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Perplex
Dashboard
Topics
Exponents & LogarithmsApproximations & ErrorSequences & SeriesMatricesComplex NumbersFinancial Mathematics
Cartesian plane & linesFunction TheoryModellingTransformations & asymptotes
2D & 3D GeometryVoronoi DiagramsTrig equations & identitiesVectorsGraph Theory
ProbabilityDescriptive StatisticsBivariate StatisticsDistributions & Random VariablesInference & Hypotheses
DifferentiationIntegrationDifferential Equations
Review VideosFormula BookletMy Progress
BlogLanding Page
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Perplex
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Distributions & Random Variables
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Normal Distribution
Poisson Distribution
Normal Distribution
Distributions & Random Variables

Normal Distribution

0 of 0 exercises completed

Introduction and definition of the normal distribution, standard deviations, normal and inverse normal calculations, ​z​-values, normal standardization

Want a deeper conceptual understanding? Try our interactive lesson! (Plus Only)

The Normal Distribution
SL 4.9

The normal distribution, often called the bell curve, is a symmetric, bell-shaped probability distribution widely used to model natural variability and measurement errors. It appears frequently in natural settings because averaging many small, independent effects tends to produce results that cluster around a central value, naturally forming a bell-shaped distribution.


The normal distribution is characterized by its mean, ​μ, and standard deviation, ​σ, which completely and uniquely describe both the central value and how "spread out" the curve is. By convention, we describing the normal distribution by writing ​X∼N(μ,σ2). Notice that ​σ2​ is the variance, not the standard deviation.


The probability that ​X​ is less than a given value ​a, written ​P(X<a), is equal to the area under the curve to the left of ​x=a:

It follows that the total area under the curve is ​1, which is required as the probabilities must sum to ​1.

The Bell Curve properties
SL 4.9

Because of the symmetry of the normal distribution, we know that

​
P(X>μ)=P(X<μ)=21​=0.5🚫
​

Further, for any real number ​a,

​
P(X>μ+a)=P(X<μ−a)
​


The Empirical Rule
SL 4.9

It is also useful (but not often required) to know the empirical rule:

​
P(μ−σ<X<μ+σ)P(μ−2σ<X<μ+2σ)P(μ−3σ<X<μ+3σ)​≈68%≈95%≈99.7%​
​
Normal calculations
SL 4.9

To calculate ​P(a<X<b)​ for ​X∼N(μ,σ2)​ on your GDC, press 2nd ​→​distr (on top of vars) to open the probability distribution menu. Select normalcdf( with your cursor. Type the value of ​a​ after "lower," the value of ​b​ after "upper," the value of ​μ​ after "​μ," and the value of ​√σ2=σ​ after ​σ​ (since the calculator asks for standard deviation, not variance). Then click enter twice and the calculator will return the value of ​P(a<X<b).


If you want to find a one-sided probability like ​P(a<X), enter the value ​±1×1099​ as the upper or lower bound.


Under the hood, the calculator is finding the area under the normal curve between ​x=a​ and ​x=b:

Inverse Normal Calculations
SL 4.9

The calculator can also perform inverse normal calculations. That is, given the mean ​μ, the standard deviation ​σ, and the probability ​P(X<a)=k, the calculator can find the value ​a.


On your GDC, press 2nd ​→​distr (on top of vars) to open the probability distribution menu. Select invNorm( with your cursor. Type the value of ​k​ after "area," the value of ​μ​ after "​μ," and the value of ​√σ2=σ​ after ​σ​ (since the calculator asks for standard deviation, not variance). Then click enter twice and the calculator will return the value of ​a.


Note the calculator specifically returns the value of the "left end" of the tail. To find the value of some ​b​ when given ​P(b<X)=k, enter the value of ​1−k​ (the complement) as the area.

Nice work completing Normal Distribution, here's a quick recap of what we covered:

Skills covered

Mixed Practice

Exercises checked off

I'm Plex, here to help you understand this concept!
/
Distributions & Random Variables
/
Normal Distribution
Poisson Distribution
Normal Distribution
Distributions & Random Variables

Normal Distribution

0 of 0 exercises completed

Introduction and definition of the normal distribution, standard deviations, normal and inverse normal calculations, ​z​-values, normal standardization

Want a deeper conceptual understanding? Try our interactive lesson! (Plus Only)

The Normal Distribution
SL 4.9

The normal distribution, often called the bell curve, is a symmetric, bell-shaped probability distribution widely used to model natural variability and measurement errors. It appears frequently in natural settings because averaging many small, independent effects tends to produce results that cluster around a central value, naturally forming a bell-shaped distribution.


The normal distribution is characterized by its mean, ​μ, and standard deviation, ​σ, which completely and uniquely describe both the central value and how "spread out" the curve is. By convention, we describing the normal distribution by writing ​X∼N(μ,σ2). Notice that ​σ2​ is the variance, not the standard deviation.


The probability that ​X​ is less than a given value ​a, written ​P(X<a), is equal to the area under the curve to the left of ​x=a:

It follows that the total area under the curve is ​1, which is required as the probabilities must sum to ​1.

The Bell Curve properties
SL 4.9

Because of the symmetry of the normal distribution, we know that

​
P(X>μ)=P(X<μ)=21​=0.5🚫
​

Further, for any real number ​a,

​
P(X>μ+a)=P(X<μ−a)
​


The Empirical Rule
SL 4.9

It is also useful (but not often required) to know the empirical rule:

​
P(μ−σ<X<μ+σ)P(μ−2σ<X<μ+2σ)P(μ−3σ<X<μ+3σ)​≈68%≈95%≈99.7%​
​
Normal calculations
SL 4.9

To calculate ​P(a<X<b)​ for ​X∼N(μ,σ2)​ on your GDC, press 2nd ​→​distr (on top of vars) to open the probability distribution menu. Select normalcdf( with your cursor. Type the value of ​a​ after "lower," the value of ​b​ after "upper," the value of ​μ​ after "​μ," and the value of ​√σ2=σ​ after ​σ​ (since the calculator asks for standard deviation, not variance). Then click enter twice and the calculator will return the value of ​P(a<X<b).


If you want to find a one-sided probability like ​P(a<X), enter the value ​±1×1099​ as the upper or lower bound.


Under the hood, the calculator is finding the area under the normal curve between ​x=a​ and ​x=b:

Inverse Normal Calculations
SL 4.9

The calculator can also perform inverse normal calculations. That is, given the mean ​μ, the standard deviation ​σ, and the probability ​P(X<a)=k, the calculator can find the value ​a.


On your GDC, press 2nd ​→​distr (on top of vars) to open the probability distribution menu. Select invNorm( with your cursor. Type the value of ​k​ after "area," the value of ​μ​ after "​μ," and the value of ​√σ2=σ​ after ​σ​ (since the calculator asks for standard deviation, not variance). Then click enter twice and the calculator will return the value of ​a.


Note the calculator specifically returns the value of the "left end" of the tail. To find the value of some ​b​ when given ​P(b<X)=k, enter the value of ​1−k​ (the complement) as the area.

Nice work completing Normal Distribution, here's a quick recap of what we covered:

Skills covered

Mixed Practice

Exercises checked off

I'm Plex, here to help you understand this concept!

Generating starter questions...

Generating starter questions...