B9DA101: Statistics for Data Analytics

Guideline:

• This CA assesses students on core concept in descriptive analytics, discrete and continuous probability models and hypotheses tests.
• Submission of a shiny app including UI and Server and URL on shinyapp.io is mandatory.
• Any submission after deadline will not be considered and scored

• Item 1

  Tab a:  Describe the dataset using appropriate plots/curves/charts,…   (7)

  Tab b: Consider one of continuous attributes, and compute central and variational measures.

  Tab c:  For a particular variable of the dataset, use Chebyshev’s rule, and propose one-sigma interval. Based on your proposed interval, specify the outliers if any.

  Tab d: Explain how the box-plot technique can be used to detect outliers. Apply this technique for one attribute of the dataset.

• Item 2:

  Tab a:  Select four variables of the dataset, and propose an appropriate probability model to quantify uncertainty of each variable.

  Tab b:  For each model in part (a), estimate the parameters of model.

  Tab c: Express the way in which each model can be used for the predictive analytics, then find the prediction for each attribute.

  Item 3:

  Tab a:  Consider two categorical variables of the dataset, develop a binary decision making strategy to check whether two variables are independent at the significant level alpha=0.01.  To do so,

  1. State the hypotheses.
  2. Find the statistic and critical values.
  3. Explain your decision and Interpret results.

  Tab b: Consider one categorical variable, apply goodness of fit test to evaluate whether a candidate set of probabilities can be appropriate to quantify the uncertainty of class frequency at the significant level alpha=0.05.

  Tab c: Consider one continuous variable in the dataset, and apply test of mean for a proposed candidate of  at the significant level alpha=0.05.