Given the following table, does it represent a valid discrete probability distribution? x: P(x):
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
Epidemiologists can attempt to deal with confounding in a study by all but which of the following?
A
Restriction
B
Matching
C
Ignoring the confounder in the analysis
D
Randomization
Verified step by step guidance1
Understand that confounding occurs when an extraneous variable influences both the independent and dependent variables, potentially biasing the results of a study.
Recognize that common methods to control confounding include Restriction (limiting study participants to certain categories), Matching (pairing subjects with similar confounder characteristics), and Randomization (randomly assigning subjects to groups to evenly distribute confounders).
Note that 'Ignoring the confounder in the analysis' means not accounting for the confounding variable at all, which does not control for confounding and can lead to biased results.
Therefore, the method that does NOT deal with confounding is ignoring the confounder in the analysis, as it fails to address the bias introduced by confounding variables.
Summarize that Restriction, Matching, and Randomization are valid techniques to handle confounding, while ignoring the confounder is not.
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