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
Sign UpLogin
Perplex
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
Sign UpLogin
Perplex
/
Modelling
/
Logistic Models
Power Models & Proportionality
Logistic Models
Modelling

Logistic Models

0 of 0 exercises completed

Models of the form ​a+blnx, logarithmic scales and linearizing exponential data.

Want a deeper conceptual understanding? Try our interactive lesson!

Logistic Models
AHL AI 2.9

A logistic model describes growth that appears exponential for smaller values but slows as it approaches a carrying capacity, represented by a horizontal asymptote above the curve.


Logistic models are given by the general equation ​f(x)=1+Ce−kxL​, where ​L​ is the carrying capacity. Logistic models are particularly effective for modelling population growth, as they tend to grow exponentially from small numbers yet have a carrying capacity capped by the scarcity of space, food, and water. ​k​ is often called the intrinsic rate, and it represents the rate of growth of a quantity before it nears carrying capacity. Finally, ​C​ controls the initial population since ​f(0)=1+CL​, where ​f(0)​ is the initial population.

Nice work completing Logistic Models, 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!
/
Modelling
/
Logistic Models
Power Models & Proportionality
Logistic Models
Modelling

Logistic Models

0 of 0 exercises completed

Models of the form ​a+blnx, logarithmic scales and linearizing exponential data.

Want a deeper conceptual understanding? Try our interactive lesson!

Logistic Models
AHL AI 2.9

A logistic model describes growth that appears exponential for smaller values but slows as it approaches a carrying capacity, represented by a horizontal asymptote above the curve.


Logistic models are given by the general equation ​f(x)=1+Ce−kxL​, where ​L​ is the carrying capacity. Logistic models are particularly effective for modelling population growth, as they tend to grow exponentially from small numbers yet have a carrying capacity capped by the scarcity of space, food, and water. ​k​ is often called the intrinsic rate, and it represents the rate of growth of a quantity before it nears carrying capacity. Finally, ​C​ controls the initial population since ​f(0)=1+CL​, where ​f(0)​ is the initial population.

Nice work completing Logistic Models, 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...