Post on 06-Jul-2016
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review7 MAY 2014
Teddy Li
AP STATISTICS B
UNIT 5 CHAPTER 18-22• reject the null hypothesis• fail to reject the null hypothesis
• If Alpha is low and Power is low, more possibility for Type II Error
• SAMPLE MEANS: Quantitative Variable• SAMPLE PROPORTIONS: Categorical Variable
UNIT 5 CHAPTER 18-22, CONT.
• The power of a test concerns itself with the ability to detect an alternative hypothesis.
• When choosing a significance level alpha, you’re setting the probability of a Type I error to alpha.
• Z Test Statistic: • Always draw a picture!• Var(X-Y) = Var(X) + Var(Y)• SD2 = Var
UNIT 5 CHAPTER 18-22, CONT.• Parameter: true proportion of a population• Statistic: sample proportion• Central Limit Theorem: If the assumptions of SRS, 10% Condition,
Success/Fail and Independence are true, the data is approximately normal and you can use the normal model.
• Mean & SD for Sample Proportion: • Mean & SD for Sample Mean: • Mean:
UNIT 6 CHAPTER 23-25• Narrow confidence interval results from a larger sample size.• t test
• SRS, 10% Condition, Nearly Normal (Draw Histogram)
• Estimate Average number:
UNIT 6 CHAPTER 23-25, CONT. Chapter 23: One Sample T (means) Chapter 24: Two Sample T (difference in means) Chapter 25: Paired T Test
Hypothesis write a statement 0 write a statement write a statement
Assumptions 1. SRS2. 10% Condition for mean3. Nearly Normal – check histogram
1. SRS2. 10% Condition for both means3. Independent Groups/Independence4. CLT (Central Limit Theorem)
1. SRS2. 10% Condition of sample3. Paired Data Assumption4. Nearly Normal – check histogram
Test an appropriate hypothesis
Reminder
Conclusion
At this alpha level, there [is/is not] enough evidence that the mean is equal to the null
At the alpha level, there [is/is not] enough evidence of a significant difference in [topic]
At this alpha level, there [is/is not] enough evidence that the before/after are the same
Sample Size Estimate
Use the same S and ME (given in problem)
none
USE THE CALCULATOR TO FIND df
none
USE THE CALCULATOR TO FIND df
Confidence Interval
UNIT 7 CHAPTER 26-27
• Goodness of Fit: determine how close the observed values fit the null; diff between observed and expected
• Homogeneity: compare distribution of counts for 2+ groups of SAME categorical variable; distributions are same
• Independence: test whether two DIFF categorical variables are independent; populations are independent
UNIT 7 CHAPTER 26-27, CONT.
• If you reject the null, RESIDUALS • Straight Enough Condition (scatterplot), Randomization Condition, Equal
Variance Condition (no pattern in residuals), Nearly Normal• Degrees of freedom: n-2
• CI
UNIT 7 CHAPTER 26-27, CONT.• Example
• T test: • 1.55 – 1.85
Variable Coeff SE(Coeff) t-ratio P-ValueIntercept -42.734 2.717 -15.7 <.0001Waist 1.70 .0743 22.9 <.0001