Statistics (Formula and Definition), Study notes of Mathematics

This document includes the different measures of central tendency, and anything related to statistics.

Typology: Study notes

2017/2018

Available from 01/28/2022

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REVIEWER IN RESEARCH
I. Measures of Central Tendency
Central Tendency
-descriptive statistic
-measures of the average
-middle
Mean
-average
-uses all score in the data set
-affected by anomalies
Formula:
x=x
n
Median
-middle value
Formula:
~
x=n
2
*n=number of data in a given set
Mode
-most common value
-greatest frequency
Formula:
^
x=¿
most frequent
II. Measures of Variability
Range
-the simplest measure of
variation to compute
-the difference between the
smallest and the largest value in a
given set
Standard Deviation
-most commonly used
measure of variation
-indicates how closely the
values of a given data set are
clustered around the mean.
-the positive square root of
the variance
-denoted by
σ
Formula:
σ=
(
xμ
)
2
N
¿
¿
Variance
-denoted by
σ2
Formula:
σ2=
(
xμ
)
2
N
¿
¿
Unbiased Estimator
-a statistic whose value
approximates the expected value of
a population variance
Formula:
Where
x
= sample mean
N= sample size
Shortcut formulas
-they save time when
repeated subtracting and squaring
occur in the original formulas
δ2=nx2
(
x
)
2
n
(
n1
)
¿
¿
δ
=
s2
pf2

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REVIEWER IN RESEARCH

I. Measures of Central TendencyCentral Tendency

- descriptive statistic - measures of the average - middle  Mean - average - uses all score in the data set - affected by anomalies Formula:

x =

x

n

Median

- middle value Formula:

~ x = n

*n=number of data in a given set  Mode -most common value -greatest frequency Formula :

^ x =¿ most frequent

II. Measures of VariabilityRange -the simplest measure of variation to compute -the difference between the smallest and the largest value in a given set  Standard Deviation -most commonly used measure of variation -indicates how closely the values of a given data set are clustered around the mean. -the positive square root of the variance

-denoted by σ

Formula:

( x − μ )

2

N

Variance

-denoted by σ^2

Formula:

2 =∑

( x − μ )

2

N

Unbiased Estimator

- a statistic whose value approximates the expected value of a population variance Formula:

2 =∑

( x − x )

2

n − 1

Where x = sample mean

N= sample size  Shortcut formulas

- they save time when repeated subtracting and squaring occur in the original formulas

2 = n (^) ∑ x 2

(∑ x^ ) 2

n ( n − 1 )

δ = (^) √ s^2