That, my friend, is not a question.
No, it is not. A 99% confidence interval would be wider. Best regards, NS
t-test for means
A variable measured at the interval or ratio level can have more than one arithmetic mean.
Interval notation is a method of writing down a set of numbers. An example of this is all numbers that are greater than five.Ê
Time is ratio data because it has a true, meaningful data. You can say that at time 20 seconds, it is twice the amount of time than 10 seconds. Interval data doesn't have a true zero e.g. degrees celcius. Although you can say 60 degrees is hotter than 30 degrees you can't say that it is twice as hot.
No, it is not. A 99% confidence interval would be wider. Best regards, NS
Never!
if the confidence interval is 24.4 to 38.0 than the average is the exact middle: 31.2, and the margin of error is 6.8
The increase in sample size will reduce the confidence interval. The increase in standard deviation will increase the confidence interval. The confidence interval is not based on a linear function so the overall effect will require some calculations based on the levels before and after these changes. It would depend on the relative rates at which the change in sample size and change in standard deviation occurred. If the sample size increased more quickly than then standard deviation, in some sense, then the size of the confidence interval would decrease. Conversely, if the standard deviation increased more quickly than the sample size, in some sense, then the size of the confidence interval would increase.
Yes. Simply make sure that the interval is greater than or equal to the range of the random variable.
Yes, a confidence interval can include negative values, especially when estimating parameters that can take on negative values, such as differences in means or certain regression coefficients. For instance, if you are estimating the difference between two means and the interval ranges from -2 to 5, it indicates that the true difference could be negative, suggesting that one mean may be less than the other. The presence of negative values in a confidence interval reflects the uncertainty and variability in the estimate.
A confidence interval, for a given probability, is the interval within which the true value may be found with that probability if the null hypothesis is true. There are two possible reasons why a confidence interval may be asymmetrical. One is that the alternative hypothesis is asymmetrical: for example, H0 is X = 5 and H1 is X > 5 (rather than X ≠5). The other possible reason is that the test statistic has an asymmetrical distribution. Either of these can give rise to asymmetrical CIs.
The mean is sensitive to outliers and skewed data, which can distort the confidence interval, making it wider or narrower than it should be. In contrast, the median is a robust measure of central tendency that is less affected by extreme values, providing a more reliable confidence interval in skewed distributions. Therefore, using the median can yield a more accurate representation of the data's central tendency when the dataset contains outliers. Choosing between mean and median depends on the data's distribution characteristics and the specific analysis requirements.
Expected value is the outcome of confidence of how probability distribution is characterized. If the expected value is greater than the confidence interval then the results are significant.
t-test for means
The moon is not wider than the earth. Therefore, it can not be much wider than the earth.
If the first derivative of a function is greater than 0 on an interval, then the function is increasing on that interval. If the first derivative of a function is less than 0 on an interval, then the function is decreasing on that interval. If the second derivative of a function is greater than 0 on an interval, then the function is concave up on that interval. If the second derivative of a function is less than 0 on an interval, then the function is concave down on that interval.