Basics of ML & AL Week 2 Challenge

2)

The applicablity of simple mean and expected value are dependent on the nature of the case study.

In smaller case studies where the random variable outcomes can be measured as frequency of occurences or each observation in the sample can be noted, the simple mean is calculated as sum of all observations/number of observations.

However, in a case of an infinite number of observations, it is very difficult to note each and every outcome of the given data set. In such cases, the probability of each outcome is determined using a sample study and using this probability the expected value is determined using:

Mean or expected value, μ=∑x.P(x)μ=∑x.P(x)

The expected values reduces the analysis expenditure and serves the purpose of the measuring the central tendency of the data set

3)

Having skewness is neither good nor bad for the analysis. For some analysis having skewness might be good. while for some analysis might not be good.

Skewness is a measurement of the distortion of symmetrical distribution or asymmetry in a data set.

For a normal distribution, the mean, median and mode coincide. However, this is an ideal case and most of the real data have either postive or negative skewness.

A positive skewness indicates that the median and mode are greater than the mean value. On the other hand, for a negatve skewness, the mean value is higher than the median and mode.

5)

It is a normal distribution curve. The peak of the curve divides the curve and the area of the curve is symmetric about the y-axis with the mean or expected value located at the origin. The right side of the curve is divided using the standard deviation as follows:

The area under the curve between the mean and 1 standard deviation is 34.1% of the total area. This means that 34.1% of the outcomes lie in this region.

The area under the curve between the mean and two standard deviation is 47.7% of the total area. This means that 47.7% of the outcomes lie in this region.