HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.3.

Slides:



Advertisements
Similar presentations
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 12.2.
Advertisements

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.4.
Chapter 8: Estimating with Confidence
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7.3 Estimating a Population mean µ (σ known) Objective Find the confidence.
Fall 2006 – Fundamentals of Business Statistics 1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 7 Estimating Population Values.
Estimation 8.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
Confidence Intervals Week 10 Chapter 6.1, 6.2. What is this unit all about? Have you ever estimated something and tossed in a “give or take a few” after.
Confidence Intervals: Estimating Population Mean
Confidence Intervals for the Mean (σ Unknown) (Small Samples)
Hypothesis Testing and T-Tests. Hypothesis Tests Related to Differences Copyright © 2009 Pearson Education, Inc. Chapter Tests of Differences One.
Chapter 7 Confidence Intervals and Sample Sizes
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. 3.1-mean-backwards.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 6.4.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.1.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 10.2.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 10.3.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 10.2.
Section 8.2 Estimating Population Means (Large Samples) HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.5.
Lesson Confidence Intervals: The Basics. Knowledge Objectives List the six basic steps in the reasoning of statistical estimation. Distinguish.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Estimating a Population Mean: σ Known 7-3, pg 355.
Alan Flores, Kathy Alvarado, Carol Prieto Period:
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7.4 Estimation of a Population Mean  is unknown  This section presents.
Chapter 6 Lecture 3 Sections: 6.4 – 6.5.
Section 8.2 Estimating Population Means (Large Samples) HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.2.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 8.
Confidence Interval Estimation for a Population Proportion Lecture 31 Section 9.4 Wed, Nov 17, 2004.
Section 10.3 Hypothesis Testing for Means (Large Samples) HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 6.4.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 6.4.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 12.2.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 10.3.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 12.3.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7-5 Estimating a Population Variance.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 6.3.
Section 6-3 Estimating a Population Mean: σ Known.
Section 7-3 Estimating a Population Mean: σ Known.
Statistics for Business and Economics 8 th Edition Chapter 7 Estimation: Single Population Copyright © 2013 Pearson Education, Inc. Publishing as Prentice.
Section 8.4 Estimating Population Proportions HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant Systems,
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.2.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.1.
 A Characteristic is a measurable description of an individual such as height, weight or a count meeting a certain requirement.  A Parameter is a numerical.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Example: In a recent poll, 70% of 1501 randomly selected adults said they believed.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 6.
Chapter 6 Lecture 3 Sections: 6.4 – 6.5. Sampling Distributions and Estimators What we want to do is find out the sampling distribution of a statistic.
1 Chapter 6. Section 6-3. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION E LEMENTARY.
Confidence Intervals for a Population Mean, Standard Deviation Unknown.
Section 6.4 Finding z-Values Using the Normal Curve ( with enhancements by D.R.S. ) HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008.
Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Section 6.2 Confidence Intervals for the Mean (  Unknown)
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7.4: Estimation of a population mean   is not known  This section.
Section 6.2 Confidence Intervals for the Mean (Small Samples) Larson/Farber 4th ed.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.3.
Section 8.1 Introduction to Estimating Population Means HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
Chapter Outline 6.1 Confidence Intervals for the Mean (Large Samples) 6.2 Confidence Intervals for the Mean (Small Samples) 6.3 Confidence Intervals for.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Example: In a recent poll, 70% of 1501 randomly selected adults said they believed.
Section 8.3 Estimating Population Means (Small Samples) HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.1.
Understandable Statistics Eighth Edition By Brase and Brase Prepared by: Lynn Smith Gloucester County College Edited by: Jeff, Yann, Julie, and Olivia.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.3.
Section 6.2 Confidence Intervals for the Mean (Small Samples) © 2012 Pearson Education, Inc. All rights reserved. 1 of 83.
Confidence Interval for a Population Mean Estimating p Estimating  (  known) Estimating  (  unknown) Sample Size.
Inference: Conclusion with Confidence
CHAPTER 8 Estimating with Confidence
Estimating Population Means (Large Samples)
Introduction to Estimating Population Means
Inference: Conclusion with Confidence
Chapter 6 Confidence Intervals.
Confidence intervals for the difference between two means: Independent samples Section 10.1.
Presentation transcript:

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.3 Estimating Population Means (  Unknown) And some valuable added stuff by D.R.S., University of Cordele

Should I use z or t for my confidence interval? We have two bell-shaped distributions, z and t. Which one to use? I need more advanced techniques that are beyond the scope of this course. Is the population approximately normally distributed? no Do I know the value of σ, the population’s standard deviation? yes Use z Use t yes no

The formula for E in a t problem is very much like the formula for E in a z problem They have the same arithmetic structure. Difference: using a t critical value instead of a z critical value. Difference: using sample standard deviation s instead of population standard deviation σ.

