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Visual stats: Curve fitting implemented by adding details

Introduction
->> Visual Stats
->> Main Interface
   
Quick Start Tutorial
->> Functions
->> Operations
->> Parameters
->> Syntax
->> Priority Of Operations And Functions
   
Menus & Toolbar
->> File Menu
->> Edit Menu
->> Format Menu
->> Setting Menu
->> Stats Menu
->> Help Menu
->> Standard Toolbar
   
Stats
->> Probability
->> Descriptive Statistics Summary
->> Frequency Analysis
   
Compare Means
->> One Sample T Test
->> Independent-Samples T Test
->> Paired-Samples T Test
   
Compare Variables
->> One Sample
->> Two Samples
   
Variance Analysis
->> One-Way ANOVA
->> Two-Way ANOVA
   
Regression
->> Univariate Linear Regression
->> Multivariate Linear Regression
->> Curve Fitting
->> Nonlinear Curve Fitting
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Curve Fitting

The Main Steps Of Operation:

To implement curve fitting, following these steps:

1. Input your data in Data Editor.
2. Select Stats -> Regression -> Curve Fitting... from the menus.
3. Select variables and models in Curve Fitting dialogbox, and click OK button.

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Models:

Visual Stats provides ten models. The following table shows the names and functions of these models.

Name
Model
Linear y=a*x+b
Inverse(1) y=a/x+b
Inverse(2) y=1/(a/x+b)
Logarithmic y=a*ln(x)+b
Exponential y=exp(a*x+b)
XY Logarithmic y=exp(a*ln(x)+b)
S y=exp(a/x+b)
X Root y=a*sqr(x)+b
Y Root y=(a*x+b)^2
XY Root y=(a*sqr(x)+b)^2
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