曙海教学优势
曙海培训的课程培养了大批受企业欢迎的工程师。大批企业和曙海
建立了良好的合作关系。曙海培训的课程在业内有着响亮的知名度。
本课程，秉承二十一年积累的教学品质，以项目实现为导向,老师将会与您分享设计的全流程以及工具的综合使用经验、技巧。
此课程提供使用MATLAB和Statistics Toolbox™执行统计数据分析的实践经验。课程中的示例和练习演示如何在整个分析过程中使用适当的产品功能，从导入和组织数据，到探索性分析，验证性分析和仿真。内容包括：
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Importing and Organizing Data |
Objective: Bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data. · Importing data · Data types · Tables of data · Merging data · Categorical data · Missing data |
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Exploring Data |
Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics. · Plotting · Central tendency · Spread · Shape · Correlations · Grouped data |
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Distributions |
Objective: Investigate different probability distributions and fit distributions to a data set. · Probability distributions · Distribution parameters · Comparing and fitting distributions · Nonparametric fitting |
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Hypothesis Tests |
Objective: Determine how likely an assertion about a data set is. Apply hypothesis tests for common uses, such as comparing two distributions and determining confidence intervals for a sample mean. · Hypothesis tests · Tests for normal distributions · Tests for nonnormal distributions |
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Analysis of Variance |
Objective: Compare the sample means of multiple groups and find statistically significant differences between groups. · Multiple comparisons · One-way ANOVA · N-way ANOVA · MANOVA · Nonnormal ANOVA · Categorical correlations |
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Regression |
Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Explore techniques for improving model quality. · Linear regression models · Fitting linear models to data · Evaluating the fit · Adjusting the model · Logistic and generalized linear regression · Nonlinear regression |
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Working with Multiple Dimensions |
Objective: Simplify high-dimensional data sets by reducing the dimensionality. · Feature transformation · Feature selection |
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Random Numbers and Simulation |
Objective: Use random numbers to evaluate the uncertainty or sensitivity of a model, or perform simulations. Generate random numbers from various distributions, and manage the MATLAB random number generation algorithms. · Bootstrapping and simulation · Generating numbers from standard distributions · Generating numbers from arbitrary distributions · Controlling the random number stream |
