Quick and Concise Stats Notes with ALL key elements Included
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Course
Statistical Methods (X_401020)
Institution
Vrije Universiteit Amsterdam (VU)
Book
Elementary Statistics in Social Research: Pearson New International Edition
If you need a quick recap go through my summary, it is well organized and easy to follow. Perfect for someone who doesn't quite know the true reason behind why we do certain steps in statistics and needs a quick and easy summary.
FINAL - SUMMARY (DOES NOT INCLUDE MIDTERM CONTENT)
GENERAL FORMULAS:
For the sample population, the denominator is n - 1 whereby N or n means the total number
SAMPLING DIST OF SAMPLE MEAN AND PROPORTION:
,ESTIMATING POPULATION PROPORTION:
- The sample proportion p is the best point estimate of the population proportion p.
- CONFIDENCE INTERVAL: The confidence level is the probability 1 - a (such as 0.95, or
95%) that the confidence interval actually does contain the population parameter,
assuming that the estimation process is repeated a large number of times. (The confi-
dence level is also called the degree of confidence, or the confidence coefficient.)
- CRITICAL VALUE: A critical value is the number on the borderline separating sample
statistics that are likely to occur from those that are unlikely. The number za>2 is a
critical value that is a z score with the property that it separates an area of a/2 in the right
tail of the standard normal distribution (as in Figure 2).
-
Find Confidence Intervals proportions:
CONFIDENCE INTERVAL FOR ESTIMATONG PROP
Finding the Sample Size Required to Estimate a Population Proportion:
,Round to the whole number for n
Finding the Point Estimate and E from a Confidence Interval
, ESTIMATING POPULATION MEAN:
Find Confidence Intervals Meam:
CONFIDENCE INTERVAL FOR ESTIMATING MEAN STDV NOT KNOWN
Important Properties of the Student t Distribution:
Because s is rarely known, confidence interval estimates of a population mean m almost always
use the Student t distribution instead of the standard normal distribu- tion. Here are some
important properties of the Student t distribution.
1. The Student t distribution is different for different sample sizes. (See Figure 5
for the cases n = 3 and n = 12.)
2. The Student t distribution has the same general symmetric bell shape as the standard normal
distribution, but has more variability (with wider distribu- tions) as we expect with small samples.
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