This document discusses different types of statistics. It defines descriptive statistics as summarizing and describing data, while inferential statistics use samples to make inferences about populations. Measures of central tendency like mean, median and mode are described as well as measures of variability such as range, standard deviation and variance. Specific types of each are defined and explained, such as weighted mean, interquartile range, and harmonic mean. Tables and figures are included to illustrate the differences between descriptive and inferential statistics and examples of various statistical measures.

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L F U
First Stage - Group ( A )Faculty of
Business and Economics
Department of Business Administration
First Stage - Group ( A )
ReportFirst Stage - Group ( A )Faculty
of Business and Economics
Department of Business Administration
First Stage - Group ( A )Faculty of
Statistics and types of statistics
Supervised by: Dlshad Mahmood SalehStatistics and
types of statistics
Supervised by: Dlshad Mahmood Saleh
Prepared by: Hawre Idrees KareemSupervised by: Dlshad Mahmood
SalehStatistics and types of
Supervised by:
Prepared by: Hawre Idrees
KareemSupervised by: Dlshad
Mahmood Saleh
Prepared by: Hawre Idrees Kareem
Prepared by:
Prepared by: Hawre Idrees Kareem
Prepared by: Hawre Idrees Kareem
(2022–2023)
Figure
1 types of
statis
tics(
202
2–
ReportFirst Stage - Group ( A )
Report
ReportFirst Stage - Group ( A )
Faculty of Business and
EconomicsLebanese
French University
Faculty of Business and Economics
Department of Business
Administration
First Stage - Group ( A )Faculty
of Business and
EconomicsLebanese
French University
Faculty of Business and
EconomicsLebanese
Report
Report
Report

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Contents
Introduction ............................................................................................................................................3
types of statistics ................................................................................................................................3
what are Descriptive Statistics and Inferential Statistics....................................................................4
difference between Descriptive statistics and Inferential statistics...............................................4
Types of Descriptive Statistics.............................................................................................................6
Types of Measure of Central Tendency ..............................................................................................7
Types of mean.....................................................................................................................................8
Types of Measure of Variability........................................................................................................10
Measure of Variability.......................................................................................................................10
Figure 1 types of statistics....................................................................................................................3
Figure 2 Inferential Vs Descriptive Statistics........................................................................................6
Figure 3 data-variability........................................................................................................................9
Table 1 Inferential Vs Descriptive Statistics - The Difference..............................................................5
Table 2 Types of Measure of Central Tendency..................................................................................7
Table 3 Types of mean ........................................................................................................................8
Table 4 Types of Measure of Variability............................................................................................10

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Figure 1 types of statistics
Introduction
Statistics is a branch of mathematics that deals with collecting, organizing, analyzing,
presenting, and interpreting data. Statistics can help us understand patterns, trends, and
relationships in the world, and make informed decisions based on data. Statistics can be
divided into two main types: descriptive and inferential.
types of statistics

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what are Descriptive Statistics and Inferential Statistics
Descriptive Statistics: and Inferential Statistics are two broad categories in the field of
statistics that have different purposes and methods. Descriptive statistics summarize and
display the characteristics of a data set, such as the mean, median, mode, frequency, and
distribution of the values. Descriptive statistics can precisely describe the data that is
collected from a sample or a population. Inferential statistics, on the other hand, use the
data from a sample to make estimates and test hypotheses about a larger population.
Inferential statistics: on the other hand, use the data from a sample to make estimates and
test hypotheses about a larger population. Inferential statistics allow researchers to draw
conclusions and make predictions based on their data. However, inferential statistics also
involve uncertainty and sampling error, so they cannot guarantee the accuracy or
generalizability of the results.
difference between Descriptive statistics and Inferential statistics
 Descriptive statistics and inferential statistics are two branches of statistics that have
different purposes and methods. Descriptive statistics summarize and display the
data in a meaningful way, such as using tables, charts, graphs, or measures of central
tendency and variability. Descriptive statistics do not make any assumptions or draw
any conclusions about the population from which the data are obtained. They only
describe what is seen in the sample.
 Inferential statistics use the data from a sample to make inferences or predictions
about the population that the sample stands for. Inferential statistics rely on
probability theory and hypothesis testing to decide how likely it is that the results
obtained from the sample are generalizable to the population. Inferential statistics
can also estimate the parameters of the population, such as the mean, proportion,
or correlation coefficient, based on the sample statistics.

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Table 1 Inferential Vs Descriptive Statistics - The Difference
Inferential Vs Descriptive Statistics - The Difference
DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS
The utilization of descriptive statis-
tics researchers has total
crude populace data.
The vast majority of the researchers
take the help of inferential statistics
when the crude populace data is in
huge amounts and can't be Assam-
bled or gathered.
The utilization of descriptive statis-
tics is when inspecting
isn't re- quired.
Here testing measure is required as
the analysis depends on
test boundaries.
Properties of the crude populace are
Mean, median and mode are known
as descriptivestatistics pa- riometers.
Properties of the examining data in
the inferential statistics are not
named as boundaries fairly pro-
nouned as statistics.
This kind of statistics has certain
constraints. One can possibly apply
this while having
really estimated data.
It tends to be applied to a huge
populace of data when the example
data is a delegate of the populace.
The descriptive type of statistics is
quite often 100% precise as there are
no suspicions being made for the
crude populace data
Whereas, inferential statistics
depend on the theories or
conclusions dependent on samples.
That is the reason one can't locate a
100% precision in inferential statis-
tics.

