Wondershare EdrawMind
MindMap Gallery statistical model
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statistical model
Data models are applicable to finance, marketing, manufacturing, healthcare, and social sciences. They are based on data analysis and modeling and are used to predict, analyze, and optimize various phenomena and problems in different industries, and to derive unknown data from known data.
Edited at 2023-12-03 11:58:49
- bacteria
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
- Plant asexual reproduction
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
- Reproductive development of animals
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
statistical model
- bacteria
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
- Plant asexual reproduction
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
- Reproductive development of animals
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
- Recommended to you
- Outline
statistical model
Model classification
linear regression model
Basic concepts of linear regression
Definition of linear regression
Assumptions of linear regression
How to solve linear regression
least squares method
gradient descent method
Advantages and Disadvantages of Linear Regression
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
logistic regression model
Basic concepts of logistic regression
Logistic regression definition
Assumptions of logistic regression
Logistic regression solution method
maximum likelihood estimation method
gradient descent method
Advantages and Disadvantages of Logistic Regression
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
Hidden Markov Model
Basic concepts of hidden Markov models
Definition of Hidden Markov Model
Assumptions of Hidden Markov Model
How to solve hidden Markov models
forward algorithm
backward algorithm
Advantages and Disadvantages of Hidden Markov Models
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
Naive Bayes model
Basic concepts of Naive Bayes
Definition of Naive Bayes
Assumptions of Naive Bayes
Naive Bayes solution method
Bayes theorem
maximum likelihood estimation method
Advantages and Disadvantages of Naive Bayes
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
Support vector machine model
Basic concepts of support vector machines
Definition of support vector machine
Assumptions of support vector machines
Support vector machine solution method
maximum margin classifier
kernel function
Advantages and Disadvantages of Support Vector Machines
Advantages: good generalization ability, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
Kmeans clustering model
Basic concepts of Kmeans clustering
Definition of Kmeans clustering
Assumptions of Kmeans clustering
Kmeans clustering solution method
Selection of initial center point
iterative optimization process
Advantages and Disadvantages of Kmeans Clustering
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
Decision tree model
Basic concepts of decision trees
Decision tree definition
Decision tree assumptions
Decision tree solution method
ID3 algorithm
C4.5 algorithm
Advantages and Disadvantages of Decision Trees
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: easy to overfit, sensitive to noise
Gaussian Mixture Model
Basic concepts of Gaussian mixture models
Definition of Gaussian Mixture Model
Assumptions of Gaussian Mixture Model
Solution method of Gaussian mixture model
expectation maximization algorithm
Bayesian estimation method
Advantages and Disadvantages of Gaussian Mixture Models
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data
conditional random field model
Basic concepts of conditional random field models
Definition of conditional random field model
Assumptions of Conditional Random Field Model
Solution method of conditional random field model
gradient descent method
stochastic gradient descent
Advantages and Disadvantages of Conditional Random Field Models
Advantages: simple, easy to understand, high computational efficiency
Disadvantages: Poor fitting effect for nonlinear data;
Applicable industries
Financial sector
risk assessment
credit risk
Default probability prediction
Credit Rating
market risk
Statistical model stock price prediction
Introduction to statistical models
Definition of statistical model
Classification of statistical models
time series model
cross-sectional model
Application of statistical models
Advantages and Disadvantages of Statistical Models
Introduction to Stock Price Forecasting
Stock Price Prediction Definition
Methods of stock price prediction
technical analysis
fundamental analysis
statistical modeling method
Application of stock price prediction
Advantages and Disadvantages of Stock Price Forecasting
Application of statistical models in stock price prediction
Advantages of statistical models in stock price prediction
Accuracy of statistical models
Statistical model flexibility
Disadvantages of statistical models in stock price prediction
Limitations of statistical models
Computational complexity of statistical models
Challenges of statistical models in stock price prediction
Data quality issues
Missing data problem
Data noise problem
Model selection problem
Model selection criteria
Model selection method
Parameter estimation problem
Parameter estimation method
Parameter estimation accuracy
The development trend of statistical models in stock price prediction
Application of deep learning in stock price prediction
Advantages of deep learning
Deep learning challenges
Application of big data in stock price prediction
Advantages of big data
Big data challenges;
interest rate forecast
Portfolio Optimization
Asset allocation
Asset Class
stock
large cap stocks
Small and mid-cap stocks
bond
national debt
corporate debt
money market instruments
cash
short term bonds
real estate
Residential
commercial estate
Commodity
gold
crude
Configure policy
mean variance model
Minimize risk
maximize benefits
risk parity model
Balance risk and reward
Maximum Sharpe Ratio Model
Maximize return to risk ratio
Minimum variance model
Minimize risk
maximum profit model
maximize benefits
Portfolio Optimization
Markowitz portfolio theory
efficient frontier
minimum variance combination
Blackschultz model
European option pricing
capital asset pricing model
market mix
risk premium
arbitrage pricing theory
No arbitrage opportunities
risk neutral pricing
industry analysis
Industry life cycle
Start-up period
growth period
mature stage
Recession
Industry competition landscape
perfect competition
Monopolistic Competition
Oligopoly
complete monopoly
Industry drivers
policy factors
technical factors
market factors
economic factors;
