numerical
values of the parameter from a sample.
Example, a company may be interested in estimating the
share of the population who are aware of its product.
4. HYPOTHESIS TESTING
On the other hand, hypothesis is concerned with passing a
judgment on some assumption which we make( on the basis of
some theory or information) about a true value of a population
parameter.
5. COMPARISON BETWEEN ESTIMATION AND
HYPOTHESIS TESTING
• Utilises the information of a sample .
• In parameter estimation we use some formula in which we
substitute the observations of a sample in order to obtain
numerical estimate of the population parameter.
• In hypothesis testing we begin with some assumption about
the true value of the population parameter.
• Then we calculate certain test statistic and draw conclusion.
6. POINT ESTIMATION AND INTERVAL ESTIMATION.
An estimate of the population parameter given
by
a single number is called is called a point
estimate of the parameter.
7. EX.
A firm wish to estimate amount of time its
salesman spend on each sales call.
8. INTERVAL ESTIMATION
An estimate of a population parameter given by
two numbers between which the parameter
may
be considered to lie. The interval estimation
consists of lower and upper limits and we
assign
a probability (say 95% confidence) that this
interval contains the true value of the
parameter.
10. STANDARD ERROR
Standard deviation of sample statistic is called
standard error.
Infinite Population
(i) Standard error of mean when population s.d (σ) is known.
S.E. = σ
√ n
(i) Standard error of mean when population s.d (σ) is not known.
S.E. = s
√ n
12. EX 1
From a random sample of 36 New Delhi civil
service personnel, the mean age and the
sample
standard deviation were found to be 40 years
and 4.5 years respectively. Construct a 95 per
cent confidence interval for the mean age of
civil
servants in New delhi.
40 ±1.47 years.
13. EX2
The quality department of a wire manufacturing
company periodically selects a sample of wire
specimens in order to test for breaking strength.
Past experience has shown that the breaking
strength of a certain type of wire are normally
distributed with standard deviation of 200 kg. A
random sample of 64 specimens gave a mean of
6,200 kg. The quality control supervisor wanted a
95
percent confidence interval for the mean breaking
Strength of the population.6151 and 6249.
14. EX 3
A manager wants an estimate of average sales of salesman
in his company. A random sample of 100 out of 500
salesmen is selected and average sales is found to be
Rs. 750( thousand). Given population standard deviation is
Rs. 150 (thousand) , manager specifies a 98% confidence
interval. What is the interval estimate for average sales of
salesman?
718720 to 781280.