The trend platform allows you to create sensors which show the trend ofnumeric state orstate_attributes from other entities. This sensor requiresat least two updates of the underlying sensor to establish a trend.Thus it can take some time to show an accurate state. It can be usefulas part of automations, where you want to base an action on a trend.
Each time the state changes, a new sample is stored along with the sample time. Samples older than sample_duration seconds will be discarded. The max_samples parameter must be large enough to store sensor updates over the requested duration. If you want to trend over two hours and your sensor updates every 120s then max_samples must be at least 60, i.e., 7200/120 = 60.
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A trend line is then fitted to the available samples, and the gradient of thisline is compared to min_gradient to determine the state of the trend sensor.The gradient is measured in sensor units per second - so if you want to knowwhen the temperature is falling by 2 degrees per hour,use a gradient of (-2) / (60 x 60) = -0.00055
Students who took the 2023 long-term trend reading and mathematics assessments were asked how many days of school they had missed in the last month. Responses to the survey question for both subjects indicate a decrease in the percentages of 13-year-old students reporting having missed none to 2 days in the past month compared to 2020. Conversely, there were increases in the percentages of 13-year-old students who reported missing 3 or 4 days and students who reported missing 5 or more days in the last month. The percentage of students who reported missing 5 or more days doubled from 5 percent in 2020 to 10 percent in 2023.
Since the 1970s, the NAEP long-term trend assessments have been administered to monitor the academic performance of students across three age levels (9-, 13-, and 17-year-old students). This report mainly focuses on the comparison of age 13 students (typically in grade 8) between 2020 and 2023. A report card summarizing results for 9- and 13-year-old students across all administrations back to the 1970s is forthcoming.
The instruments and methodologies of LTT and main NAEP assessment programs are different; therefore, direct comparisons between the LTT results presented in this report and the assessment results presented in other main NAEP reports are not possible. Read more about the differences between long-term trend and main NAEP assessments.
The long-term trend (LTT) assessment in reading requires age 13 students to read a variety of short texts (expository pieces, poems, riddles, advertisements, and story excerpts) and to respond to questions about what they read. Students participating in the 2023 LTT reading assessment read passages and responded to questions in three 15-minute sections. Each section contained three or four short passages and approximately 10 questions. The majority of questions were presented in a multiple-choice format, though most sections included one constructed-response question.
NOTE: The NAEP long-term trend (LTT) reading and mathematics scales range from 0 to 500. Because the scales were developed separately for each subject, comparisons cannot be made from one subject to another. Black includes African American. Hispanic includes Latino. Race categories exclude Hispanic origin. The information about National School Lunch Program (NSLP) variable is based on available school records. If school records were not available, the student was classified as "Information not available." The category "students with disabilities" includes students identified as having either an Individualized Education Program (IEP) or protection under Section 504 of the Rehabilitation Act of 1973. The results for students with disabilities and English learners are based on students who were assessed and cannot be generalized to the total population of such students. See more information about student group variables. Detail may not sum to totals because of rounding or the omission of categories. Although the estimates (e.g., average scores or percentages) are shown as rounded numbers in the charts, the positions of the data points in the graphics are based on the unrounded numbers. Unrounded numbers were used for calculating the differences between the estimates, and for the statistical comparison test when the estimates were compared to each other. Not all apparent differences between estimates are statistically significant. NAEP reports results using widely accepted statistical standards; findings are reported based on a statistical significance level set at .05, with appropriate adjustments for multiple comparisons. Only those differences that are found to be statistically significant are referred to as "higher" or "lower."
The reports listed below have been issued pursuant to section 6206 of the Anti-Money Laundering Act of 2020 (AMLA), which requires FinCEN to periodically publish threat pattern and trend information derived from Bank Secrecy Act (BSA) filings. These reports highlight the value of information filed by financial institutions in accordance with the Bank Secrecy Act.