Investment strategy selection
Portfolio Optimization
Portfolio construction
Asset allocation
risk assessment
earnings forecast
portfolio adjustments
Portfolio Assessment
Risk-benefit ratio
portfolio performance
industry analysis
Category
Industry life cycle
Industry competition landscape
Industry trend forecast
macroeconomic environment
Industry policy impact
financial market analysis
market type
stock market
Bond Market
derivatives market
Market trend forecast
market sentiment
market liquidity
Investment strategy selection
Portfolio Optimization
Portfolio construction
Asset allocation
risk assessment
earnings forecast
portfolio adjustments
Portfolio Assessment
Risk-benefit ratio
portfolio performance
industry analysis
Category
Industry life cycle
Industry competition landscape
Industry trend forecast
macroeconomic environment
Industry policy impact
financial market analysis
market type
stock market
Bond Market
derivatives market
Market trend forecast
market sentiment
market liquidity;
Medical industry
disease prediction
disease risk assessment
assessment method
statistical model
linear regression
logistic regression
Support Vector Machines
machine learning
decision tree
random forest
deep learning
Evaluation indicators
accuracy
sensitivity
specificity
ROC curve
Applicable industries
Medical industry
disease prediction
Disease diagnosis
disease treatment
insurance industry
Premium calculation
risk assessment
Financial sector
credit assessment
Risk Management
disease prediction
disease type
Cardiovascular diseases
cancer
diabetes
method of prediction
data collection
Data preprocessing
Model training
Model evaluation
forecast result
disease probability
Disease risk level;
Disease development trend prediction
Applicable industries for statistical models
Medical industry
disease prediction
Disease development trend prediction
disease risk prediction
Disease diagnosis
disease treatment
other industry
Disease development trend prediction method
time series analysis
regression analysis
machine learning
Disease development trend prediction application
public health policy development
disease prevention and control
Medical resource allocation
Personal health management;
Drug Discovery
Drug effectiveness prediction
Drug R&D Background
Long drug development cycle
Requires a lot of time and resources
Drug development success rate is low
Need to improve success rate
Drug effectiveness prediction methods
statistical modeling methods
regression analysis
linear regression
logistic regression
time series analysis
ARIMA model
GARCH model
machine learning methods
Support Vector Machines
random forest
deep learning
Drug effectiveness prediction applications
drug screening
Improve screening efficiency
clinical trial design
Optimize clinical trial plans
Post-marketing surveillance of drugs
Monitor drug efficacy and safety;
Prediction of drug side effects
Education industry
Student performance prediction
Test score prediction
data collection
Collect basic student information
Name, gender, age, etc.
Collect student performance data
Previous test scores, homework scores, etc.
Data preprocessing
Data cleaning
Remove outliers, missing values, etc.
Data normalization
Convert data of different dimensions into a unified dimension
Feature selection
Select features related to test scores
Subject grades, study time, etc.
Feature dimensionality reduction
Dimensionality reduction methods such as PCA and LDA
Model selection
Choose an appropriate statistical model
Linear regression, logistic regression, etc.
Model training
Train the model using training data
Model evaluation
Evaluate model performance using test data
Indicators such as precision rate and recall rate
Model optimization
Adjust model parameters, features, etc. to improve model performance;
Learning progress prediction
Learning progress assessment
Learning progress detection
Learning progress monitoring
Learning progress record
Learning progress analysis
Learning progress prediction
Learning progress adjustment
Learning progress optimization
learning progress plan
Learning progress adjustment
Learning progress tracking
Learning Progress Prediction Model
statistical model
linear regression model
logistic regression model
Decision tree model
Machine learning model
neural network model
Support vector machine model
random forest model
deep learning model
Convolutional neural network model
Recurrent Neural Network Model
Long short-term memory network model;
Teaching strategy optimization
Teaching method selection
Course schedule optimization
Retail industry
Product sales forecast
Product sales forecast
Product sales data collection
Collect historical sales data
Collect product price data
Collect product inventory data
Collect product promotion activity data
Collect product seasonal data
Collect external environmental data
Collect economic environment data
Collect consumer behavior data
Collect competitor data
Data preprocessing
Data cleaning
Missing value handling
Remove missing values
Fill missing values
Outlier handling
Remove outliers
Correct outliers
Data normalization
Normalized
standardization
Data dimensionality reduction
Principal component analysis
factor analysis
Model selection
linear regression model
simple linear regression
multiple linear regression
time series model
moving average method
Exponential smoothing
neural network model
multilayer perceptron
recurrent neural network
Model training
Training data partitioning
Training set
Validation set
test set
Model parameter optimization
gradient descent method
Newton's method
Model evaluation
mean square error
R-squared value
Outcome prediction
Forecast product sales
Forecast future sales
Forecast sales trends
Visualization of prediction results
line chart
histogram;
Commodity price forecast
Inventory management optimization
Inventory level forecast
Inventory forecasting methods
time series analysis
moving average method
Exponential smoothing
ARIMA model
regression analysis
linear regression
Multiple Regression
logistic regression
Neural Networks
multilayer perceptron
recurrent neural network
long short term memory network
Inventory forecasting indicators
inventory turnover
inventory holding cost
Out of stock rate
Inventory accuracy
Inventory forecasting application
Inventory management optimization
Inventory safety level setting
Inventory ordering strategy development
Inventory monitoring and early warning
Retail industry
Product display optimization
Product pricing strategy formulation
Product promotion strategy formulation
other industry
Production plan formulation
Supply chain management optimization
Sales forecasting and analysis.
