Question

Define.

(a) Descriptive statistics

(b)Inferential Statistics

(c)Nominal Data

(d) Ordinal data

(e)Standard deviation

Answer 1

DEFINITIONS:

(a) DESCRIPTIVE STATISTICS:

Descriptive statistics refers to methods for summarizing the collected data which can be either a representation of the entire or a sample of a population.The summaries usually consists of graphs and numbers such as averages and percentages. The main purpose of descriptive statistics is to reduce the data to simple summaries without distorting or losing much information.

Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the mean, median, and mode, while measures of variability include the standard deviation, variance, the minimum and maximum variables, and the kurtosis and skewness.

(b) INFERENTIAL STATISTICS:

Inferential statistics refers to methods of making decisions or predictions about a population, based on data obtained from a sample of that population. In most surveys, we have data for a sample, not for the entire population. We use descriptive statistics to summarize the sample data and inferential statistics to make predictions about the population.

(c) NOMINAL DATA:

Nominal data (also known as nominal scale) is a type of categorical data that is used to label variables without providing any quantitative value. Nominal data cannot be ordered and cannot be measured.In a nominal level variable, values are grouped into categories that have no meaningful order.

(d) ORDINAL DATA:

Ordinal data is a statistical type of categorical data in which variables exist in naturally occurring ordered categories. The distance between two categories is not established using ordinal data.

In statistics, a group of ordinal numbers indicates ordinal data and a group of ordinal data are represented using an ordinal scale. The main difference between nominal and ordinal data is that ordinal has an order of categories while nominal doesn’t.

(e) STANDARD DEVIATION:

Standard deviation is the measure of dispersion of a set of data from its mean. It measures the absolute variability of a distribution; the higher the dispersion or variability, the greater is the standard deviation and greater will be the magnitude of the deviation of the value from their mean.