Measures of Central Tendency, Measures of Position, Measures of Dispersion, Skewness, Correlation, Regression Analysis - Business Statistics & Research Methods For Assistant Professor Exam
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This document discusses various measures of central tendency and dispersion that are commonly used in epidemiology to summarize data distributions. It describes the mean, median and mode as measures of central tendency that convey the average or typical value, and how the appropriate measure depends on the data's measurement level, shape and research purpose. Measures of dispersion like range, interquartile range, variance and standard deviation describe how spread out the data is from the central value. The document provides formulas and explanations for calculating and interpreting each measure.
Here are the steps to find the quartiles for this data set:
1. Order the data from lowest to highest: 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7
2. The number of observations is 16. To find the quartiles, we split the data into 4 equal parts.
3. n/4 = 16/4 = 4
4. Q1 is the median of the lower half of the data, which is the 4th observation: 2
5. Q2 is the median of all the data, which is also the 8th observation: 3
6. Q3 is the median of the upper half
Descriptive statistics are used to summarize and describe characteristics of a data set. It includes measures of central tendency like mean, median, and mode, measures of variability like range and standard deviation, and the distribution of data through histograms. Inferential statistics are used to generalize results from a sample to the population it represents through estimation of population parameters and hypothesis testing. Correlation and regression analysis are used to study relationships between two or more variables.
The document discusses measures of dispersion, which describe how varied or spread out a data set is around the average value. It defines several measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. The standard deviation is described as the most important measure, as it takes into account all values in the data set and is not overly influenced by outliers. The document provides a detailed example of calculating the standard deviation, which involves finding the differences from the mean, squaring those values, summing them, and taking the square root.
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This document discusses various measures of central tendency and dispersion that are commonly used in epidemiology to summarize data distributions. It describes the mean, median and mode as measures of central tendency that convey the average or typical value, and how the appropriate measure depends on the data's measurement level, shape and research purpose. Measures of dispersion like range, interquartile range, variance and standard deviation describe how spread out the data is from the central value. The document provides formulas and explanations for calculating and interpreting each measure.
Here are the steps to find the quartiles for this data set:
1. Order the data from lowest to highest: 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7
2. The number of observations is 16. To find the quartiles, we split the data into 4 equal parts.
3. n/4 = 16/4 = 4
4. Q1 is the median of the lower half of the data, which is the 4th observation: 2
5. Q2 is the median of all the data, which is also the 8th observation: 3
6. Q3 is the median of the upper half
Descriptive statistics are used to summarize and describe characteristics of a data set. It includes measures of central tendency like mean, median, and mode, measures of variability like range and standard deviation, and the distribution of data through histograms. Inferential statistics are used to generalize results from a sample to the population it represents through estimation of population parameters and hypothesis testing. Correlation and regression analysis are used to study relationships between two or more variables.
The document discusses measures of dispersion, which describe how varied or spread out a data set is around the average value. It defines several measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. The standard deviation is described as the most important measure, as it takes into account all values in the data set and is not overly influenced by outliers. The document provides a detailed example of calculating the standard deviation, which involves finding the differences from the mean, squaring those values, summing them, and taking the square root.
Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
Let me know if anything is required. ping me at google #bobrupakroy
This document provides an overview of descriptive statistics and related concepts. It begins with an introduction to descriptive analysis and then covers various types of variables and levels of measurement. It describes measures of central tendency including mean, median and mode. Measures of dispersion like range, standard deviation and normal distribution are also discussed. The document also covers measures of asymmetry, relationship and concludes with emphasizing the importance of statistical planning in research.
Descriptive statistics is used to describe and summarize key characteristics of a data set. Commonly used measures include central tendency, such as the mean, median, and mode, and measures of dispersion like range, interquartile range, standard deviation, and variance. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when data is arranged in order. The mode is the most frequently occurring value. Measures of dispersion describe how spread out the data is, such as the difference between highest and lowest values (range) or how close values are to the average (standard deviation).
The document discusses various measures used to describe the dispersion or variability in a data set. It defines dispersion as the extent to which values in a distribution differ from the average. Several measures of dispersion are described, including range, interquartile range, mean deviation, and standard deviation. The document also discusses measures of relative standing like percentiles and quartiles, and how they can locate the position of observations within a data set. The learning objectives are to understand how to describe variability, compare distributions, describe relative standing, and understand the shape of distributions using these measures.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the central tendency and dispersion of data distributions.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the distribution, central tendency and dispersion of data.
