Which of the following is a requirement for a probability distribution?
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
4. Probability
Basic Concepts of Probability
Struggling with Statistics?
Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
Which of the following scenarios fits the condition of a process?
A
Flipping a fair coin times and recording whether each flip is heads or tails
B
Measuring the exact height of each student in a class
C
Selecting a card from a deck, not replacing it, and repeating the process
D
Rolling a die and recording the number that appears each time
Verified step by step guidance1
Understand the definition of a Bernoulli process: it is a sequence of independent trials where each trial has exactly two possible outcomes (often called 'success' and 'failure'), and the probability of success remains constant across trials.
Analyze the first scenario: flipping a fair coin 10 times. Each flip has two outcomes (heads or tails), the flips are independent, and the probability of heads (success) is constant at 0.5. This fits the Bernoulli process definition.
Consider the second scenario: measuring the exact height of each student. This is a continuous measurement, not a binary outcome, so it does not fit the Bernoulli process.
Look at the third scenario: selecting a card from a deck without replacement. The trials are not independent because the composition of the deck changes after each draw, so this is not a Bernoulli process.
Examine the fourth scenario: rolling a die and recording the number. Each trial has six possible outcomes, not two, so it does not meet the Bernoulli process criteria.
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