Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 17m
- 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 - ExcelBonus23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - ExcelBonus28m
- Confidence Intervals for Population Means - ExcelBonus25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - ExcelBonus12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - ExcelBonus9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - ExcelBonus21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - ExcelBonus12m
- Two Variances and F Distribution29m
- Two Variances - Graphing CalculatorBonus16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - ExcelBonus8m
- Finding Residuals and Creating Residual Plots - ExcelBonus11m
- Inferences for Slope31m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression15m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 29m
4. Probability
Basic Concepts of Probability
Multiple Choice
Which of the following is NOT a basic principle (axiom/property) of probability?
A
For any event , .
B
For any two events and , .
C
If events and are mutually exclusive, then .
D
, where is the sample space.
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Verified step by step guidance1
Step 1: Understand the basic axioms (principles) of probability. These are fundamental rules that any probability measure must satisfy.
Step 2: Recall the first axiom: For any event \(A\), the probability must satisfy \(0 \leq P(A) \leq 1\). This means probabilities are always between 0 and 1 inclusive.
Step 3: Recall the second axiom: The probability of the sample space \(S\) is 1, i.e., \(P(S) = 1\). This means something in the sample space must occur.
Step 4: Recall the third axiom: For any two mutually exclusive (disjoint) events \(A\) and \(B\), the probability of their union is the sum of their probabilities, i.e., if \(A \cap B = \emptyset\), then \(P(A \cup B) = P(A) + P(B)\).
Step 5: Analyze the given statements and identify which one violates these axioms. The statement that \(P(A \cup B) = P(A) + P(B)\) for any two events \(A\) and \(B\) (without the condition of mutual exclusivity) is NOT a basic axiom, because this equality only holds when \(A\) and \(B\) are mutually exclusive.
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A fair six-sided die is rolled once. What is the probability of rolling an even number?
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