Question

1. how do we know when a sample is representative of a population? 2. did you have any outlier samples? when? 3. why did your confidence increase in your predictive accuracy as you gained more data? 4. what does generalization mean? how can it be applied to a sample taken of waterloo students?

Explanation:

1. A sample is representative when its characteristics match those of the population. This can be achieved through random - sampling and ensuring sufficient sample size.
2. Outlier samples are data points that deviate significantly from the rest. They can be identified using statistical methods like the inter - quartile range. The presence of outliers depends on the data collection process and the nature of the variable.
3. More data reduces sampling error and provides a more comprehensive view of the population, thus increasing confidence in predictive accuracy.
4. Generalization is applying findings from a sample to the larger population. For a sample of university students, it can be applied if the sample is representative in terms of demographics, academic performance, etc.

Answer:

1. A sample is representative when it is randomly selected and has characteristics similar to the population.
2. Outlier samples are identified by their significant deviation from other data points. Their presence depends on the data collection context.
3. More data reduces sampling error and gives a better understanding of the population, increasing predictive confidence.
4. Generalization means applying sample findings to the population. For university students, it's valid if the sample is representative.