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This deck focuses on Confidence Intervals Slope Of Regression Models, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
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What is the point estimate for the population slope β in a regression model?
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b (the least-squares slope). Sample statistic estimates population parameter.
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This deck focuses on Confidence Intervals Slope Of Regression Models, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: b (the least-squares slope). Sample statistic estimates population parameter.
Answer: 16. df=18−2=16.
Answer: df=16. Use df=n−2=18−2 for regression slope inference.
Answer: b±t∗SEb. Uses sample slope plus/minus critical value times standard error.
Answer: s is the residual standard deviation. Measures typical size of residuals.
Answer: 0.84. Margin of error equals t∗×SEb=2.10×0.40.
Answer: Normality of residuals. Required for valid t-distribution inference about the slope.
Answer: SEb=∑(xi−xˉ)2s. Measures variability of slope estimate.
Answer: (2.2,4.2). Calculate 3.2±2.00(0.50)=3.2±1.0.
Answer: Residuals are approximately Normal. Required for valid t distribution inference.
Answer: β (the true population slope). Greek letter beta represents the true slope in the population regression line.
Answer: b is the least-squares sample slope. Calculated from sample data using least squares method.
Answer: 95% of intervals from repeats capture β. Long-run proportion containing true slope.
Answer: Slope not statistically different from 0 at level α. Cannot reject null hypothesis of no linear relationship.
Answer: s=n−2∑ei2. Sum of squared residuals divided by df.
Answer: (1.35, 3.45). 2.4±2.10(0.5)=2.4±1.05.
Answer: Yes, because the entire interval is >0. All plausible values are positive.
Answer: Equal variance (homoscedasticity). Residuals should have roughly constant spread across all x values.
Answer: Positive linear association; reject H0:β=0. All plausible slopes are positive, indicating x and y increase together.
Answer: Independence of observations. Ensures one observation doesn't influence another's error term.
Answer: s=n−2∑ei2 (residual SD). Square root of sum of squared residuals divided by degrees of freedom.
Answer: β (the true population slope). CIs estimate population parameters, not sample statistics.
Answer: Yes, because 0 is in the interval. CI contains 0, so no linear relationship proven.
Answer: Negative linear association; reject H0:β=0. All plausible slopes are negative, indicating x increases as y decreases.
Answer: df=n−2. Lose 2 df: one for intercept, one for slope.
Answer: dollars per hour. Slope units are always y-units divided by x-units.
Answer: t distribution with df=n−2. Used when population SD is unknown.
Answer: t∗SEb. How far CI extends from point estimate.
Answer: It decreases (interval becomes narrower). Larger n reduces SEb and t∗.
Answer: Constant variance (homoscedasticity). Equal spread at all x values.
Answer: 0.60. ME=2.00×0.30=0.60.
Answer: Linearity of the mean response. Ensures the model form is appropriate.
Answer: Linearity (scatterplot shows linear trend). Ensures the model y=β0+βx+ϵ is appropriate.
Answer: b±t∗SEb. Sample slope ± critical value × standard error.
Answer: t∗=t2α,n−2. Critical value from t-distribution with n−2 df and area α/2 in each tail.
Answer: Random sample or random assignment. Ensures results apply beyond the sample.
Answer: df=n−2. Subtract 2 from sample size for slope inference.
Answer: C% confident β is between L and U (units of y per x). States confidence that true slope falls within calculated bounds.