???????? CONFIDENCE INTERVAL #01
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In statistics, a confidence interval (CI) is a type of interval estimate of an unknown population parameter. When to use a confidence interval? Confidence intervals are used to indicate the reliability of an estimate. For example, a CI can be used to describe how reliable the results of a survey are. All other things being equal, a survey that results in a small CI is more reliable than one that results in a larger CI. How do you calculate a confidence interval? Get the population standard deviation (σ) and the sample size (n). Take the square root of the sample size and divide it by the population standard deviation. ... How to calculate the margin of error. Desired confidence level z-score 90% 1.65 95% 1.96 99% 2.58 How to calculate the confidence interval on the scientific calculator? The confidence interval calculator calculates the confidence interval by taking the standard deviation and dividing it by the square root of the sample size, according to the formula σ x = σ/√n . ------------------------------------------------- tag: Meaning of Confidence Interval, confidence interval for proportion, confidence interval in excel, confidence interval, statistical confidence interval, confidence interval for mean, confidence interval of the mean, confidence interval solved exercises, confidence interval 95, confidence interval for proportion, confidence interval with known variance, confidence interval pdf, confidence interval example, confidence interval solved exercises, confidence interval formula, confidence interval interpretation, how to use confidence interval 1 Descriptive statistics and exploratory data analysis: graphs, diagrams, tables, descriptive measures (position, dispersion, skewness and kurtosis). 2 Probability. 2.1 Basic definitions and axioms. 2.2 Conditional probability and independence. 2.3 Discrete and continuous random variables. 2.4 Probability distribution. 2.5 Probability function. 2.6 Probability density function. 2.7 Expectation and moments. 2.8 Special distributions. 2.9 Conditional distributions and independence. 2.10 Transformation of variables. 2.11 Laws of large numbers. 2.12 Central limit theorem. 2.13 Random samples. 2.14 Sampling distributions. 3 Statistical inference. 3.1 Point estimation: estimation methods, properties of estimators, sufficiency. 3.2 Interval estimation: confidence intervals, credibility intervals. 3.3 Hypothesis testing: simple and compound hypotheses, significance levels and power of a test, Student's t-test, chi-square test. 4 Linear regression analysis. 4.1 Least squares and maximum likelihood criteria. 4.2 Linear regression models. 4.3 Inference on model parameters. 4.4 Analysis of variance. 4.5 Residual analysis. 5 Sampling techniques: simple random, stratified, systematic and cluster sampling. 5.1 Sample size.
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