Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- 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 20m
- 9. Hypothesis Testing for One Sample5h 15m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- 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 Calculator15m
- 11. Correlation1h 24m
- 12. Regression3h 42m
- 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 Slope32m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression23m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 32m
12. Regression
Prediction Intervals
Struggling with Statistics for Business?
Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
A linear regression model predicts weekly revenue from ad spending. You find the prediction interval for exactly \$200 in ad spending is (\$520, \$610). Choose the answer that best describes what this interval means.
A
The model will generate at least \$520 in revenue.
B
The average revenue for \$200 in ad spending is exactly \$565.
C
We are confident that a single weekly revenue value with \$200 in ad spending will fall between \$520 and \$610.
D
We are confident the mean revenue from \$200 in ad spending is between \$520 and \$610.
0 Comments
Verified step by step guidance1
Understand the context: The problem involves interpreting a prediction interval in a linear regression model. A prediction interval provides a range where we expect a single observation (in this case, weekly revenue) to fall, given a specific value of the independent variable (ad spending).
Clarify the difference between prediction intervals and confidence intervals: A prediction interval is used to estimate the range for a single observation, while a confidence interval estimates the range for the mean of the dependent variable. This distinction is crucial for selecting the correct interpretation.
Analyze the given interval: The prediction interval for \$200 in ad spending is (\$520, \$610). This means that the model predicts a single weekly revenue value to fall within this range with a certain level of confidence (typically 95%).
Evaluate the provided options: The first option ('The model will generate at least \$520 in revenue') is incorrect because a prediction interval does not guarantee a minimum value; it only provides a range with a certain confidence level. The second option ('The average revenue for \$200 in ad spending is exactly \$565') is also incorrect because the interval does not describe the mean revenue. The fourth option ('We are 95% confident the mean revenue from \$200 in ad spending is between \$520 and \$610') is incorrect because the interval is a prediction interval, not a confidence interval for the mean.
Select the correct answer: The third option ('We are 95% confident that a single weekly revenue value with \$200 in ad spending will fall between \$520 and \$610') is correct because it accurately describes the purpose of a prediction interval in this context.
Watch next
Master Prediction Intervals with a bite sized video explanation from Patrick
Start learningRelated Videos
0
Prediction Intervals practice set

