Ppt on statistics and probability lessons

Probability and Statistics

based on what information you want the reader to draw from the graph. An Advanced Display of Data Hans Rosling Probably one of the most informative and modern displays of data can be seen from the work of Hans Rosling. The link above shows a video of/any of the data values When best to use The mean is best used when you data is continuous and symmetrical. Often necessary for use in other statistical measures. Lessons on Arithmetic Mean How to Find the Mean Visit the web site above to learn more about the /


A Course on Software Test Automation Design

Statistical Testing: Thoughts Toward an Architecture We have a population of tests, which may have been sandboxed and which may carry self-check info. A test series involves a sample of these tests. We have a population of diagnostics, probably too/, Model-Based Testing, Proceedings of Software Quality Week 1997 (not included in the course notes) Michael Deck and James Whittaker, Lessons learned from fifteen years of cleanroom testing. STAR 97 Proceedings (not included in the course notes). Doug Hoffman/


Hee-chan Lew Korea National University of Education

students. Korean mathematics education seems to have many serious weak points, despite of students very proud achievement. Affective Characteristics: Lesson from TIMSS Report Korean students affective characteristics was not friendly to mathematics compared with other countries. In the case of/Time Type of problems Type A (130,000) (100,000) MathⅠ 40% MathⅡ 40% Select 1 among Calculus, Statistics and probability, Discrete math 20% 30 items 100 Minutes Choice:70%, Short Answer:30% Type B (270,000) (300,000) /


Bouncing Through Percentages and Probability Ambar Paulino and Christina Raiti.

dealing with fractions, percentages, and probability Students will create word problems dealing with real basketball scenarios and their particular players Activities: Create their own study guides Split into partners and test each other with their/statistics and give logical reasons to support their players Students will listen and ask questions about guest Speaker Wendy Davis’s speech Activities: Students will listen to guest speaker Wendy Davis Students will present their final projects Activities: Lesson/


Counting Outcomes Lesson 6.2.3.

means for you: Lesson 6.2.3 Counting Outcomes California Standards: Statistics, Data Analysis and Probability 3.1 Represent all possible outcomes for compound events in an organized way (e.g., tables, grids, tree diagrams) and express the theoretical probability of each outcome. Statistics, Data Analysis and Probability 3.3 Represent probabilities as ratios, proportions, decimals between 0 and 1, and percentages between 0 and 100 and verify that the probabilities computed are reasonable; know/


Development of New Drugs: Lessons from Clinical Trials Paul A. Meyers, MD Vice-Chair, Pediatrics Memorial Sloan-Kettering Professor of Pediatrics Weill.

of New Drugs: Lessons from Clinical Trials / malignancy Retrieval therapies for solid tumors Surgery, radiation, chemo Phase 3: Statistical Considerations post hoc analyses – Post relapse survival and MTP in osteosarcoma Surgical resection of metastatic sites necessary for survival No impact/toxicity profile Phase III studies in non-sarcoma indications in combination with chemothearpy: low toxicity Probable favorable benefit:risk ratio in phase III trials in sarcoma Regulatory Issues Requirement for placebo /


© 2012 Common Core, Inc. All rights reserved. commoncore.org NYS COMMON CORE MATHEMATICS CURRICULUM Statistics and Probability in Grades 6 - 11 A Story.

Session Objectives Explore the distinction between mathematical thinking and statistical thinking. Examine the development of the statistics and probability content over grades 6 – 11. Introduce overarching themes that provide coherence in the statistics and probability content across the grades. Illustrate development of statistical thinking across the grades with a trajectory of lesson activities. Explore other dimensions in the development of statistical thinking across the grades. 2 © 2012 Common Core/


8.1 WELCOME TO COMMON CORE HIGH SCHOOL MATHEMATICS LEADERSHIP 2014-2015 SCHOOL YEAR SESSION 8 17 DEC 2014 ASSOCIATIONS, PROGRESSIONS, AND DATA (OH MY!)

housekeeping (with Meghan Steinmeyer)  Progressions document: Grades 7 and 8 & the middle grades spectrum  Break  Model lesson, Part 1: (inspired by) engage ny Grade 8, Lessons 13 & 14  Model lesson, Part 2: associating Grade 8 and high school  Closing remarks & For Next Time 8.3 LEARNING INTENTIONS AND SUCCESS CRITERIA We are learning to…  Describe the progression of statistics and probability concepts in Grades 6-8 of CCSSM  Create/


Statistics and Modelling Course 2011. Topic: Sample statistics & expectation Part of Achievement Standard 90643 Solve straightforward problems involving.

Statistics and Modelling Course 2011 Topic: Sample statistics & expectation Part of Achievement Standard 90643 Solve straightforward problems involving probability 4 Credits Externally Assessed NuLake Pages 147  163 Sigma: Old version – Ch 2. New version – Ch 7. LESSON 1 – Probability distribution Points of today:  Learn the meaning of discrete and continuous random variables.  Use a probability distribution table to display outcomes.  What is Expected Value and how do you calculate it/


Integrating Probability, Statistics and Genetics in Grade 7 Steven Blumsack Emeritus Professor, Mathematics (FSU) Assistant in Research: FCR-STEM (FSU)

Integrating Probability, Statistics and Genetics in Grade 7 Steven Blumsack Emeritus Professor, Mathematics (FSU) Assistant in Research: FCR-STEM (FSU) We will work in terms of 3 (2 if necessary) Each team packet: 3 BLUE, 3 WHITE, 1 YELLOW, 1 PURPLE, 1 penny RATIONALE Integration of mathematics & science – Provides context in mathematics classroom – Opens door for deeper discussions in science – Reduces “Silos” Why Probability, Statistics, Genetics/


Dr. C. Ertuna1 Statistical Sampling & Analysis of Sample Data (Lesson - 04/A) Understanding the Whole from Pieces.

