Definition of statistics A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of quantative and qualitative data.

Why We Study Statistics? (a) Set clearly defined goals for the investigations. (b) Make a plan of what data to collect and how to collect it. (c) Apply appropriate statistical methods to extract information from the data. (d) Interpret the information and draw conclusions.

Why statistics is important for engineering? An engineer is someone who solves problems of interest to society by the efficient application of scientific principles. Engineers accomplish this by either refining an existing product or process or by designing a new product or process that meets customers’ needs. The engineering, or scientific, method is the approach to formulating and solving these problems. The steps in the engineering method are as follows: 1. Develop a clear and concise description of the problem. 2. Identify, at least tentatively, the important factors that affect this problem or that may play a role in its solution. 3. Propose a model for the problem, using scientific or engineering knowledge of the phenomenon being studied. State any limitations or assumptions of the model.

Why statistics is important for engineering? 4. Conduct appropriate experiments and collect data to test or validate the tentative model or conclusions made in steps 2 and Refine the model on the basis of the observed data. 6. Manipulate the model to assist in developing a solution to the problem. 7. Conduct an appropriate experiment to confirm that the proposed solution to the problem is both effective and efficient. 8. Draw conclusions or make recommendations based on the problem solution.

Types of statistics Descriptives statistics: Methods of organizing, summarizing and presenting data in an informative way. Inferential statistics: The methods used to determine something about a population on the basis of a sample.

Definitions Data Observations (such as measurements, genders, survey responses) that have been collected Statistics a collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data

Population the complete collection of all elements (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all subjects to be studied. Census Collection of data from every member of a population. Sample Sub collection of members selected from a population. Definitions

Parameter a numerical measurement describing some characteristic of a population. Population

Statistic a numerical measurement describing some characteristic of a sample. Sample Statistic

Main types of data collection A) Census: A census refers to data collection about every unit in a group or population. Advantages of using census Sampling variance Sampling variance is zero: There is no sampling variability attributed to the statistic because it isstatistic calculated using data from the entire population. Detail: Detailed information about small sub-groups of the population can be made available.

Main types of data collection Disadvantages of using census 1. It is costly in money and time. 2. Uncontrolled: because a census of large population is such a huge undertaking that it makes it difficult to keep every single operation under the same level of control.

Main types of data collection Sample survey In a sample survey, only part of the total population is approached for data. if you collected data about the height of 10 students in a class of 30, that would be a sample survey of the class rather than a census. Advantages of using census 1. Save time, effort and money. 2. We need fewer people to respond in the sample. 3. The smaller scale of this operation allows for better monitoring and quality control.

Main types of data collection Sample survey Disadvantages of using census 1. Sampling variance is non zero. 2. The sample may not be large enough to produce information about small population sub-groups or small geographical areas.

Main types of data collection Administrative data Administrative data are collected as a result of an organization's day-to-day operations such data on births, deaths, marriages and divorces. Advantages of using administrative data 1.Sampling variance is zero. 2.Data becomes a time series which allow for trend analysis. 3.It may eliminate the need to design a census or survey and the associated work. 4.Since the data are already collected, there is no additional burden on the respondents.

Main types of data collection Disadvantages of using administrative data 1. Data items may be limited to essential administrative information, unlike a survey. 2. Data are limited to the population on whom the administrative records are kept. 3. Definitions are created to serve specific purpose, but often change and evolve over time. 4. The definitions are established by those who create and manage the file for their own purposes. 5. The emphasis placed on data quality may differ from organization to organization.

Types of statistical data Quantitative data When the variable studied can be reported numerically, the variable is called a quantitative variable. Example: The income of college graduates, children in a family, height of a student. Qualitative (or attribute) data When the characteristic being studied is nonnumeric, it is called a qualitative variable. Example: The genders (male/female), eye color, type of cars owned, marital status.

Working with Quantitative Data Quantitative data can further be described by distinguishing between discrete and continuous types.

Discrete data Can assume only certain values, and there are usually gaps between the values. Example: The number of Lumps that a factory can produce, the number of bedrooms in a house.

Continuous (numerical) data Observations of continuous variables can assume any value within a specific range. Examples: The air pressure in a tire, and the weight of a shipment of tomatoes.

Types of Data Data QuantativeQualitative Continuous Discrete Nominal Ordinal