How do you calculate sample size for time study?
How to Calculate Sample Size
- Determine the population size (if known).
- Determine the confidence interval.
- Determine the confidence level.
- Determine the standard deviation (a standard deviation of 0.5 is a safe choice where the figure is unknown)
- Convert the confidence level into a Z-Score.
What is a good sample size for data analysis?
A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500.
How do you find the sample size of a data set?
How to Find a Sample Size Given a Confidence Level and Width (unknown population standard deviation)
- za/2: Divide the confidence level by two, and look that area up in the z-table: .95 / 2 = 0.475.
- E (margin of error): Divide the given width by 2. 6% / 2.
- : use the given percentage. 41% = 0.41.
- : subtract. from 1.
How to tell the difference between two time series?
I have two sets of time series data (series1 and series2). Each data set has 20 samples for 20 time intervals (one sample per each time interval). I want to see if these two data sets are significantly different.
How to compare two data sets with different sample sizes?
I have two data sets that have different sample sizes and I am unsure on how to compare them. Dataset 1 has 51 participants but dataset 2 has only 20 participants.
How many tires are in a time series?
I have a set of 10 time-series; each time-series consists of data collected from torque value of a tire (so 10 tires in total) and the sets may not contain same number of data points (set size differ). Each time-series data is pretty much just the tire_id, timestamp, and the sig_value (value from the signal, or the sensor).
How to do anomaly detection between time series?
I am trying to do some anomaly detection between time#series using Python and sklearn (but other package suggestions are definitely welcome!). I have a set of 10 time-series; each time-series consists of data collected from torque value of a tire (so 10 tires in total) and the sets may not contain same number of data points (set size differ).