After exams I understood my improvement areas in terms of learning. Predominantly these are crucial chapters
- P test using R Programming
- P test using Python Programming
- Hypothesis test using R Programming
- Hypothesis test using Python Programming
I glanced through couple of sites, Bookmarking some of pointers
Normal Distribution Properties
Key Pointers
- Normal distribution unimodal and symmetric
- Mean (Mu)
- Standard Deviation (Sigma)
- 99.7% < 3 Sigma
- 95% < 2 Sigma
- Z > 2 (Unusual)
- pnorm (percentile of observation)
- Qnorm for quantile or cutoff values
Key Pointers
- Creating Null and Alternate Hypothesis conditions
- Identifying sample space, standard error, population mean, standard deviation from input question
- Computing P value
Happy Learning!!!
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