This document provides an overview of key concepts in descriptive statistics, including measures of center, variation, and relative standing. It discusses the mean, median, mode, range, standard deviation, z-scores, percentiles, quartiles, interquartile range, and boxplots. Formulas and properties of these statistical concepts are presented along with guidelines for interpreting and applying them to describe data distributions.
This document discusses measures of central tendency and dispersion. It defines mean, median and mode as measures of central tendency, which describe the central location of data. The mean is the average value, median is the middle value, and mode is the most frequent value. It also defines measures of dispersion like range, interquartile range, variance and standard deviation, which describe how spread out the data are. Standard deviation in particular measures how far data values are from the mean. Approximately 68%, 95% and 99.7% of observations in a normal distribution fall within 1, 2 and 3 standard deviations of the mean respectively.
BRM_Data Analysis, Interpretation and Reporting Part II.pptAbdifatahAhmedHurre
This document provides an overview of data analysis, interpretation, and reporting. It discusses descriptive and inferential analysis, and univariate, bivariate, and multivariate analysis. Specific quantitative analysis techniques covered include measures of central tendency, dispersion, frequency distributions, histograms, and tests of normality. Hypothesis testing procedures like t-tests, ANOVA, and non-parametric alternatives are also summarized. Steps in hypothesis testing include stating the null hypothesis, choosing a statistical test, specifying the significance level, and deciding whether to reject or fail to reject the null hypothesis based on findings.
This document provides an overview of key concepts in statistics including measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation), and central moments (skewness, kurtosis). It discusses calculating and comparing the mean, median, mode, and how they each describe the central position of a data distribution. It also explains how variance and standard deviation measure how spread out the data is from the mean. The document is intended as a textbook for students and general readers to learn basic statistical concepts.
This document provides an overview of biostatistics and various statistical concepts used in dental sciences. It discusses measures of central tendency including mean, median, and mode. It also covers measures of dispersion such as range, mean deviation, and standard deviation. The normal distribution curve and properties are explained. Various statistical tests are mentioned including t-test, ANOVA, chi-square test, and their applications in dental research. Steps for testing hypotheses and types of errors are summarized.
The document discusses various measures of central tendency and distribution used in statistics. It defines central tendency as a value that represents the center of a data set, such as the mean, median, and mode. It then describes different averages that can be used as measures of central tendency, including the arithmetic mean, median, mode, geometric mean, harmonic mean, and weighted mean. The document also covers measures of dispersion like range, mean deviation, standard deviation, variance, and quartile deviation. It provides formulas and examples to calculate these statistical concepts.
This document provides an overview of basic statistics concepts and terminology. It discusses descriptive and inferential statistics, measures of central tendency (mean, median, mode), measures of variability, distributions, correlations, outliers, frequencies, t-tests, confidence intervals, research designs, hypotheses testing, and data analysis procedures. Key steps in research like research design, data collection, and statistical analysis are outlined. Descriptive statistics are used to describe data while inferential statistics investigate hypotheses about populations. Common statistical analyses and concepts are also defined.
The document discusses various statistical analysis methods and their purposes. It defines descriptive statistics as methods used to describe characteristics of a population or sample, such as measures of central tendency, dispersion, location, and distribution. Inferential statistics draw inferences from samples to make generalizations about populations. The document outlines specific descriptive and inferential statistical tests for both parametric and nonparametric data, and explains that the appropriate statistical method depends on the research problem and level of measurement used in a study.
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
This document discusses descriptive statistics and provides information on various descriptive statistics measures. It defines descriptive statistics as means of organizing and summarizing observations. It describes different types of descriptive statistics including measures of central tendency such as mean, median and mode, and measures of dispersion such as range, variance, standard deviation and interquartile range. Examples are provided to demonstrate how to calculate mean, median and mode from a data set. Additional measures like percentiles, quartiles, boxplots, skewness and kurtosis are also explained.
This document provides an overview of key concepts in statistics including measures of central tendency, measures of dispersion, probability, correlation, time series analysis, and network planning methods like CPM and PERT. It defines terms like mean, median, mode, standard deviation, variance, probability, random experiment, correlation, time series, network, critical path, slack time, and differences between CPM and PERT. It also lists advantages and limitations of using PERT/CPM for project management.
