PHPMem v2.0.1

Version
1.6.45
Uptime
7 days 10 hours 41 minutes 42 seconds

Memory

Total
512MB
Used
13,32MB (2.6%)
Free
498,68MB

Keys

Current
10 162
Total (since start)
11 092
Evictions
0
Reclaimed
157
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
3 / 1 024 max
Total
66 227
Rejected
0
llm:b61e94ecf55bfd41618cb60696447e3068de5bc0764e3f1a062e8d5532006cc8
TTL 3 days 8 hours 35 minutes 41 seconds Size 2,27KB Export
Edit
{
    "action": "tool_call",
    "reasoning": "I have correlation between the two numeric columns (Player ID and Birth year: -0.033, very weak). I also have contingency tables showing the structure of categorical relationships. Now I need to compute actual association measures (Cramér's V or similar) for the categorical pairs to quantify their strength. Let me calculate chi-square based associations for the key categorical variable pairs.",
    "tool": "query_sql",
    "purpose": "Calculate association strength between categorical variables using contingency tables and chi-square-based measures",
    "args": {
        "sql": "WITH gender_hand AS (SELECT Gender, \"Playing hand\", COUNT(*) as obs FROM raw.ittf_player_info GROUP BY Gender, \"Playing hand\"), gender_style AS (SELECT Gender, \"Playing style\", COUNT(*) as obs FROM raw.ittf_player_info GROUP BY Gender, \"Playing style\"), gender_grip AS (SELECT Gender, Grip, COUNT(*) as obs FROM raw.ittf_player_info GROUP BY Gender, Grip), activity_hand AS (SELECT Activity, \"Playing hand\", COUNT(*) as obs FROM raw.ittf_player_info GROUP BY Activity, \"Playing hand\"), activity_style AS (SELECT Activity, \"Playing style\", COUNT(*) as obs FROM raw.ittf_player_info GROUP BY Activity, \"Playing style\") SELECT 'Gender × Playing hand' as pair, COUNT(DISTINCT Gender) * COUNT(DISTINCT \"Playing hand\") as expected_cells, COUNT(*) as actual_cells, SUM(obs) as total_obs, MIN(obs) as min_cell, MAX(obs) as max_cell, STDDEV(obs) as stddev_obs FROM gender_hand UNION ALL SELECT 'Gender × Playing style', COUNT(DISTINCT Gender) * COUNT(DISTINCT \"Playing style\"), COUNT(*), SUM(obs), MIN(obs), MAX(obs), STDDEV(obs) FROM gender_style UNION ALL SELECT 'Gender × Grip', COUNT(DISTINCT Gender) * COUNT(DISTINCT Grip), COUNT(*), SUM(obs), MIN(obs), MAX(obs), STDDEV(obs) FROM gender_grip UNION ALL SELECT 'Activity × Playing hand', COUNT(DISTINCT Activity) * COUNT(DISTINCT \"Playing hand\"), COUNT(*), SUM(obs), MIN(obs), MAX(obs), STDDEV(obs) FROM activity_hand UNION ALL SELECT 'Activity × Playing style', COUNT(DISTINCT Activity) * COUNT(DISTINCT \"Playing style\"), COUNT(*), SUM(obs), MIN(obs), MAX(obs), STDDEV(obs) FROM activity_style",
        "source": "raw"
    }
}