Day 21 – 1 and 2 sample proportions

Slides:



Advertisements
Similar presentations
Chapter 10, part D. IV. Inferences about differences between two population proportions You will have two population proportions, p1 and p2. The true.
Advertisements

CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.
Section 7.1 Hypothesis Testing: Hypothesis: Null Hypothesis (H 0 ): Alternative Hypothesis (H 1 ): a statistical analysis used to decide which of two competing.
Stat 217 – Day 21 Cautions/Limitations with Inference Procedures.
Stat Day 16 Observations (Topic 16 and Topic 14)
Inference (CI / Tests) for Comparing 2 Proportions.
Chapter 9 Hypothesis Testing 9.4 Testing a Hypothesis about a Population Proportion.
Chapter 9 Hypothesis Testing.
Significance Tests for Proportions Presentation 9.2.
Statistics Pooled Examples.
Understanding the Variability of Your Data: Dependent Variable Two "Sources" of Variability in DV (Response Variable) –Independent (Predictor/Explanatory)
Section 9.2 Testing the Mean  9.2 / 1. Testing the Mean  When  is Known Let x be the appropriate random variable. Obtain a simple random sample (of.
STT 315 Ashwini Maurya Acknowledgement: Author is indebted to Dr. Ashok Sinha, Dr. Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit many.
Large sample CI for μ Small sample CI for μ Large sample CI for p
12.2 (13.2) Comparing Two Proportions. The Sampling Distribution of.
Section 3.3: The Story of Statistical Inference Section 4.1: Testing Where a Proportion Is.
Statistical Inference Drawing conclusions (“to infer”) about a population based upon data from a sample. Drawing conclusions (“to infer”) about a population.
Inference for Proportions Section Starter Do dogs who are house pets have higher cholesterol than dogs who live in a research clinic? A.
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 11 Section 3 – Slide 1 of 27 Chapter 11 Section 3 Inference about Two Population Proportions.
AP Statistics Chapter 24 Notes “Comparing Two Sample Means”
A.P. STATISTICS EXAM REVIEW TOPIC #2 Tests of Significance and Confidence Intervals for Means and Proportions Chapters
Comparing Two Proportions Chapter 21. In a two-sample problem, we want to compare two populations or the responses to two treatments based on two independent.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Chapter 10 Inference on Two Samples 10.3 Inference About Two Population Proportions.
Introduction to Inference Tests of Significance Proof
Christopher, Anna, and Casey
Chapter Nine Hypothesis Testing.
Regression and Correlation
Chapter 9: Testing a Claim
Comparing Two Proportions
Copyright © 2013 Pearson Education, Inc.
CHAPTER 9 Testing a Claim
Introduction to Inference
Chapter 24 Comparing Means.
More Hypothesis Testing
Chapter 9: Testing a Claim
STAT 312 Chapter 7 - Statistical Intervals Based on a Single Sample
Chapter 9: Testing a Claim
Hypothesis Tests for 1-Sample Proportion
MATH 2311 Section 8.2.
Chapter 9 Hypothesis Testing
Hypothesis Tests for a Population Mean in Practice
Males and Female College Completion Rates in Wisconsin in 1957
Inferences on Two Samples Summary
Chapter 9 Hypothesis Testing
Section 12.2: Tests about a Population Proportion
Putting It All Together: Which Method Do I Use?
Hypothesis Tests for Proportions
Summary of Tests Confidence Limits
AP STATISTICS LESSON 10 – 2 (DAY 3)
Chapter 9: Testing a Claim
CHAPTER 12 Inference for Proportions
CHAPTER 12 Inference for Proportions
Chapter 9: Testing a Claim
Chapter 9: Testing a Claim
CHAPTER 10 Comparing Two Populations or Groups
CHAPTER 9 Testing a Claim
CHAPTER 9 Testing a Claim
CHAPTER 9 Testing a Claim
Chapter 9: Testing a Claim
Chapter 9: Testing a Claim
Chapter 9: Testing a Claim
Chapter 9: Testing a Claim
CHAPTER 9 Testing a Claim
CHAPTER 9 Testing a Claim
Homework: pg. 709 #27, ) µ=the mean dissolved oxygen content in the stream. Ho:µ=5 Ha:µ
Chapter 9: Testing a Claim
Unit 5: Hypothesis Testing
Chapter 9: Testing a Claim
7.4 Hypothesis Testing for Proportions
Presentation transcript:

Day 21 – 1 and 2 sample proportions

An example Is smoking less common among pregnant women in NC than the general population of women? Nationally, about 13% of women smoke. Step 1: Set up hypotheses Step 2: Set up sampling distribution (Sketching the curve, finding p and n and SE) Step 3: Find the p-value of the sample proportion 𝑝 Step 4: Draw conclusions

Your turn – 5 minutes Approximately 8%* of pregnant women in the US reported smoking in 2014. Is the rate of smoking higher than this in NC for women 35 and under? Clearly label your hypothesis testing steps and include an appropriate graph.

Do smoking mothers give birth to a higher proportion of low-weight babies? Find the proportion of low-weight babies from smoker mothers. Find the proportion of low-weight babies from non-smoker mothers. Find: 𝑝 𝑠𝑚𝑜𝑘𝑒𝑟 − 𝑝 𝑛𝑜𝑛−𝑠𝑚𝑜𝑘𝑒𝑟 The standard error for the confidence interval is: Build a 95% Confidence Interval for the difference

Do smoking mothers give birth to a higher proportion of low-weight babies? Now for a hypothesis test: Null Hypothesis: Alternative Hypothesis: Since we are assuming that the two populations are identical we need to build a standard error based on the pooled proportion: Find the proportion of all babies in the sample (smokers and non-smokers) that had low-weight.  Call this 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 The standard error for the hypothesis test is:

Do smoking mothers give birth to a higher proportion of low-weight babies? Step 1: Hypotheses: 𝐻 𝑂 : 𝑝 𝑠𝑚𝑜𝑘𝑒𝑟 − 𝑝 𝑛𝑜𝑛𝑠𝑚𝑜𝑘𝑒𝑟 =0 𝐻 𝐴 : 𝑝 𝑠𝑚𝑜𝑘𝑒𝑟 − 𝑝 𝑛𝑜𝑛𝑠𝑚𝑜𝑘𝑒𝑟 >0 Step 2: Sampling Distribution: 𝑆𝐸= 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 1− 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 𝑛 1 + 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 1− 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 𝑛 2 𝑝 𝑠𝑚𝑜𝑘𝑒𝑟 − 𝑝 𝑛𝑜𝑛𝑠𝑚𝑜𝑘𝑒𝑟 = Things we know: 𝑝 𝑠𝑚𝑜𝑘𝑒𝑟 = 𝑛 𝑠𝑚𝑜𝑘𝑒𝑟 = 𝑝 𝑛𝑜𝑛𝑠𝑚𝑜𝑘𝑒𝑟 = 𝑛 𝑛𝑜𝑛𝑠𝑚𝑜𝑘𝑒𝑟 = 𝑝 𝑝𝑜𝑜𝑙𝑒𝑑 =

Do smoking mothers give birth to a higher proportion of low-weight babies? Step 3: p-value p-value = Step 4: Conclusion: With a p-value of… we conclude that…

Surprised by the results? Why is this? Variability in birth weight comes from many factors. While smoking might be one factor, it is not a strong enough factor to see a difference in this data set. A primary goal of statistics is to find “signals in the noise.” If you don’t find the signal, it could be because it isn’t there, or because there is too much noise. Try limiting other variables. With the data in this study, we cannot make claims about differences in birth weight…