Descriptions of data statistics for researchHarve Abella
This document defines and describes various measures of central tendency and variation that are used to summarize and describe sets of data. It discusses the mean, median, mode, midrange, percentiles, quartiles, range, variance, standard deviation, interquartile range, coefficient of variation, measures of skewness and kurtosis. Examples are provided to demonstrate how to compute and interpret these statistical measures.
This document provides an overview of descriptive statistics and index numbers used in data analysis. It defines descriptive statistics as methods used to describe and summarize patterns in data without making conclusions beyond what is directly observed. Various measures of central tendency like the mean, median, and mode are described as well as measures of dispersion such as range, standard deviation, and variance. Index numbers are constructed to study changes that cannot be measured directly, and weighted indexes like the Laspeyres and Paasche indexes are discussed.
Capital structure theories - NI Approach, NOI approach & MM ApproachSundar B N
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Sample and Population in Research - Meaning, Examples and TypesSundar B N
Sample and Population in Research - Meaning, Examples and Types of Population.
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Let me know if anything is required. ping me at google #bobrupakroy
This document provides an overview of descriptive statistics and related concepts. It begins with an introduction to descriptive analysis and then covers various types of variables and levels of measurement. It describes measures of central tendency including mean, median and mode. Measures of dispersion like range, standard deviation and normal distribution are also discussed. The document also covers measures of asymmetry, relationship and concludes with emphasizing the importance of statistical planning in research.
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The document discusses various measures of central tendency and distribution used in statistics. It defines central tendency as a value that represents the center of a data set, such as the mean, median, and mode. It then describes different averages that can be used as measures of central tendency, including the arithmetic mean, median, mode, geometric mean, harmonic mean, and weighted mean. The document also covers measures of dispersion like range, mean deviation, standard deviation, variance, and quartile deviation. It provides formulas and examples to calculate these statistical concepts.
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The document discusses various statistical analysis methods and their purposes. It defines descriptive statistics as methods used to describe characteristics of a population or sample, such as measures of central tendency, dispersion, location, and distribution. Inferential statistics draw inferences from samples to make generalizations about populations. The document outlines specific descriptive and inferential statistical tests for both parametric and nonparametric data, and explains that the appropriate statistical method depends on the research problem and level of measurement used in a study.
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An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
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Measures of Central Tendency, Measures of Position, Measures of Dispersion, Skewness, Correlation, Regression Analysis - Business Statistics & Research Methods For Assistant Professor Exam
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2. 2
Measures of Central Tendency
Measures of Position
Measures of Dispersion
Skewness
Correlation
Regression Analysis
3. Meaning of Research
Research is an investigative process of finding reliable solution to a
problem through a systematic selection, collection, analysis and
interpretation of data relating to problem.
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4.
5. Data analysis is the process of systematically
applying statistical and logical techniques to
describe, illustrate, condense and recap and
evaluate data
Technically speaking, processing implies editing,
coding, classification and tabulation of collected
data so that they are amenable to analysis.
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6. DATA
Data play the role of raw material for any statistical
investigation and defined in a single sentence as
“The values of different objects collected in a survey or
recorded values of an experiment over a time period taken
together constitute what we call data in Statistics”
Each value in the data is known as observation.
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7. DEFINITION OF STATISTICS
“Statistics are the numerical statements of facts in any department of
enquiry placed in relation to each other.” – A.L. Bowly.
“The science which deals with the collection, tabulation, analysis and
interpretation of numerical data.” – Croxton and Cowden.
“Statistics is a branch of science which deals with collection,
classification, tabulation, analysis and interpretation of data.”
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8. Types of Data Analysis
Descriptive Statistics - provide an overview of the attributes
of a data set. These include measurements of central tendency
(frequency, histograms, mean, median, & mode) and dispersion
(range, variance & standard deviation)
Inferential Statistics - provide measures of how well your
data support your hypothesis and if your data are generalizable
beyond what was tested (significance tests).
These include hypothesis testing and estimates.
