Basics of ML & AL Week 2 Challenge
- In file.
- A practical approach results in frequency distribution and a mean value; a theoretical approach results in a probability distribution and an expected value. According to the Law of Large Numbers states, as the number of trials in a probability experiment increases, the difference between the theoretical probability of an event and the relative frequency approaches zero.
- The rule of thumb says that:
- If the skewness is between -0.5 and 0.5, the data are fairly symmetrical.
- If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed.
- If the skewness is less than -1 or greater than 1, the data are highly skewed.
- A) 84.1% B) 42.464% C) 89.653% D)13.5%
- If the data values in a normal distribution are converted to standard score (z-score) in a standard normal distribution the empirical rule describes the percentage of the data that fall within specific numbers of standard deviations (σ) from the mean (μ) for bell-shaped curves. The empirical rule allows researchers to calculate the probability of randomly obtaining a score from a normal distribution. The empirical rule allows us to calculate the probability of randomly obtaining a score from a normal distribution. 68.2% of data falls within the first standard deviation from the mean. This means there is a 68% probability of randomly selecting a score between -1 and +1 standard deviations from the mean. 95.4% of the values fall within two standard deviations from the mean. This means there is a 95% probability of randomly selecting a score between -2 and +2 standard deviations from the mean. 99.7% of data will fall within three standard deviations from the mean. This means there is a 99.7% probability of randomly selecting a score between -3 and +3 standard deviations from the mean.