The statement that 99% of all confidence intervals with a 99% confidence level should contain the population parameter of interest is false.
A confidence interval (CI) is essentially a range of estimates for an unknown parameter in frequentist statistics. The most frequent confidence level is 95%, but other levels, such 90% or 99%, are infrequently used for generating confidence intervals.
The confidence level is a measurement of the proportion of long-term associated CIs that include the parameter's true value. This is closely related to the moment-based estimate approach.
In a straightforward illustration, when the population mean is the quantity that needs to be estimated, the sample mean is a straightforward estimate. The population variance can also be calculated using the sample variance. Using the sample mean and the true mean's probability.
Hence we can generally infer that the given statement is false.
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The chi-squared test statistic will be 3.11. The test statistic is contrasted with a predicted value based on the Chi-square distribution.
<h3>What is the chi-squared test statistic?</h3>
Finding the squared difference between the actual and anticipated data values, then dividing that difference by the expected data values, constitutes the test statistic.
The formula for the chi-squared test statistic is;
Where,
is the observed value
is the expected value
The chi-square test statics is;
Hence, the chi-squared test statistic will be 3.11.
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12.50x4=50. If he works more than 4 hours he will earn more money from hourly.