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10. Types of Data Analysis
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Parametric
tests
Z-test
ANOVA
Chi-Square
Test
T-test
Non-
Parametric
tests-
Mann-
Whitney test
(U-test)
Kruskal-
Wallis test
(H-test)
Rank
correlation
test
Chi-Square
Test
Inferential Statistics
11. Measures of Central Tendency
● According to Professor Bowley, averages are “statistical constants
which enable us to comprehend in a single effort the
significance of the whole”.
● Measures of central tendency show the tendency of some central
value around which data tend to cluster.
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13. Arithmetic mean (also called mean) is
defined as the sum of all the observations
divided by the number of observations.
It is particularly useful when we are
dealing with a sample as it is least
affected by sampling fluctuations.
It is the most popular average and should
always be our first choice unless there is a
strong reason for not using it.
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14. WEIGHTED MEAN
A weighted mean is a kind of average .
Instead of each data point contributing
equally to the final mean, some data
points contribute more “weight” than
others.
If all the weights are equal, then the
weighted mean equals the arithmetic
mean.
Weighted means are very common in
statistics, especially when
studying populations.
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16. Harmonic Mean
HM is defined as the value
obtained when the number of
values in the data set is divided by
the sum of reciprocals.
The harmonic mean (HM) is
defined as the reciprocal (inverse)
of the arithmetic mean of the
reciprocals of the observations of
a set.
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17. MEDIAN
Median is that value of the variable which
divides the whole distribution into two
equal parts.
The data should be arranged in
ascending or descending order of
magnitude.
When the number of observations is odd
then the median is the middle value of
the data.
For even number of observations, there
will be two middle values.
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18. MODE
Highest frequent observation in the
distribution is known as mode.
In other words, mode is that
observation in a distribution which has
the maximum frequency.
For example, when we say that the
average size of shoes sold in a shop is 7
it is the modal size which is sold most
frequently.
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19. Relationship between Mean, Median
and Mode
For a symmetrical distribution
the mean, median and mode
coincide.
But if the distribution is
moderately asymmetrical, there
is an empirical relationship
between them.
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21. PARTITION VALUES
Partition values are those values of
variable which divide the
distribution into a certain
number of equal parts.
Here it may be noted that the data
should be arranged in ascending or
descending order of magnitude.
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22. Deciles divide whole distribution in
to ten equal parts.
There are nine deciles.
D1, D2,...,D9 are known as 1st Decile,
2nd Decile,...,9th Decile
respectively and ith Decile contains
the (iN/10)th part of data.
Here, it may be noted that the data
should be arranged in ascending or
descending order of magnitude.
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23. PERCENTILE
Percentiles divide whole distribution
in to 100 equal parts.
There are ninety nine percentiles.
P1, P2, …,P99 are known as 1st
percentile, 2nd percentile,…,99th
percentile and ith percentile contains
the (iN/100)th part of data.
Here, it may be noted that the data
should be arranged in ascending or
descending order of magnitude.
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24. QUARTILES
Quartiles divide whole distribution in to four
equal parts.
There are three quartiles- 1st Quartile
denoted as Q1, 2nd Quartile denoted as Q2
and 3rd Quartile as Q3, which divide the
whole data in four parts.
1st Quartile contains the ¼ part of data, 2nd
Quartile contains ½ of the data and 3rd
Quartile contains the ¾ part of data.
Here, it may be noted that the data should be
arranged in ascending or descending order of
magnitude.
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25. 25
Measure of dispersion is
calculated to get an idea
about the variability in
the data.
According to Spiegel, the
degree to which
numerical data tend to
spread about an average
value is called the
variation or dispersion of
data.
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Actually, there are two basic kinds of a measure of
dispersion
(i) Absolute measures - used to measure the
variability of a given data expressed in the same unit.
(ii) Relative measures - used to compare the
variability of two or more sets of observations
28. Range
Range is the simplest measure of dispersion.
It is defined as the difference between the
maximum value of the variable and the
minimum value of the variable in the
distribution.
Its merit lies in its simplicity.
The demerit is that it is a crude measure
because it is using only the maximum and the
minimum observations of variable.
R=X max-X min
where, X max : Maximum value of variable and
X min : Minimum value of variable
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29. Quartile Deviation
Quartile deviation is a statistic that
measures the deviation in the
middle of the data.
Quartile deviation is also referred
to as the semi interquartile
range and is half of the difference
between the third quartile and
the first quartile value.
