

Confidence Intervals The Summary from class is outlined below. It outlines our textbook, " Lind. (2005). Statistical techniques in business & economics (11th ed). New York: McGrawHill, Chapter 8." A confidence interval is a range of values within which the population parameter is expected to occur. Confidence Intervals define the upper and lower limits of a population mean calculated from a sample of the population. This "guess at the true mean" must be associated with a percentage of confidence to give the inference true meaning. The science of inferential statistics is based on this concept. The factors that determine the width of a confidence interval are:
If the population standard deviation is known or the sample is greater than 30 we use the z distribution. Two Sided based on a sample. If the population standard deviation is unknown and the sample is less than 30 we use the t distribution. Confidence Intervals are calculated by the following equation:
Two sided based on a population. The standard deviation divided by the square root of the number of samples is multiplied by the Z value. The Z value is related to the Normal distribution curve and is the heart of inferential statistics. It is the X axis scalar that correlates to the probability of the entire distribution. Here is a link found by MARGARET HOOPER
Point Estimates: A point estimate is a single value (statistic) used to estimate a population value (parameter). Interval Estimates: An Interval Estimate states the range within which a population parameter probably lies. The interval within which a population parameter is expected to occur is called a confidence interval. The two confidence intervals that are used most extensively in our book are the 95% and the 99%.
Now we can always use these values (when asked) without looking them up in the table. Standard Error of the Sample Mean The standard error of the sample mean is the standard deviation of the sampling distribution of the sample means. It is computed by is the symbol for the standard error of the sample mean. is the standard deviation of the population. n is the size of the sample. If the standard deviation of the population is not known and n > or = to 30, the standard deviation of the sample, designated s, is used to approximate the population standard deviation. The formula for the standard error is: FinitePopulation Correction Factor A population that has a fixed upper bound is said to be finite. For a finite population, where the total number of objects is N and the size of the sample is n. An adjustment is made to the standard errors of the sample means and the proportion: Standard error of the sample means This adjustment is called the finitepopulation correction factor. If n/N < .05, the finitepopulation correction factor is ignored. Standard error of the sample proportions Selecting a Sample Size To determine the size of a sample you should determine:
Do not need to know the size of the population. n = ( ((z x s)^2)/E) E is the allowable error, z is the z value corresponding to the selected level of confidence, and s is the sample deviation of the pilot survey.


