Statistics and Probability STA301 - Defination

Statistics:

Statistics is that science which enables to draw conclusions about various phenomena one the basis of real data collected on sample basis. OR Statistics is a science of facts and figures.

INFERENTIAL STATISTICS: That branch of Statistics which enables us to draw conclusions or inferences about various phenomena on the basis of real data collected on sample basis.

Data: A well defined collection of objects is known as data.

Qualitative data: Data that are labels or names used to identify an attribute of each element. Qualitative data may be nonnumeric or numeric.

Qualitative variable: A variable with qualitative data.

Quantitative data: Data that indicate how much or how many of something. Quantitative data are always numeric.

Variable: A measurable quantity which can vary from one individual or object to another is called a variable.

Nominal Scale: The classification or grouping of observations into mutually exclusive qualitative categories is said to constitute a nominal scale e.g. students are classified as male and female.

Ordinal Scale: It includes the characteristic of a nominal scale and in addition has the property of ordering or ranking of measurements e.g. the performance of students can be rated as excellent, good or poor.

Interval Scale: A measurement scale possessing a constant interval size but not true zero point is called an Interval Scale.

Ratio Scale: It is a special kind of an interval scale in which the scale of measurement has a true zero point as its origin.

Biased Errors: An error is said to be biased when the observed value is consistently and constantly higher or lower than the true value.

Unbiased Errors or Random Errors: An error, on the other hand, is said to be unbiased when the deviations, i.e. the excesses and defects, from the true value tend to occur equally often.

Primary Data: The data published or used by an organization which originally collected them are called primary data thus the primary data are the first hand information collected, complied, and published by an organization for a certain purpose.

Secondary Data: The data published or used by an organization other than the one which originally collected them are known as secondary data.

DIRECT PERSONAL INVESTIGATION: In this method, an investigator collects the information personally from the individuals concerned. Since he interviews the informants himself, the information collected is generally considered quite accurate and complete.

INDIRECT INVESTIGATION: Sometimes the direct sources do not exist or the informants hesitate to respond for some reason or other. In such a case, third parties or witnesses having information are interviewed.

Population: The collection of all individuals, items or data under consideration in statistical study is called Population.

Sample: A sample is a group of units selected from a larger group (the population). By studying the sample it is hoped to draw valid conclusions about the larger group. OR Sample is that part of the Population from which information is collected.

SAMPLING FRAME: A sampling frame is a complete list of all the elements in the population. Sampling Error: The sampling error is the difference between the sample statistic and the population parameter.

Non-Sampling Error: Such errors which are not attributable to sampling but arise in the process of data collection even if a complete count is carried out.

Sampling with replacement: Once an element has been included in the sample, it is returned to the population. A previously selected element can be selected again and therefore may appear in the sample more than once.

Sampling without replacement: Once an element has been included in the sample, it is removed from the population and cannot be selected a second time.

Standard error: The standard deviation of a point estimator. AND The degree of scatter of the observed values about the regression line measured by what is called standard deviation of regression or standard error of estimate.

Sampling Unit: The units selected for sampling. A sampling unit may include several elements.

NON-RANDOM SAMPLING: Nonrandom sampling’ implies that kind of sampling in which the population units are drawn into the sample by using one’s personal judgment. This type of sampling is also known as purposive sampling.

Quota Sampling: Quota sampling is a method of sampling widely used in opinion polling and market research. Interviewers are each given a quota of subjects of specified type to attempt to recruit for example, an interviewer might be told to go out and select 20 adult men and 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing.

RANDOM SAMPLING: The theory of statistical sampling rests on the assumption that the selection of the sample units has been carried out in a random manner. By random sampling we mean sampling that has been done by adopting the lottery method.

Simple random sampling: Finite population: a sample selected such that each possible sample of size n has the same probability of being selected.

Infinite population: a sample selected such that each element comes from the same population and the elements are selected independently.

Pie Chart: Pie Chart consists of a circle which is divided into two or more mars in accordance with the number of distinct classes that we have in our data.

SIMPLE BAR CHART: A simple bar chart consists of horizontal or vertical bars of equal width and lengths proportional to values they represent.

