In Problems 9–14, determine whether the distribution is a discrete probability distribution. If not, state why.
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: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 9m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors17m
- 10. Hypothesis Testing for Two Samples5h 37m
- 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
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
5. Binomial Distribution & Discrete Random Variables
Discrete Random Variables
Problem 6.1.32b
Textbook Question
"Video Poker The following table shows the net winnings from a \$1 bet in a video poker game.
b. If a player expects to play 90 games in one hour, how much can the player expect to win or lose during that hour?"

Verified step by step guidance1
Step 1: Understand the problem context. The table provides the probabilities of different outcomes in a video poker game along with the corresponding profit (or loss) for each outcome from a \$1 bet.
Step 2: Calculate the expected profit for a single game. Use the formula for expected value: \(E(X) = \sum (P_i \times X_i)\), where \(P_i\) is the probability of outcome \(i\) and \(X_i\) is the profit for outcome \(i\). Multiply each profit by its probability and sum all these products.
Step 3: Interpret the expected value. The expected value represents the average profit or loss per game if the game is played many times.
Step 4: Calculate the expected profit for 90 games. Since the player expects to play 90 games in one hour, multiply the expected profit per game by 90 to find the total expected profit or loss for the hour.
Step 5: Summarize the result. The final value from step 4 will indicate how much the player can expect to win or lose during one hour of playing 90 games.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Expected Value
Expected value is the weighted average of all possible outcomes, calculated by multiplying each outcome's value by its probability and summing these products. It represents the long-term average gain or loss per game, helping players understand the average result of repeated plays.
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Expected Value (Mean) of Random Variables
Probability Distribution
A probability distribution lists all possible outcomes of a random experiment along with their probabilities. Understanding this distribution is essential to calculate expected values and to assess the likelihood of different results in games like video poker.
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Calculating Probabilities in a Binomial Distribution
Scaling Expected Value Over Multiple Trials
When a game is played multiple times, the total expected winnings or losses are found by multiplying the expected value per game by the number of games played. This helps estimate overall profit or loss over a session, such as 90 video poker games in one hour.
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