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Frequently Asked Questions

  1. Are there any tutorial videos?
  2. What is "Application Status"?
  3. What is "Date Range"?
  4. What types of CSI data are available on California Solar Statistics?
  5. How do I use the graphs?
  6. How often is the data updated?
  7. Which application stage transitions are used for calculations in the Data Annex?
  8. What is the 95th Percentile Methodology?


Are there any tutorial videos?

Navigating the interactive graphics on this site is easy once you get the hang of it. We have made a few tutorial videos to make it even easier!



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What is "Application Status"?

Canceled projects are in any stage with a status of Canceled. Installed projects are in the Incentive stage with status In Payment, or Paid. Pending projects are those that do not fit into either of the other statuses.
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What is "Date Range"?

The date range applies to the first time a project entered Application Review, which is when the rebate application was first submitted to the Program Administrators.
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What types of CSI data are available on California Solar Statistics?

When contractors or homeowners apply for incentives with the CSI Thermal program, they provide data about their residence or business and about the solar thermal system they are installing. California Solar Thermal Statistics analyzes selected data expected to be of greatest interest to the public.
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How do I use the graphs?

CSI Thermal Statistics uses Tableau® to power the visualizations and tables on the site. For more instruction, please view the Tableau Quick Start Guide.
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How often is the data updated?

The data is updated every other week.
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Which application stage transitions are used for calculations in the data annex?

Application to Reservation

This statistic looks at the amount of time from Reservation: Application Review to Reservation: Approved for each application. It measures the amount of time it took for each two-step application to be approved for reservation funds after it was submitted to the system. This metric does not include one-step applications.

Incentive Approval

This statistic looks at the amount of time from Incentive: Application Review to Incentive: Approved for each application. It measures the amount of time it took for each application to be approved for incentive funds after it was submitted to the PAs. This is in the second phase for two-step applications and the final phase for one-step applications.

Payment

This statistic looks at the time between Incentive Approval and the date of first payment for each application. It measures the amount of time it took for the first (or only) check to be cut after the incentive was approved. This is in the second phase for two-step applications and the final phase for one-step applications. This metric relies on data from the Full Export and cannot be replicated with data from the Public Export.

Project Completion

This statistic looks at the time between Reservation: Approved and Incentive: Approved. It represents the amount of time it took to complete the actual thermal hot water project for two-step applications. This metric does not include one-step applications.
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What is the 95th Percentile Methodology?

Percentiles are used to compare one value in a data set to all other values; they give a good idea of how one value compares to the rest of a set.

For example, if 10 days is the 95th percentile of application processing times, only 5% of applications take 10 days or more to process.

To calculate a percentile, the data are first arranged in numerical order and given a rank between 1 and N, where N is the number of total data points in the set.

The percentile being calculated is then given a rank to match a data point in the set. The following equation is used to calculate the rank of a given percentile, where n is the rank given to the Pth percentile, out of a list of N data points.

n = P/100 x N +0.5

n is rounded to the nearest integer

For example, for a set of 1000 data points, the rank of the 95th percentile is 951. The 95th percentile would correspond with the 951st data point. It can be concluded for this example that, at most, 5% of the set is greater than the 951st value.
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