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30. Mean Deviation
Mean deviation is defined as
average deviation from any
arbitrary value viz. mean, median,
mode, etc.
It is often suggested to calculate it
from the median because it gives
least value when measured from the
median.
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31. VARIANCE
While calculating the mean deviation,
negative deviations are straightaway
made positive.
To overcome this drawback we move
towards the next measure of dispersion
called variance.
Variance is the average of the square of
deviations of the values taken from mean.
Taking a square of the deviation is a
better technique to get rid of negative
deviations.
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32. Standard Deviation
The standard deviation of a
random variable, sample, statistical
population, data set, or probability
distribution is the square root of
its variance.
A low SD and high SD
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33. SKEWNESS
In addition to measures of central
tendency and dispersion,
we also need to have an idea about the
shape of the distribution.
Measure of Skewness gives the
direction and the magnitude of the
lack of symmetry.
Skewness means lack of symmetry.
In Statistics, a distribution is called
symmetric if mean, median and mode
coincide.
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35. VARIOUS MEASURES OF SKEWNESS
Measures of skewness help us to know to what degree and in which
direction (positive or negative) the frequency distribution has a departure
from symmetry.
Although positive or negative skewness can be detected graphically
depending on whether the right tail or the left tail is longer but, we don’t
get idea of the magnitude.
Besides, borderline cases between symmetry and asymmetry may be
difficult to detect graphically.
Hence some statistical measures are required to find the magnitude of lack
of symmetry.
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36. VARIOUS MEASURES OF SKEWNESS
Absolute Measures of Skewness
Following are the absolute measures of
skewness:
1. Skewness (Sk) = Mean – Median
2. Skewness (Sk) = Mean – Mode
3. Skewness (Sk) = (Q3-Q2)-(Q2-Q1)
Relative Measures of Skewness
Karl Pearson’s coefficient of
skewness=
Sk=Mean-Mode/SD
Sk(P)=3(Mean-Mode)/SD
Bowley’s coefficient of skewness
Sk(B)=Q3+Q1-2Md/Q3-Q1
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37. CORRELATION
Correlation was developed in 1885
by Francis Galton
Observations are available on two or more
variables.
When two variables are related in such a
way that change in the value of one
variable affects the value of another
variable, then variables are said to be
correlated or there is correlation
between these two variables.
Examples
1. Heights and weights of persons of
a certain group;
2. Sales revenue and advertising
expenditure in business; and
3. Time spent on study and marks
obtained by students in exam.
39. COEFFICIENT OF CORRELATION
Scatter diagram tells us whether variables are
correlated or not.
But it does not indicate the extent of which they
are correlated.
If X and Y are two random variables then
correlation coefficient between X and Y is
denoted by r and defined as
Coefficient of correlation measures the intensity
or degree of linear relationship between two
variables.
It was given by British Biometrician Karl
Pearson (1867-1936).
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Coefficient of correlation gives the
exact idea of the extent of which
they are correlated.
40. LINEAR REGRESSION
If two variables are correlated significantly, then
it is possible to predict or estimate the values of
one variable from the other.
Regression analysis is a statistical technique which is
used to investigate the relationship between
variables.
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Examples for Simple Regression
The effect of price increase on
demand
The effect of change in the money
supply on the increase rate
Effect of change in expenditure on
advertisement on sales and profit in
business are such examples
where investigators or researchers try to
construct cause and affect relationship.
41. The concept of Regression Analysis
developed by Sir Fransis Galton (1822-
1911) in his article “Regression towards
mediocrity in hereditary structure”
The term, regression, was originally
used in 1885 by Sir Francis Galton in his
analysis of the relationship between the
heights of children and parents.
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Who
developed
Regression
Analysis
42. DISTINCTION BETWEEN CORRELATION
AND REGRESSION
(i) Correlation studies the linear relationship between two variables while
regression analysis is a mathematical measure of the average relationship
between two or more variables.
(ii) Correlation has limited application because it gives the strength of linear
relationship while the purpose of regression is to "predict" the value of the
dependent variable for the given values of one or more independent variables.
(iii) Correlation makes no distinction between independent and dependent variables
while linear regression does it, i.e. correlation does not consider the concept of
dependent and independent variables while in regression analysis one variable is
considered as dependent variable and other(s) is/are as independent variable(s).
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