Could you produce 1000 data points in R with a normal distribution, defined mean, and variance, analyze the dataset summary, and plot its density? Also, show how to do this in a Python environment on Codility.
How can you simulate 1000 normally distributed data points with mean=x and variance=y in R, summarize the statistics, and create a density plot? Additionally, replicate this task in Python on the Codility editor.
How do you generate a normally distributed dataset of 1000 points with specified mean and variance in R, conduct a summary analysis, and draw a density plot? Also, show how to accomplish this in Python on Codility.
In R, how can you generate a dataset of 1000 normally distributed points with mean=x and variance=y, provide summary statistics, and plot the density? Also, replicate these steps in a Python script within Codility's editor.
What steps would you take to generate 1000 normal distribution data points with mean=x, variance=y in R, summarize the data, and plot the density? Then, how would you achieve the same outcome using Python in the Codility code editor?
What's the procedure for producing 1000 normally distributed samples with mean=x and variance=y in R, conducting a summary statistical analysis, and graphing a density plot? Implement the equivalent in Python via the Codility online editor.
Your main responsibility is to generate 1000 normally distributed data points using R and Python, with a specified mean and variance value. Calculate summary statistics of the dataset and clarify their meaning. Create a density plot of the data in R and Python, as well.