I've read that when considering well distributed variables, median and mean tend to be similar, but can't figure out why mathematically this is the case.
I have a set of textual datasets that have the following average and variance tokens lengths: Dataset1 avg = 28.18, var = 393.03 Dataset2 avg = 32.70, var = 644.79 Dataset3 avg = 36.94, var = 805.50 Dataset4 avg = 28.56, var = 436.86 Dataset5 avg = 53.13, var = 612.18 How can I sample a smaller set of instances from Dataset5 that is similar (or equal if possible) in terms of avg and var to any of the above …
I have some data which you can group based on different variables. I know how to test if they have significantly different means. But what the deviation inside the samples?
I am trying to understand a textbook exercise I am doing. I have an array of data force_b = array([0.172, 0.142, 0.037, 0.453, 0.355, 0.022, 0.502, 0.273, 0.72 ,0.582, 0.198, 0.198, 0.597, 0.516, 0.815, 0.402, 0.605, 0.711, 0.614, 0.468]) with the mean = 0.4191000000000001 I have another mean of 0.55 and I have to shift the data of the array above so that I get an array with the mean of 0.55 The solution in the exercise is translated_force_b = …
In an attempt to find the mean number of hours his tutorial classmates spent per day preparing for tutorials, John collected data from 10 of his friends in the tutorial group and found that the mean is 2.4 hours with a standard deviation of 0.8 hours. However, a day later he felt that the sample size is too small. So he collected data from another 5 of his friends and found that the mean is 2.0 hours with a standard …
I have played around with logistic regression a little using movement data intervals that are prelabeled as either resting or active. I now found that if I divide the mean movement of the individual intervals by the intervals standard deviation, the outcome is quite a good predictor of whether the interval is a resting interval or or active, with an average auc = 0.93 in a 20 fold cross validation. Does someone have an idea of what I have created …
I have a simple question. Please see the below screenshot : It is from a midterm exam from a university : https://cedar.buffalo.edu/~srihari/CSE555/exams/midterm-solution-2006.pdf My questions is how the means are postive ? I am asking because the class samples are all negative so I would expect that the mean is also negative ?
How can I find the mean for each of the channels (RGB) across an array of images? For example train_dataset[0]['image'].shape is (600, 800, 3) and len(train_dataset) is 720 meaning it includes 720 images of dimension 600x800 and 3 channels. train_dataset[0]['image'] is an ndarray. I am looking to end up with 3 numbers each representing the mean for each of the channels across all these 720 images. I have this very dumb solution but I wonder if there's a better solution? …
In order to establish an overall rating for a product from a series of user ratings (from 1 to 5), I thought that the median would be a good idea so that extreme values would not have too much influence. But in doing so, it is hard to rank products since they will all have a whole ranking. So I thought about averaging the mean and the median. Is this a known measure? Is it relevant in this case?
I need to find a probability distribution to fit my data. My data has two important features, duration and activity count. Duration means how long one sequence lasts and activity count means the number of activities in one sequence. I want to draw a curve, which should be (but not definitely necessary) like normal distribution. The height of the peak is related to the activity count. The breadth of the peak (confidence area) is related to the duration. In my …