MULTIPLE BAR CHARTS: This kind of a chart consists of a set of grouped bars, the lengths of which are proportionate to the values of our variables, and each of which is shaded or colored differently in order to aid identification.

CLASS BOUNDARIES: The true class limits of a class are known as its class boundaries.

HISTOGRAM: A histogram consists of a set of adjacent rectangles whose bases are marked off by class boundaries along the X-axis, and whose heights are proportional to the frequencies associated with the respective classes.

FREQUENCY POLYGON: A frequency polygon is obtained by plotting the class frequencies against the mid-points of the classes, and connecting the points so obtained by straight line segments.

FREQUENCY CURVE: When the frequency polygon is smoothed, we obtain what may be called the frequency curve.

CUMULATIVE FREQUENCY DISTRIBUTION: As in the case of the frequency distribution of a discrete variable, if we start adding the frequencies of our frequency table column-wise, we obtain the column of cumulative frequencies.

AVERAGES (I.E. MEASURES OF CENTRAL TENDENCY): A single value which intended to represent a distribution or a set of data as a whole is called an average. It is more or less a central value around which the observations tend to cluster so it is called measure of central tendency. Since measure of central tendency indicate the location of the distribution on X axis so it is also called measure of location.

The Arithmetic, Geometric and Harmonic means Are averages that are mathematical in character, and give an indication of the magnitude of the observed values.

The Median Indicates the middle position while the mode provides information about the most frequent value in the distribution or the set of data.

THE MODE: The Mode is defined as that value which occurs most frequently in a set of data i.e. it indicates the most common result.

DOT PLOT: The horizontal axis of a dot plot contains a scale for the quantitative variable that we want to represent. The numerical value of each measurement in the data set is located on the horizontal scale by a dot.

GROUPING ERROR: “Grouping error” refers to the error that is introduced by the assumption that all the values falling in a class are equal to the mid-point of the class interval.

Ogive: A graph of a cumulative distribution.

Dispersion: The variability that exists between data set.

Range: The range is defined as the difference between the maximum and minimum values of a data set.

The coefficient of variation: The coefficient of variation expresses the standard deviation as the percentage of the arithmetic mean.

Quartiles: Quartiles are those three quantities that divide the distribution into four equal parts.

Quartile Deviation: The quartile deviation is defined as half of the difference between the first and third quartiles.

Quantiles: Collectively the quartiles, the deciles, percentiles and other values obtained by equall sub-division of the data are called quantiles.

Percentiles: Percentiles are those ninety nine quantities that divide the distribution into hundred equall parts

Absolute measure of dispersion: An absolute measure of dispersion is one that measures the dispersion in terms of the same units or in the square of units, as the units of the data.

Relative measure of dispersion: Relative measure of dispersion is one that is expressed in the form of a ratio, co-efficient of percentage and is independent of the units of measurement.

COEFFICIENT OF QUARTILE DEVIATION: The Coefficient of Quartile Deviation is a pure number and is used for COMPARING the variation in two or more sets of data.

Mean Deviation: The mean deviation is defined as the arithmetic mean of the deviations measured either from the mean or from the median, all deviations being counted as positive.

Variance: Variance is defined as the square of the standard deviation.

Standard Deviation: Standard Deviation is defined as the positive square root of the mean of the squared deviations of the values from their mean.

Five-number summary: An exploratory data analysis technique that uses the following five numbers to summarize the data set: smallest value, first quartile, median, third quartile, and largest value.

Moments: Moments are the arithmetic means of the powers to which the deviations are raised.

Correlation: Correlation is a measure of the strength or the degree of relationship between two random variables. OR Interdependence of two variables is called correlation. OR Correlation is a technique which measures the strength of association between two variables.

RANDOM EXPERIMENT: An experiment which produces different results even though it is repeated a large number of times under essentially similar conditions is called a Random Experiment.

SAMPLE SPACE: A set consisting of all possible outcomes that can result from a random experiment (real or conceptual), can be defined as the sample space for the experiment and is denoted by the letter S. Each possible outcome is a member of the sample space, and is called a sample point in that space.