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Restaurant energy use benchmarking guideline
Original publisher: Golden, Colo.: National Renewable Energy Laboratory,  OCLC Number: (OCoLC)744593249 Subject: Restaurants -- Energy consumption -- United States. Excerpt: ... Figure 2-2 Flow chart summary of advanced performance evaluation and prediction 2 In Figure 2-2, it is important to note that the R value does not represent the only...
Original publisher: Golden, Colo.: National Renewable Energy Laboratory,  OCLC Number: (OCoLC)744593249 Subject: Restaurants -- Energy consumption -- United States. Excerpt: ... Figure 2-2 Flow chart summary of advanced performance evaluation and prediction 2 In Figure 2-2, it is important to note that the R value does not represent the only statistical parameter that could be relevant to evaluating the strength of the linear regressions. This statistic is emphasized here because it is more widely known and is included in the example spreadsheet. Distinguishing which correlation fits the operator's needs might be a function of the difference in 2 R values between two regressions instead of the two absolute values. For example, one hypothetical regression might combine the effects of transactions and weekly hours and result in 2 an R value of 38 %. Another regression might add the effects of heating degree day ( HDD ) to 2 those of transactions and weekly hours and result in an R value of 39 %. Including the HDD 2 independent variable increased the R value, and therefore the strength of the regression, but 5
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