Inventory turnover optimization;
Inventory turnover ratio definition
Inventory turnover rate refers to the number of times inventory is turned over
Inventory turnover refers to the number of times inventory is sold or consumed within a certain period of time
The higher the inventory turnover rate, the more effective the inventory management is.
The lower the inventory turnover number, the inventory management needs to be improved.
Inventory turnover rate is an important indicator to measure the level of inventory management
Inventory turnover ratio calculation formula
Inventory turnover rate = cost of sales / average inventory amount
Cost of sales refers to the income earned by an enterprise from selling goods or providing services within a certain period of time.
Average inventory amount refers to the average value of goods in stock of a company during a certain period of time
Inventory turnover rate reflects the efficiency of enterprise inventory management
Inventory turnover rate optimization method
Optimize inventory structure
Properly set inventory safety levels
Inventory safety level refers to the amount of inventory set by an enterprise to cope with emergencies or fluctuations in market demand.
Properly setting inventory safety levels can improve inventory turnover rates
Optimize inventory varieties and specifications
Reasonable selection of inventory varieties and specifications can improve inventory turnover rate
Optimize inventory management processes
Strengthen the informatization construction of inventory management
Use information technology to improve inventory management efficiency
Strengthen inventory management training
Improve employee inventory management skills
Optimize inventory management strategies
Implement inventory management strategies
Implement inventory management strategies such as ABC classification and economic lot size
Optimizing inventory management strategies can improve inventory turnover;
Instructions
Understand the basic concepts and principles of statistical models
Learn basic concepts of statistical models, such as parameter estimation, hypothesis testing, etc.
Learn parameter estimation methods, such as maximum likelihood estimation, least squares estimation, etc.
Learn the principles and applications of maximum likelihood estimation
Learn the principles and applications of least squares estimation
Learn hypothesis testing methods, such as t-test, analysis of variance, etc.
Learn the principles and applications of t-test
Learn the principles and applications of analysis of variance
Learn classification and selection methods for statistical models
Learn the classification of statistical models, such as linear models, nonlinear models, etc.
Learn the principles and applications of linear models
Learn the principles and applications of nonlinear models
Learn statistical model selection methods, such as AIC, BIC, etc.
Learn the principles and applications of AIC
Learn the principles and applications of BIC
How to use statistical models
Choose the right model
Select based on data type
Selecting linear models for continuous data
Choosing a logistic regression model for categorical data
Choose based on question type
Choosing a regression model for prediction problems
Classification problem selection classification model
Prepare data
Data collection
Collect raw data
Organize data
Clean data
Missing value handling
Outlier handling
feature engineering
Feature selection
Feature scaling
Training model
selection algorithm
Linear Regression Selected Least Squares Method
Logistic regression selects maximum likelihood estimation
Training model
Train the model using training data
Adjust parameters to optimize the model
Evaluation model
Divide the test set
Divide the data into training set and test set
Evaluation indicators
Regression problems using mean square error
Classification problems use accuracy, recall, F1 value, etc.
application model
Predict new data
Use the trained model to predict new data
Model update
Update model based on new data
Model explanation
Interpret model results
Interpret model predictions
Visual model
Use graphs to display model results;
Master the application skills of statistical models
Learn how to correctly set model parameters
Learn how to set initial values for your model
Learn how to set up optimization methods for your model
Learn how to evaluate model performance
Learn how to calculate a model’s prediction error
Learn how to evaluate a model’s predictive power
Practical statistical models
Utilize statistical software for model building and evaluation
Learn how to use R for model building and evaluation
Learn how to use R language for data preprocessing
Learn how to use R language for model building
Learn how to use R for model evaluation
Learn how to leverage Python for model building and evaluation
Learn how to use Python for data preprocessing
Learn how to use Python for model building
Learn how to use Python for model evaluation;
statistical model