The Enrollment Trends Snapshot is produced by the Medicaid and CHIP Learning Collaborative (MACLC), using CMS PI data. CMS began releasing this new data product in September 2020 to provide a retrospective view of the past 12 months of Medicaid and CHIP enrollment up to the most recent monthly enrollment report and compares enrollment to various factors. The complete dataset is available on Data.Medicaid.gov. This tool will help to easily track trends in Medicaid and CHIP program enrollment and also look at the potential drivers of increases in enrollment.
The TREND function returns values along a linear trend. It fits a straight line (using the method of least squares) to the array's known_y's and known_x's. TREND returns the y-values along that line for the array of new_x's that you specify.
Linear trend estimation is a statistical method that is used to analyze data patterns. When a series of measurements of a process are treated as a sequence or time series, trend estimation can be used to make and justify statements about tendencies in the data by relating the measurements to the times at which they occurred. This model can then be used to describe the behavior of the observed data.
In particular, it is useful to determine if measurements exhibit an increasing or decreasing trend which is statistically distinguished from random behavior. Some examples are determining the trend of the daily average temperatures at a given location from winter to summer, and determining the trend in a global temperature series over the last 100 years. In the latter case, issues of homogeneity are important (for example, about whether the series is equally reliable throughout its length).
The distribution of trends was calculated by simulation in the above discussion. In simple cases, such as normally distributed random noise, the distribution of trends can be calculated exactly without simulation.
where a {\displaystyle a} and b {\displaystyle b} are unknown constants and the e {\displaystyle e} 's are randomly distributed errors. If one can reject the null hypothesis that the errors are non-stationary, then the non-stationary series {yt } is called trend-stationary. The least squares method assumes the errors to be independently distributed with a normal distribution. If this is not the case, hypothesis tests about the unknown parameters a and b may be inaccurate. It is simplest if the e {\displaystyle e} 's all have the same distribution, but if not (if some have higher variance, meaning that those data points are effectively less certain) then this can be taken into account during the least squares fitting, by weighting each point by the inverse of the variance of that point.
In most cases, where only a single time series exists to be analyzed, the variance of the e {\displaystyle e} 's is estimated by fitting a trend to obtain the estimated parameter values a ^ {\displaystyle {\hat {a}}} and b ^ , {\displaystyle {\hat {b}},} thus allowing the predicted values
Once we know the "noise" of the series, we can then assess the significance of the trend by making the null hypothesis that the trend, a {\displaystyle a} , is not different from 0. From the above discussion of trends in random data with known variance, we know the distribution of calculated trends to be expected from random (trendless) data. If the estimated trend, a ^ {\displaystyle {\hat {a}}} , is larger than the critical value for a certain significance level, then the estimated trend is deemed significantly different from zero at that significance level, and the null hypothesis of zero underlying trend is rejected.
The use of a linear trend line has been the subject of criticism, leading to a search for alternative approaches to avoid its use in model estimation. One of the alternative approaches involves unit root tests and the cointegration technique in econometric studies.
The estimated coefficient associated with a linear trend variable such as time is interpreted as a measure of the impact of a number of unknown or known but unmeasurable factors on the dependent variable over one unit of time. Strictly speaking, that interpretation is applicable for the estimation time frame only. Outside that time frame, one does not know how those unmeasurable factors behave both qualitatively and quantitatively. Furthermore, the linearity of the time trend poses many questions:
3. The inclusion of a linear time trend in a model precludes by assumption the presence of fluctuations in the tendencies of the dependent variable over time; is this necessarily valid in a particular context?
Research results of mathematicians, statisticians, econometricians, and economists have been published in response to those questions. For example, detailed notes on the meaning of linear time trends in regression model are given in Cameron (2005);[1] Granger, Engle and many other econometricians have written on stationarity, unit root testing, co-integration and related issues (a summary of some of the works in this area can be found in an information paper[2] by the Royal Swedish Academy of Sciences (2003); and Ho-Trieu & Tucker (1990) have written on logarithmic time trends with results indicating linear time trends are special cases of cycles. 17dc91bb1f
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