•Science of gathering, analyzing, interpreting, and presenting data •Measurement taken on a sample

•Type of distribution being used to analyze data

Descriptive statistics:

Using data gathered on a group to describe or reach conclusions about that same group only. Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data.

Collect, organize, summarize, display, analyze

Eg: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2002. The statistic 9 describes the number of problems out of every 100 machines.

Inferential statistics:

Using sample data to reach conclusions about the population from which the sample was taken. Statistical inference is the process of using data obtained from a small group of elements (the sample) to make estimates and test hypotheses about the characteristics of a larger group of elements (the population).

Predict/forecast, make estimates about population behavior based on sample, , test hypothesis, make decisions

Eg 1: TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers.

Eg 2: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

Data:

Data are the facts and figures that are collected, summarized, analyzed, and interpreted. The data collected in a particular study are referred to as the data set. The elements are the entities on which data are collected. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular element is called an observation. The total number of data values in a data set is the number of elements multiplied by the number of variables.

Why do we need data:

•To assist in formulating alternative courses of action •To provide input to study

•To measure performance of service or production process •To provide input to survey

•To evaluate conformance to standards

•To satisfy curiosity

•Best use of imperfect information. Eg: 50,000 customers, 1,600 workers, 386,000 transactions •Good decisions in uncertain conditions Eg: New product launch: Fail? OK? Make you rich? •Competitive Edge Eg: For you in the job market!

Qualitative data:

Data of the nominal or ordinal level that classifies by a label or category. The labels may be numeric or nonnumeric. The statistical analysis for qualitative data, are rather limited.

Eg: Gender, religious affiliation, type of automobile owned, state of birth, eye color are examples

Quantitative data:

Data is of the interval or ratio level that measures on a naturally occurring numeric scale. Quantitative data indicate either how many or how much. Quantitative data that measure how many are discrete. Quantitative data are always numeric. Quantitative data that measure how much are continuous because there is no separation between the possible values for the data. Ordinary arithmetic operations are meaningful only with quantitative data.

Eg: balance in your checking account, minutes remaining in class, or number of children in a family

Discrete data:

Numeric data in which the values can come only from a list of specific values. Discrete data results from a counting process.

Eg: The number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1, 2, 3 etc).

Continuous data:

Numeric data that can take on values at every point over a given interval. Continuous data result from a measuring process

Eg: The pressure in a tire, the weight of a pork chop, or the height of students in a class

Applications in business:

Accounting: Public accounting firms use statistical sampling procedures when conducting audits for their clients.

Finance: Financial advisors use a variety of statistical information,...