Download Terms: Population, Sample, Parameter, Statistic, Census, Random

Terms:
Population, Sample, Parameter, Statistic, Census, Random Sample, Simple Random Sample,
Sampling Frame, Probability experiment, Outcome, Sample space, Event, Probability of an
event, Null event, Mutually exclusive events, Independent events, Random variable, Discrete
random variable, Probability mass function of a discrete random variable, Continuous random
variable, Probability density function of a continuous random variable, Mean (Expected value) of
a random variable, Variance and standard deviation of a random variable, Cumulative
distribution of a random variable, Standard normal density, Standardizing a normal random
variable, Standard error of a statistic, Estimator, Estimate, Unbiased estimator, Central Limit
Theorem, Margin of error, Confidence level, Null hypothesis, Alternative hypothesis, Decisions
in hypothesis testing, Type I error, Type II error, Level of significance, One-sided and two-sided
alternative hypotheses, One-tailed and Two-tailed rejection regions, p-value, Paired data,
Independent random samples, Goodness of fit test, Test for homogeneity, Independent variable,
Dependent variable, Scatterplot, Linear correlation coefficient, Fitted value, Residual, Least
squares regression line, Error sum of squares, Mean squared error
Notation:
Sample mean, Sample median, Sample variance, Sample standard deviation, Population mean,
Population variance, Sample space, Event, Probability of an event, Null event, Union event,
Intersection event, Complement of an event, Conditional probability of B given A, Random
variable, Probability mass function of a discrete random variable, Cumulative distribution
function, Mean of a random variable, Variance and standard deviation of a random variable,
Probability density function of a continuous random variable, N(  ,  2 ), Standard normal
random variable, z  , Mean of the sample mean, Variance of the sample mean, Standard error of
the sample mean, Sample proportion, Mean of the sample proportion, Variance of the sample
proportion, Standard error of the sample proportion, Confidence level, Null hypothesis,
Alternative hypothesis, Probability of a type I error, Probability of a type II error, Level of
significance, p-value, t α, df , Population median, χ 2 α, df , F α, df1 , df2 , Linear correlation coefficient,
Least squares regression line, Notation for the assumptions for inferential statistics in regression
Lists:
3 reasons to take a sample instead of a census, 3 properties of a probability mass function of a
discrete random variable, 4 properties of a binomial experiment, 3 properties of a probability
density function of a continuous random variable, 4 properties of a normal density function, 2
measures of the quality of an interval estimator, 5 properties of a student’s t distribution, 4
properties of a Chi-Square distribution, 4 properties of the F distribution, 5 continuous random
variables, 3 properties of r, 2 goals in regression analysis, 3 properties of the least squares
regression line
Other:
When to use median instead of mean, Physical interpretation of sample mean, Formula to use for
P(A) if the outcomes in S are equally likely, Physical representation of the mean of a discrete
random variable, Physical representation of the mean of a continuous random variable, Use of
the standard normal table, The unbiased estimator for the population mean, The unbiased
estimator for the population proportion, Difference between the two questions in middle of p.
110, Interpretation of a confidence interval estimate using the words of the problem, The
assumption made in hypothesis testing, Writing a conclusion of a hypothesis test using the words
of the problem, How to make a decision based on a p-value, Measuring the strength of evidence
supporting H1, How to make a decision based on a confidence interval, Formula for sample
proportion, Use of the student’s t table, Use of the Chi-Square table, Use of the F table, Problem
Recognition, Interpretation of β1 , Confidence interval versus prediction interval in regression