Ch 12- descriptive and inferential statistics Flashcards
what is descriptive statistics?
they are stats that help define the sample, by showing measures of central tendency and variance. mean, mode, median, standard deviation and range.
statistical inference ?
inference analysis is used to generate conclusions about a population using the samples descriptive statistics. Inference analysis is used to generate conclusions about a population’s characteristics based on the sample data. test for standard error, null hypothesis and confidence intervals.
12-6 Statistical Inference: Sample Statistics and Population Parameter ?
whenever a probability sample is drawn from a population its not enough to just report the descriptive statistics because there are errors in the sampling process. because a census is impractical a sample of the population is taken. every sample statistic has a corresponding population parameter. for example- using the sample statistic of P (sample statistic percentage) to draw a conclusion of the population percentage known as pi.
a larger sample equates to what ?
less sampling error- the smaller the confidence interval
the two types of statistical inference?
parameter estimates and hypothesis testing.
what is a parameter estimate ?
is used to approximate the population value (parameter) by using confidence interval. basically a plus or minus after the fact and says the population parameter fall between that range.
what is hypothesis testing ?
hypothesis testing is used to compare the population statistic with what is believed to be the population parameter. before undertaking the test.
Parameter Estimation: Estimating the Population Percentage or Mean?
Parameter estimation is the process of using sample information to compute an interval that describes the range of a parameter such as the population mean (m) or the population percent-age (p). It involves the use of three values: the sample statistic (such as the mean or the percentage), the standard error of the statistic, and the desired level of confidence (usually 95% or 99%).
what is the standard error ?
the standard error is the measure of variability in a sampling distribution, its based on what we would think would occur if we took a multitude of independant samples from the population.
formula of a standard error for a mean and percentage ?
standard error of a mean example ?
2 example:
std=20
std=40
given that the sample size is 100 for both
standard error of the percentage example?
2 examples- same number of participants, 100 for both examples.
P= 90
p=50
in the first example percentage P= 90 therefor Q=10
in the second example P=50 therefore, Q=50
since, q=P-Q
what are confidence intervals ?
is the degree of accuracy desired by the researcher.
most common confidence intervals are 95% = z0.05= 1.96
formular for confidence intervals of a mean and percentage?
confidence intervals mean and percentage examples ?
mean= 45
std dev= 20
n=100
p=50
then q =50
n=100
where the Sx stand for standard error of the mean
and the Sp stands for standard error of percentage.
the steps in computing confidence intervals for both a mean and a percentage are ?
what is the hypothesis testing formular?
hypothesiszed percentage example ?
hypothesiszed percentage = 10
sample percentage statisic (p)= 80
therefore q= 20
n=1000
and then depending on the level of confidence lets say 95%= 1.96 plus or minus. you compare the 55.3 to it and since its bigger we can reject the null hypothesis at a 95% level of confidence.
hypothesized mean example ?
given that
Hypothesized mean = 2750
smale mean = 2800
std dev= 350
n=100
what are the 5 basic steps in hypothesized testing ? mena and percentage
where is the hypothesized mean displayed on the spss and where is the sample statistics ?
