3.92
There is a 95% probability that the true population proportion lies within the confidence interval.
1.96
No, it is not. A 99% confidence interval would be wider. Best regards, NS
r2, the coefficient of determination
The Confidence Interval is a particular type of measurement that estimates a population's parameter. Usually, a confidence interval correlates with a percentage. The certain percentage represents how many of the same type of sample will include the true mean. Therefore, we would be a certain percent confident that the interval contains the true mean.
There is a 95% probability that the true population proportion lies within the confidence interval.
Never!
The independent variable explains .32*100 percent of the variance in the dependent variable.This is 9%.The explainable variance is always the square of the correlation (r).
because the mean is not eual
If the budgeted amount is 0 and the actual amount is $300, what is the variance percentage?
1.96
1.0966
decrease
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
No, it is not. A 99% confidence interval would be wider. Best regards, NS
A bank wishing to estimate the mean balances owed by their MasterCard customers within 75 miles with a 98 percent confidence can use the following formula to calculate the required sample size: Sample size = (Z-score)2 * population standard deviation / (margin of error)2 Where Z-score = 2.326 for 98 percent confidence Population standard deviation = 300 Margin of error = desired confidence intervalSubstituting the values into the formula the required sample size is: 2.3262 * 300 / (Confidence Interval)2 = 553.7Therefore the bank would need to have a sample size of 554 to estimate the mean balances owed by their MasterCard customers within 75 miles with a 98 percent confidence.
Only one selected 68%