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SPSS 论文分析的统计执行工具组。为 `.sav`、`csv/xlsx` 等数据分析场景声明 MCP 统计后端接口，包括数据集检查、统计分析执行、结果解释与论文表格导出。适用于需要把统计执行能力按需注入到 DeepStudent 会话中的场景。
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| name | statistics-tools |
| description | SPSS 论文分析的统计执行工具组。为 `.sav`、`csv/xlsx` 等数据分析场景声明 MCP 统计后端接口，包括数据集检查、统计分析执行、结果解释与论文表格导出。适用于需要把统计执行能力按需注入到 DeepStudent 会话中的场景。 |
| version | 1.0.0 |
| author | Deep Student Project |
| skill-type | standalone |
| embedded-tools | [{"name":"mcp_stats_inspect_dataset","description":"检查统计数据集的变量信息、标签、变量类型、缺失情况和可分析性摘要。适用于 `.sav`、`csv`、`xlsx` 等数据资源的预检查阶段，用于在正式分析前确认变量结构并发现缺失或编码问题。","inputSchema":{"type":"object","properties":{"resource_id":{"type":"string","description":"必填，待检查数据资源的 ID。"},"format_hint":{"type":"string","description":"可选，资源格式提示，如 sav、csv、xlsx。"}},"required":["resource_id"]}},{"name":"mcp_stats_run_analysis","description":"基于指定资源和变量映射运行统计分析。必须返回包含 `analysis_type`、`variables`、`assumption_checks`、`statistics`、`p_value`、`effect_size`（如适用）、`warnings`、`narrative_summary` 的结果对象，供后续论文解释和结果写作使用。调用前必须先确认变量角色与分析目标，禁止在未知变量含义时直接执行。`analysis_type` 仅支持 descriptive、reliability、correlation、independent_t_test、paired_t_test、one_way_anova、chi_square、linear_regression。","inputSchema":{"type":"object","properties":{"resource_id":{"type":"string","description":"必填，待分析数据资源的 ID。"},"analysis_type":{"type":"string","description":"必填，统计分析类型。","enum":["descriptive","reliability","correlation","independent_t_test","paired_t_test","one_way_anova","chi_square","linear_regression"]},"variables":{"type":"object","description":"必填，分析所需变量映射，如 dependent、independent、group、predictors 等。"},"options":{"type":"object","description":"可选，前提检查、缺失值处理、置信区间、效应量等执行选项。"}},"required":["resource_id","analysis_type","variables"]}},{"name":"mcp_stats_explain_result","description":"将统计分析结果转换为面向用户或论文写作的解释文本。输入应来自 `mcp_stats_run_analysis` 的结果对象，输出应包含通俗解释、学术表述和论文方法/结果写法草稿。","inputSchema":{"type":"object","properties":{"analysis_result":{"type":"object","description":"必填，统计分析结果对象。"},"audience":{"type":"string","description":"可选，解释面向对象，如 student、researcher、reviewer。"},"style":{"type":"string","description":"可选，解释风格，如 plain_language、paper_results、paper_methods。"}},"required":["analysis_result"]}},{"name":"mcp_stats_export_tables","description":"将统计分析结果转换为适合论文或报告引用的结构化表格数据。适用于需要导出 APA 风格结果表、描述统计表、回归表或附录表的场景。","inputSchema":{"type":"object","properties":{"analysis_result":{"type":"object","description":"必填，统计分析结果对象。"},"format":{"type":"string","description":"必填，导出格式，如 markdown、json、apa_table。"}},"required":["analysis_result","format"]}}] |
这是 SPSS 论文分析工作流的统计执行工具组。
mcp_stats_run_analysis 之前，先明确研究问题和变量角色。mcp_stats_run_analysis 的返回结果应统一包含：
analysis_typevariablesassumption_checksstatisticsp_valueeffect_size（如适用）warningsnarrative_summary调用方应依赖这个统一结构完成结果解释、论文写作和表格导出。
Run real, UI-driven Deep Student cloud sync E2E tests. Use when Codex needs to validate WebDAV/cloud sync, backup, restore, credentials, conflict behavior, multi-device synchronization, or sync regressions through real Tauri UI operations followed by SQLite/WebDAV/log verification.
Manage Deep Student Tauri desktop test instances through the repo-local tauri-lab CLI. Use when Codex needs to create, start, stop, restart, lease, inspect, or clean up one or many Deep Student Tauri app windows for Computer Use testing, parallel subagents, cloud sync simulations, or local E2E debugging.
面向论文与课题写作的 SPSS 统计分析工作流。适用于 `.sav`、`csv/xlsx`、SPSS 导出结果表或截图解读场景，帮助用户先确认研究问题、变量角色、分组方式与前提条件，再组织统计执行、结果解释和论文写作输出。重点覆盖描述统计、信度、相关、t 检验、单因素方差分析、卡方和线性回归，并在信息不全时优先追问，避免模型凭列名猜测变量含义。