business stats

discrete variable

discrete variable

a number you can count

continuous variable

A number measured

nominal scale of measurement

observations classified into categories and counted, no logical order to data categories

Descriptive statistics

methods used to organize, summarize and present data in a informative way

Inferential statistics

inferences that go beyond known data

deduction

using general knowledge of a population to draw a conclusion about a specific element or sample of population

inducition

using knowledge of a sample to infer about the population

parameter

any measurable of the population

statistic

any measurable characteristic of the sample

sampling error

when the sample statistic differed from population error

nonsampling error

due to unrecognized systematic causes

mutually exclusive

when a unit is included in one category

exhaustive

when each unit must appear in one category

ordinal scale of measurement

data classfied into mutually exclusive categories, data are ordered or ranked

interval scale of measurement

differences between ranks are equal, NOT a meaningful zero starting point

ration scale of measurement

there is a meaningful zero starting point, ratios between numbers are meaningful

nominal scale of measurement example

gender, type of car

ordinal scale of measurement example

ranking employees, level of satisfaction with a meal

interval scale of measurement example

iq score, temperature

ratio scale of measurement example

income, balance in your account, investments

when to use binomial

when tehre are only two possible outcomes

characteristics of binomial

outcomes are mutually exclusive, the probability of success stays the same from trial to trial, trials are independent

discrete probality distribuiton characteristics

The variable can only assume certain values, Outcomes are mutually exclusive, Sum of the probabilities is = 1

when to use poisson distribuiton

no predetmined number of events

poisson distribuiton characteristics

it’s a special case of discrete probability distribution, Describes the number of times some event occurs
during a specified interval, The interval may be time, distance, area, or volume, It’s positively skewed

68.36 measurements

will lie within one standard deviation

95.44 observations

lie within two standard devaitions of mean

99.97 observations

lie within 3 standard deviation of meant