Monitoring the network performance experienced by the end user is crucial for managers of wireless networks as it can enable them to remotely modify the network parameters to improve the end user experience. Unfortunately, for performance monitoring, managers are typically limited to the logs of the Access Points (APs) that they manage. This information does not directly capture factors that can hinder station (STA) side transmissions. While the AP-observable measurements do indeed help to characterize the PHY performance for downlink and uplink, managers today lack models and tools to translate them into user experience metrics (such as for instance TCP throughput). Consequently, state-of-the-art methods to measure such metrics primarily involve active measurements. For instance, typically to measure achievable download and upload TCP throughputs, users use internet speed tests which perform 10s of MB of TCP uploads and downloads. Unfortunately, such active measurements increase traffic load and if used regularly and for all the STAs can potentially disrupt user traffic, thereby worsening performance for other users in the network and draining the battery of mobile devices. This thesis enables a passive AP-side network analytics. Therefore, for performance monitoring, I consider that a monitoring framework will have access only to the logs of the AP that the manager controls. Further, I consider that there is no STA side co-operation and no access to STA side logs. As a result, the framework is constrained to make an estimate solely based on passive AP-side observables. In the first part of the thesis, I present virtual speed test, a measurement based framework that enables an AP to estimate speed test results for any of its associated clients solely based on AP-side observables. Virtual speed test employs a novel L2 edge TCP model to perform throughput estimation. We implemented virtual speed test using commodity hardware, deployed it in office and residential environments, and conducted measurements spanning multiple days having different network loads and channel conditions. Overall, virtual speed test has mean estimation error less than 10% compared to ground truth speed tests, yet with zero overhead, and outcomes available at the AP. Next, I present Uplink Latency Microscope (uScope), an AP-side framework for estimation of WLAN uplink latency for any of the associated STAs and decomposition into its constituent components. Similar to virtual speed test, uScope makes estimations solely based on passive AP-side observations. The key idea in uScope is to leverage the layer-4 handshake as a virtual probe to estimate and decompose layer-2 latency. We implement uScope on a commodity hardware platform and conduct extensive field trials on a university campus and in a residential apartment complex. In over 1 million tests, uScope demonstrates high estimation accuracy with mean estimation errors under 10% for all the estimated parameters.