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Course Name:SMMD
Assignment Title:Assignment 1 (
Submitted by: Garima Agrawal (Section D)
(Student name or group name)
Group Member Name| PG ID|
Garima Agrawal| 61410506|

The data for home values has a considerable wide range (429578) as compared to the inter-quartile range (93522). This means the data has a huge spread and the same can be verified from coefficient of variation which is even more than 41%. Besides, as can be seen from graphical plot and the positive skewness (0.87) measure, the data is skewed towards right. Also, the outliers present towards the right end indicate the presence of few extremely high valued houses, due to which average price of houses is higher than the median price. The highest density of data is present in two lower quartiles, as can be seen from box plot. This shows that low valued houses are present in bulk, and thus must available in the market easily. | | Question 2:

Though normal distribution model is not an absolutely apt for the data set of prices, the data can still be analyzed by assuming normality owing to the fact that data points hover around the diagonal line of normal Quantile plot. Some data points also cross the permissible range, but the density of data (high in the middle, and low at the ends ) allows for the usage of normal distribution model.The same can be verified from the measure of Kurtosis (0.7) which is well in permissible range for usage of normal distribution model.| |

Question 3:

A.Z1 (@ x as 92.8K) = (92.8 – 164)/68 = -1.04Z2 (@ x as 255.5K) = (255.5 – 164)/68 = 1.34P(Z1 < Z < Z2) = 0.9099 – 0.1492 = 0.7607Percentage probability is 76.07, which seems to be more than the actual value, basis what can be seen via boxplot.| B.Z1 (@ x as 232K) = (232 – 164)/68 = 1P( Z < Z1) = 0.8413Percentage probability is 84.13, which is consistent with what can be seen via...
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