This video focuses on Basics of Python for Data Science. Descriptive Statistics including Measures of Central Tendency, Dispersion, Asymmetry and Relationship. Hypothesis Test covered are 2 Sample Independent Ttest, Anova Single Factor, Chi Square Test of Independence. The video also covers Label Encoding, splitting the data frames. Regression models like Multiple Linear Regression, Decision Tree and Random Forest
Showing posts with label Measure of Dispersion. Show all posts
Showing posts with label Measure of Dispersion. Show all posts
Wednesday, October 17, 2018
Python for Data Science - Basics, Descriptive Statistics, Hypothesis Tes...
This video focuses on Basics of Python for Data Science. Descriptive Statistics including Measures of Central Tendency, Dispersion, Asymmetry and Relationship. Hypothesis Test covered are 2 Sample Independent Ttest, Anova Single Factor, Chi Square Test of Independence. The video also covers Label Encoding, splitting the data frames. Regression models like Multiple Linear Regression, Decision Tree and Random Forest
Sunday, September 14, 2014
STANDARD DEVIATION - Descriptive Statistics using Microsoft Excel Statistical Functions - Measure of Dispersion
About the
Data Sheet - The data in this sheet is related to marks scored by 100 Students
in a Statistical Test.
Based on
the data, we will use Microsoft Excel Statistical functions
to analyse the descriptive statistics.
In the Data Sheet, we have Data from
A2:A101, A1 being the header of the Data.
STANDARD DEVIATION
Standard
Deviation is measure of the dispersion of a set of data from its mean. The more
spread apart the data, the higher the deviation. Standard deviation is
calculated as the square root of variance.
Standard
deviation is calculated based on a sample. The standard deviation is a measure
of how widely values are dispersed from the average value (the mean).
= STDEV(number1,number2,...)
STDEV uses the following formula:
where x is the sample mean AVERAGE(number1,number2,…)
and n is the sample size.
Or
=SQRT(Variance)
In the Data Sheet, we have Data from
A2:A101, A1 being the header of the Data.
=STDEV(A2:A101)
Result is 14.24
Or
=SQRT(Variance) or =SQRT(202.7777)
Or
=SQRT(Variance) or =SQRT(202.7777)
STDEV.P
is for population
STDEV.S
is for Sample
VARIANCE - Descriptive Statistics using Microsoft Excel Statistical Functions - Measure of Dispersion
About the
Data Sheet - The data in this sheet is related to marks scored by 100 Students
in a Statistical Test.
Based on
the data, we will use Microsoft Excel Statistical functions
to analyse the descriptive statistics.
In the Data Sheet, we have Data from
A2:A101, A1 being the header of the Data.
Variance
Variance (σ2) is a measure of the dispersion of a
set of data points around their mean value.
In
other words, variance is a mathematical expectation of the average squared
deviations from the mean.
Variance
measures the variability from an average (volatility).
=VAR(number1,[number2],...])
VAR uses the following formula:
where x is the sample mean AVERAGE(number1,number2,…)
and n is the sample size.
In the Data Sheet, we have Data from A2:A101, A1 being the
header of the Data.
=VAR(A2:A101)
Result is 202.7777
VAR.P is for Population
VAR.S is for Sample.
Higher the variance the more the volatility or values
are more different.
Lower the variance the lesser the volatility or values
are less different.
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