Basics of ML & AL Week 2 Challenge

Answer 1:

Bussiness moments for the given data are :

• Mean value of data which gives us measure of central tendancy.
• Variance which gives us measure of dispersion.
• Skewness is a measure of the symmetry in distribution.
• Kurtosis is a symmetric normal distribution where most data falls near average.

Please find attached Screen-shot with values of each of them:

Answer 2:

• Expected value is used when we want to calculate the mean of a probability distribution. This represents the average value we expect to occur before collecting any data.
• Mean is used when we want to calculate the average value of a given sample. This represents the average value of raw data that we already collected.

Answer 3:

Normal distribution gives us an ideal scenerio. Now how much the distribution deviates from this distribution is what is skewness. The significant negative skewness of a distribution may not be suitable for deep statistical analysis. The high skewness of the data may lead to misleading results from the statistical tests. Due to this reason, the data goes through a transformation process to make it close to the normal distribution.

Answer 4:

`mu` = 680

`sigma`= 31

a) P(x<711) = Area under the curse from left upto 1`sigma` because 711 = `sigma` + `mu` = 0.5(Upto half of the curve) + 0.34(for 1`sigma`) = 0.84

b) `Z = (X-mu)/sigma` = `(740-680)/31 = 1.94`

P(x>740) = P(Z<1.94) = 0.97381

c) Not visible in the question

d) `Z(720) = (720-680)/31 = 1.29`

P(Z = 1.29) = 1 - P(Z<1.29) = 1 - 0.90147 - (1-0.90147) = 0

Answer 5:

This is a normal distribution which is symmetrical about y-axis. Area between `mu and sigma` is 34.1% of total area. While area between `mu + 1sigma and mu + 2sigma` is 13.6% of total area.