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Figure 2 Inferential Vs Descriptive Statistics
Types of Descriptive Statistics
1. Measure of Central Tendency:
A measure of central tendency is a single value that describes the way in which a group of data
cluster around a central value. In other words, it is a way to summarize the average or typical value
of a dataset. there are three main measures of central tendency, mean, the median, and the mode.
the mean is the sum of all the data values divided by the number of values in the dataset, It is also
known as the arithmetic average, mean is sensitive to outliers, which are extreme values that are
much higher or lower than the rest of the data, median is the middle value of a dataset that has
been arranged in ascending or descending order, It divides the dataset into two equal halves.
Outliers do not affect the median, so it is a more robust measure of central tendency than the mean,
the mode is the most often occurring value in a dataset, There can be more than one mode if two or
more values have the same frequency, or no mode if all values have different frequencies, mode can
be used for any type of data, including nominal data that have no numerical value.

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Table 2 Types of Measure of Central Tendency
Types of Measure of Central Tendency
 Mean: The mean is the average of a set of numbers. It is calculated by adding up all
the numbers and dividing by the number of values. The mean is a useful measure of
central tendency when the data is symmetric and has no outliers.
 Median: The median is the middle value of a set of numbers when they are arranged
in ascending or descending order. If there is an odd number of values, the median is
the middle one. If there is an even number of values, the median is the average of
the middle two. The median is a useful measure of central tendency when the data is
skewed or has outliers.
 Mode: The mode is the most frequent value in a set of numbers. There can be more
than one mode if two or more values have the same frequency. The mode is a useful
measure of central tendency when the data is categorical or discrete.
Central Tendency
Mode
Mean Median

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Table 3Types of mean
Types of mean
 Geometric: The geometric mean of n positive numbers is the n-the root of their
product. It is often used to calculate average rates of change or growth, such as
population growth or compound interest.
 Harmonic: The harmonic mean of n positive numbers is the reciprocal of the
arithmetic mean of their reciprocals. It is often used to calculate average rates when
dealing with ratios or fractions, such as speed, density, or harmonic frequency.
 Weighted: The weighted mean of n numbers is the sum of their products with their
corresponding weights divided by the sum of the weights. It is often used to
calculate average values when some data values have more importance or influence
than others, such as grades, test scores, or survey responses.
Mean
Weighted
Geometric Harmonic

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Figure 3 data-variability
2. Measure of Variability:
A measure of variability is a single value that describes the spread or dispersion of a dataset. In other
words, it is a way to summarize how much the data values differ from each other, there are several
measures of variability, such as the range, the interquartile range, the standard deviation, and the
variance, range is the difference between the maximum and minimum values in a dataset, It gives an
estimate of how large the spread of the data is, but it does not consider how the data are
distributed, interquartile range (IQR) is the difference between the first quartile (Q1) and the third
quartile (Q3) of a dataset, first quartile is the median of the lower half of the data, and the third
quartile is the median of the upper half of the data, IQR shows how much variation there is in the
middle 50% of the data, and it is not affected by outliers, standard deviation (SD) is a measure of
how much each data value deviates from the mean, It is calculated by taking the square root of the
variance, standard deviation tells us how closely clustered or widely dispersed the data values are
around the mean, variance (V) is a measure of how much each data value deviates from the mean
squared. It is calculated by taking the average of the squared differences between each data value
and the mean, variance gives an idea of how much variation there is in the entire dataset, but it is
not easy to interpret because it has different units than the original data.

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Table 4 Types of Measure of Variability
Types of Measure of Variability
 Range: is the difference between the highest and lowest values in the data set. It is
the simplest measure of variability to calculate, but it is influenced by outliers and
does not give any information about the distribution of values.
 Variance: is the average of squared distances from the mean. It measures how far
each value is from the mean on average. Squaring the distances ensures that positive
and negative deviations do not cancel out. However, variance is not in the same unit
as the original data and can be difficult to interpret.
 Dispersion:
are the three commonly used measures of dispersion.
1. Interquartile range (IQR): the difference between the third quartile (Q3) and the
first quartile (Q1) of the data set. It is the range of the middle 50% of the data,
and it is less affected by outliers than the range. It is a good measure of
variability dispersion for skewed distributions.
2. Variance: the average of the squared distances of each value from the mean of
the data set. It is a measure of how much the values deviate from the center of
the distribution. It is always positive, and it has a different unit than the original
data.
3. Standard deviation: the square root of the variance. It is a measure of how much
the values deviate from the center of the distribution. It has the same unit as the
original data, and it is easier to interpret than the variance.
These measures of variability dispersion can help us compare different data sets
or different aspects of the same data set. For example, we can use them to
assess how reliable a sample mean is as an estimate of a population mean, or
how homogeneous or heterogeneous a population is.
Measure of Variability
Dispersion
Range Variance