Some Important Notes for Confidence Intervals
Confidence interval refers to the amount of uncertainty associated with a sample estimate of the population parameter. A 90% confidence level indicates that 90% of the interval estimates will include the population parameter. A 95% confidence level means that 95% of the intervals would include the parameter and these are the most commonly used levels.
Typically statistical analysis is performed on sample data due to the impracticalities associated with working on the entire population. Here the mean calculated based on the sample data would be assumed to be the mean of the population. Confidence interval provides a range of values in such cases which are likely to contain the population parameter of interest and answers the query as to how well the sample statistic estimates the underlying population value. It addresses the precision aspects.
Further, confidence interval problems require the following information. We need to understand the confidence level, statistic and margin of error. Once the above are in place we calculate the range of the confidence interval as defined by the sample statistic plus or minus the margin of error. The precision of the confidence level or in other words the uncertainty associated with it is specified by the confidence level. In most cases the margin of error is not given and it requires to be calculated.
What Are Difficulties Encountered In Confidence Intervals Problems?
Now let us look at the step by step calculation of confidence interval, it will help our learners understand the learning outcome from our courses. First step would be to identify the sample statistic, whether we are using sample mean or sample proportion to estimate the population parameter. Second and more importantly
As we noted in the previous section, the confidence level describes the uncertainty of a sampling method. As such any percentage can be used but the most widely used confidence levels are 90 percent, 95 percent and 99 percent.
Another crucial step it to compute the margin of error. Margin of error is usually given to students who are pursuing academics but in real life scenarios analysts will have to calculate them more often than not.
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We cover a broad range of topics including the basic concepts of Confidence Interval, understanding Estimate, Estimator and Estimation. Normal population with sigma known or unknown. Confidence intervals for difference of means and for population proportion. We also cover confidence Interval for Difference between Two Population Proportion.
Our Course has been designed such that all concepts of confidence interval will be supported by plenty of numerical examples so that students build a strong foundation of the subject matter.
Statistics as a subject is practical in nature and whether our learners are students enrolled for academic courses or professionals who are adding onto their skill sets, we believe that it's a must for us to ensure that they are able to apply the theory very well in real cases. Thus we have designed our course to make it as practical oriented as possible with sufficient numerical examples and case studies. Our panel of experts have a minimum of Masters Degree and even PhD in many cases in Mathematics and Statistics from renowned universities across the globe and are familiar with latest curriculums and academic requirements.
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