Discover the HUADA ZMDM-15S-A Connector: Compact and Reliable

Compact and Reliable: The HUADA ZMDM-15S-A micro rectangular connector combines compact design with high reliability. Its smaller dimensions—2.78mm shorter in length, 5.4mm narrower, and 3.9mm lower than the J29A series—make it ideal for space-constrained applications. Whether in industrial machinery, aerospace, or telecommunications, this connector delivers exceptional performance. With a maximum operating temperature of +125°C, low contact resistance (8 mΩ), and a mechanical life of 500 cycles, it’s a robust choice for demanding environments.
Versatile Applications: The ZMDM-15S-A is more than just compact; it’s a versatile solution. Capable of handling up to 5 A, it suits various scenarios. From high-tech machinery to critical systems, this connector ensures reliable electrical connections. Its pressure resistance (rated at 800V) and excellent insulation properties (5000 mΩ) further enhance its appeal. Explore the HUADA ZMDM-15S-A micro connector for efficient, space-saving connectivity!

Key Features of the HUADA ZMDM-15S-A Connector

Definition and Purpose: A z-score serves as a standardized measure that quantifies how far a specific data point deviates from the mean of a reference population.
Its primary purpose is to facilitate comparisons across different variables and distributions.
Calculation Formula: To compute the z-score for a given data point (X), apply the following formula: [ Z = frac{{X - mu}}{{sigma}} ]
(mu) denotes the mean of the population (or sample mean if population values are unknown).
(sigma) signifies the standard deviation of the population (or sample standard deviation).
Interpretation: A positive z-score indicates that the data point lies above the mean.
Conversely, a negative z-score suggests that the data point falls below the mean.
Use Cases and Insights: Distribution Position: Z-scores help us understand where a specific data point fits within a distribution.
Cross-Variable Comparison: By standardizing data, we can compare observations across different variables.
Outlier Detection: Unusually high or low z-scores may highlight outliers.
Probability Calculations: Z-scores relate to probabilities and percentiles using the standard normal distribution.

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