In probability theory, what is the sum of the probabilities for all possible outcomes in a sample space ()? That is, what is equal to?
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
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
Which of the following is a property of a probability density function ()?
A
The is always a discrete function.
B
The must be greater than for all values of .
C
The can take negative values for some values of .
D
The total area under the curve of the is equal to .
Verified step by step guidance1
Recall that a probability density function (pdf) describes the relative likelihood of a continuous random variable taking on a particular value.
Understand that a pdf is a continuous function, not a discrete one, so it cannot be described as always discrete.
Remember that the pdf must be non-negative for all values of \( x \), meaning \( f(x) \geq 0 \) for all \( x \); it cannot take negative values.
Note that the value of the pdf at any point \( x \) can be less than or equal to 1; it does not have to be greater than 1 everywhere.
The key property of a pdf is that the total area under the curve over the entire range of \( x \) must equal 1, which mathematically is expressed as \( \int_{-\infty}^{\infty} f(x) \, d x = 1 \).
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