Remove 2007 Remove Measurement Remove Statistics
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Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

This: You understand all the environmental variables currently in play, you carefully choose more than one group of "like type" subjects, you expose them to a different mix of media, measure differences in outcomes, prove / disprove your hypothesis (DO FACEBOOK NOW!!!), Measuring Incrementality: Controlled Experiments to the Rescue!

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

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Time Series with R

Domino Data Lab

We see it when working with log data, financial data, transactional data, and when measuring anything in a real engineering system. A big part of statistics, particularly for financial and econometric data, is analyzing time series, data that are autocorrelated over time. 2007-01-04 34.50 2007-01-05 33.96 2007-01-08 33.81

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A Planning Center of Excellence Delivers Performance Improvement

David Menninger's Analyst Perspectives

The difference is in using advanced modeling and data management to make faster scenario planning possible, driven by actionable key performance measures that enable faster, well-informed decision cycles. Predictive analytics applies machine learning to statistical modeling and historical data to make predictions about future outcomes.

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The Gold Standard – The Key to Information Extraction and Data Quality Control

Ontotext

Originally, the Gold Standard was a monetary system that required countries to fix the value of their currencies to a certain amount of gold, aiming to replace the unreliable human control with a fixed measurement that could be used by everyone. Simply put, we need to be able to measure and evaluate our results against clearly set criteria.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. Identification We now discuss formally the statistical problem of causal inference. we drop the $i$ index.