Stats exam1

Simple random sampling

We choose a sample of items in such a way that every sample of the same size has an equal chance of being selected.

Systematic sampling

We use a simple system to choose the sample, such as selecting every 10th or every 50th member of the population.

Convenience sampling

We use a sample that happens to be convenient to select.

Cluster sampling

We first divide the population into groups, or clusters, and select some of these clusters at random. We then obtain the sample by choosing all the members within each of the selected clusters.

Stratified sampling

We use this method when we are concerned about differences among subgroups, or strata, within a population. We first identify the strata and then draw a random sample within each stratum. The total sample consists of all the samples from the individual strata.

double barreled questions

asks two things but only allows one answer

bias

Any problem in the design or conduct of a statistical study that tends to favor certain results.

representative sample

A sample in which the relevant characteristics of the members are generally the same as the characteristics of the population

inference

a conclusion reached on the basis of evidence and reasoning.

sample

A subset of the population from which data are actually obtained.

census

a collection of data from every member of a population

confidence interval

A range of values associated with a confidence level, such as 95%, that is likely to contain the true value of a population parameter. (range estimate of

margin of error

a measure of how precise we believe a point estimate is relative to the true parameter.

sample statistics

numbers describing characteristics of the sample

parameter

a numerical characteristic of a population

population

the complete group that is being studied.

categorical (qualitive data)

place each observation into a non-numerical category. Examples: shoe brand, favorite color, zip code, major.

quantitative (numerical) data

measurements recorded as numbers with units. Examples: duration (minutes), distance (meters), weight (kg), cost (dollars)

variable

s something that can take different values across individuals or time

constant

does not vary in the context of your dataset

discrete

values occur in countable steps (often integers). Example: shoe size, number of siblings.

continuous

any value in an interval is possible in principle. Example: weight, time, distance, temperature.

nominal level of measurement

categories with no natural order (e.g., zip code, eye color)

ordinal level of measurement

ordered categories, but differences are not numerically meaningful (e.g., small/medium/large; class rank).

ranked level of measurement

a common special case of ordinal data where categories are ranks (1st, 2nd, ...). Differences between ranks are not “equal distances” in general.

interval level of measurement

numeric scale with meaningful differences, but no true zero. Example: temperature in ◦C or ◦F. (20◦C is not “twice as hot” as 10◦C.

ratio level of measurement

numeric scale with a true zero; ratios are meaningful. Example: weight, length, time, Kelvin temperature.

absolute error

M-T

relative error

M-T/T

P% of

P/100

P% more than

1+P/100

P% less than

1-P/100

frequency table

summarizes categorical data by listing each category and the frequency (count) of observations that fall in that category

relative frequency

relative frequency = frequency in category/ total number of observations.

two way table

summarizes the relationship between two categorical variables

contingency table

two-way table where one sample is classified in two ways

distribution

set of relative frequencies for all categories

cumulative frequency

running total of the counts up to a given category

cumulative relative frequency

running total of the relative frequencies