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Quickly build an estimate out from start to finish in just 10 minutes. The SharpeSoft Estimator is the most efficient and affordably priced estimating software on the market today.
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Dec 12, 2022 · At SharpeSoft, Inc. we understand the need for men and women in the field to be able to quickly and easily keep track of certain metrics while making impactful decisions on the jobsite. That is why we are always looking to save you time and money by providing top quality service with products like SharpeSoft’s Estimator. Traditional estimating methods that rely on guesswork and spreadsheet ...
- Dylan C
Sep 8, 2023 · The SharpeSoft Estimator is a powerful tool for civil construction professionals looking to streamline their project costs and bidding processes. Designed specifically for the construction industry, this software is an intuitive and user-friendly solution that allows contractors to create accurate and detailed estimates quickly.
- Matthew Caddy
SharpeSoft Estimator is an on-premise & cloud cost estimating software for contractors and sub-contractors in the construction industry. Offered on a best-of-breed basis, SharpeSoft provides integrated tools for labor and equipment costing, contractor management, bid management, proposal generator and more.
Apr 24, 2022 · In this setting we often have a general formula that defines an estimator of \(\theta\) for each sample size \(n\). Technically, this gives a sequence of real-valued estimators of \(\theta\): \( \bs{U} = (U_1, U_2, \ldots) \) where \( U_n \) is a real-valued function of \( \bs{X}_n \) for each \( n \in \N_+ \).
Product Overview. Sharpesoft Estimator is an estimating software with key features like the trench profiler, easy crew and material list building, and comprehensive cost tracking. Its ability to handle different project scales and provide detailed job cost summaries helps maintain accuracy and efficiency in the bidding and estimating processes.
Sep 19, 2023 · The formula for standard deviation is the square root of the sum of squared differences from the mean divided by the size of the data set. For a Population. σ = ∑n i=1(xi − μ)2 n− −−−−−−−−−−−√ σ = ∑ i = 1 n ( x i − μ) 2 n. For a Sample. s = ∑n i=1(xi − x¯¯¯)2 n − 1− −−−−−−−−−− ...
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