Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
1. Intro to Stats and Collecting Data
Intro to Stats
Problem 1.RE.7d
Textbook Question
State Populations Currently, California has the largest population with 39,776,830 residents, and Wyoming has the smallest population with 573,520 residents.
d. If we randomly select 50 full-time workers in each of the 50 states, what type of sample is obtained? (random, systematic, convenience, stratified, cluster)

1
Understand the different types of sampling methods: Random sampling involves selecting individuals purely by chance, systematic sampling involves selecting every nth individual, convenience sampling involves selecting individuals who are easiest to reach, stratified sampling involves dividing the population into subgroups and sampling from each, and cluster sampling involves dividing the population into clusters and randomly selecting entire clusters.
Identify the key characteristics of the sample described in the problem: We are selecting 50 full-time workers from each of the 50 states.
Recognize that the sample involves dividing the population (all full-time workers in the U.S.) into subgroups (the 50 states).
Note that from each subgroup (state), a sample of 50 full-time workers is selected.
Determine that this method of sampling, where the population is divided into subgroups and samples are taken from each subgroup, is known as stratified sampling.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sampling Methods
Sampling methods are techniques used to select a subset of individuals from a population to estimate characteristics of the whole population. Common methods include random, systematic, convenience, stratified, and cluster sampling, each with its own advantages and applications depending on the research goals and population structure.
Recommended video:
Sampling Distribution of Sample Proportion
Stratified Sampling
Stratified sampling involves dividing a population into distinct subgroups, or strata, that share similar characteristics, and then randomly selecting samples from each stratum. This method ensures representation from all subgroups, making it useful when the population has diverse segments that need to be proportionally represented in the sample.
Recommended video:
Sampling Distribution of Sample Proportion
Cluster Sampling
Cluster sampling involves dividing the population into clusters, usually based on geographical or natural groupings, and then randomly selecting entire clusters for study. This method is efficient for large populations spread over wide areas, as it reduces travel and administrative costs by focusing on specific clusters rather than individuals.
Recommended video:
Sampling Distribution of Sample Proportion
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