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Estimating Population Means (  Unknown) Confidence Interval for a Population Mean The confidence interval for a population mean is given by Where is the sample mean, which is the point estimate for the population mean, and E is the margin of error.

The train crossing at Highway 92 was observed one day. Thirteen trains passed and the length of time the roadway was blocked was recorded for each. The mean was 282 sec (4 min 42 sec) and the standard deviation was 100 sec (1 min 40 sec). The distribution of all trains’ lengths is thought to be normal. Construct the 90% confidence interval of the time it takes for a train to pass. Is this a z problem or a t problem? ______ Why is it legitimate to use these formulas? Because even though ___________, the population has ___________ ____________.

Make note of variables and values: _____ = 13 _____ = 282 _____ = 100 c = _____ α = _____ α / 2 = _____ t α / 2 = _____ To replay the entire example, go to click on Examples, click on Chapter 8, click on Section 8.3, click on “Confidence interval with t”

Compute the Margin of Error:

Compute the Confidence Interval: Verify by doing the problem with the TI-84 Tinterval.

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Example 8.16: Constructing a Confidence Interval for a Population Mean (  Unknown) A marketing company wants to know the mean price of new vehicles sold in an up-and ‑ coming area of town. Marketing strategists collected data over the past two years from all of the dealerships in the new area of town. From previous studies about new car sales, they believe that the population distribution looks like the following graph. Valid sample? _______ Normal distribution? ________ Use z or t? _______ Need a big sample, 30 or more? _____

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Example 8.16: Constructing a Confidence Interval for a Population Mean (  Unknown) (cont.) The simple random sample of 756 cars has a mean of $27,400 with a standard deviation of $1300. Construct a 95% confidence interval for the mean price of new cars sold in this area. Make some notes as you read: 756 = _____ $27400 = _______ $1300 = ______ 95% = ______ so _____ = ______ and ____ / 2 = ______ Should we use z or use t or do we need a technique that’s not part of this course? __________________________

Example 8-16 – Car Prices, continued

Do the same problem again, but this time use TI-84 STAT, TESTS, 8:TInterval What does the TI-84 give for the confidence interval? It does not tell you the margin of error, E, directly. But could you figure out E from the information shown?

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Further words about ZInterval and TInterval If you’re asked for a confidence interval, Use ZInterval for a normal distrib. situation. Use TInterval for a t-distribution situation. If the problem asks for a critical value of z or t, too, Then you have to use invNorm( or invT( or a printed table to answer that question. Make the right choice between Stats, if you’re given the mean, etc. Data, if you’re given a list of raw data

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Example 8.18: Constructing a Confidence Interval for a Population Mean (  Unknown) from Original Data Given the following sample data from a study on the average amount of water used per day by members of a household while brushing their teeth, calculate the 99% confidence interval for the population mean using a TI-83/84 Plus calculator. Assume that the sample used in the study was a simple random sample. Should we use z or t or some other advanced technique? And why?

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Example 8.18: Constructing a Confidence Interval for a Population Mean (  Unknown) from Original Data (cont.) * Household Water Used for Brushing Teeth (in Gallons per Day)

HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Example 8.18: Constructing a Confidence Interval for a Population Mean (  Unknown) from Original Data (cont.) To begin with, since we are given the raw data and not the sample statistics, we need to enter the data in the calculator list, like in L1. Then use TInterval, but this time highlight the Data option, not the Stats option! You’ll see some differences in the prompts. Confidence interval result is ( _________, ___________ ) Conclusion: _____ % confident that ___________________ _______________________

Stuff you need to know about the Practice and Certify problems They come in pairs. First part asks you for “the critical value”. Second part asks you for the confidence interval. For the critical value, the easiest thing to do is to use your printed tables. invT can be used but the tables area easier.

Stuff you need to know about the Practice and Certify problems For the Confidence Interval, TInterval with the calculator is the best way. The Tutor probably does it the long way. There are some multipart problems that give you the raw data. Put the data into a TI- 84 list. Use 1-Var Stats to answer any preliminary questions about mean and standard deviation. Use T-Interval with “Data”, not “Stats”, to get the confidence interval.