Statistical Sampling & Analysis of Sample Data (Lesson - 04/A) Understanding the Whole from Pieces Dr. C. Ertuna2 Sampling Sampling is : Collecting sample data from a population and Estimating population parameters Sampling is an important tool in business decisions since it is an effective and/to observe a particular (cumulative) probability. There is a relationship between z-score and probability over p(x) = (1-Normsdist(z))*tails and There is a relationship between z-score and the value of the random /


Welcome to our Workshop Welcome to our Workshop “Incorporating Multiculturalism into Math and Science”

are not physical, they are a tool to measure representations of things.  Include a quick lesson on a previous culture who used some method of statistics. Objective: To recognize trends and predict behavior Probability is another aspect of statistics that uses collecting and organizing data in order to see trends and predict behavior. It is used to make better judgments. The idea is to bridge the gap/


In this lesson you will learn to reach fundamental understandings of conditional probability by modeling scenarios.

Diagram P(A C ∩ B C ) P(A) P(B) P(A ∩ B) = 1 Core Lesson Find the probability using a Venn Diagram. A statistics professor gave her class two tests, one on Thursday and one on Friday. 31% of students passed both tests, while 62% of students passed the Thursday test. What percent of students passing the Thursday test also passed the Friday/


Lesson 15 - 1 Nonparametric Statistics Overview. Objectives Understand Difference between Parametric and Nonparametric Statistical Procedures Nonparametric.

Lesson 15 - 1 Nonparametric Statistics Overview Objectives Understand Difference between Parametric and Nonparametric Statistical Procedures Nonparametric methods use techniques to test claims that are distribution free Vocabulary Parametric statistical procedures – inferential procedures that rely on testing claims/results of the test are typically less powerful. Recall that the power of a test refers to the probability of making a Type II error. A Type II error occurs when a researcher does not reject the /


Recovery-Oriented Computing Statistical Analysis & Systems: Retrospective and Going Forward Emre Kıcıman Software Infrastructures.

and observed probabilities at transitions Sum the deviation between expected and observed probabilities /Statistical analysis + systems Simplify, improve admin, reliability Simplify, improve admin, reliability Automatic analysis → handles complex systems Automatic analysis → handles complex systems Fast training → scales to frequent system changes Fast training → scales to frequent system changes First round of work promising, learned important lessons First round of work promising, learned important lessons/


Return to Big Picture Main statistical goals of OODA: Understanding population structure –Low dim ’ al Projections, PCA … Classification (i. e. Discrimination)

“Stretch” in Data Miao (2015) Kernel Embedding Aizerman, Braverman and Rozoner (1964) Motivating idea: Extend scope of linear discrimination,/ (everybody currently does the latter) Kernel Embedding Standard Normal Probability Density Kernel Embedding Na ï ve Embedd ’ g, Toy/Note: Embedded data are very non-Gaussian Classical Statistics: “Use Prob. Dist’n” Looks Hopeless / Poor generalizability Too big  miss important regions Classical lessons from kernel smoothing Surprisingly large “reasonable region” I.e/


AP STATISTICS LESSON 6 - 2 AP STATISTICS LESSON 6 - 2 PROBABILITY MODELS.

AP STATISTICS LESSON 6 - 2 AP STATISTICS LESSON 6 - 2 PROBABILITY MODELS ESSENTIAL QUESTION: What is a probability model and how can it be used to solve statistics problems? Objectives:  To define and use the vocabulary of probability.  To design probability models that fit real–life problems. Basic Descriptions of Probability Models  A list of all possible outcomes.  A probability for each outcome. For example, the probability model for a coin toss is one out/


1 Opinionated in Statistics by Bill Press Lessons #15.5 Poisson Processes and Order Statistics Professor William H. Press, Department of Computer Science,

1 Opinionated in Statistics by Bill Press Lessons #15.5 Poisson Processes and Order Statistics Professor William H. Press, Department of Computer Science, the University of Texas at Austin In a “constant rate Poisson process”, independent events occur with a constant probability per unit time In any small interval  t, the probability of an event is  t In any finite interval , the mean (expected) number of events/


C Extending Mathematical Power with Date Name. Introduction to EMPower Math State of Numeracy in the US Current (and Future) Shifts in Adult Ed The EMPower.

Statistics & Probability Level E (9 – 12)Algebra & Number Sense, Algebra & Geometry, Statistics & Probability Shift 1: FOCUS Mile Deep, Inch Wide Where SHOULD we focus? Are we missing the mark? Source: National Council on Education and the Economy. What Does It Really Mean to Be College and/? Turn this into an advantage Facilitate deep understanding by slowing down Make the Lesson Easier/Make the Lesson Harder Use Technology! Provide Additional Practice College & Career Readiness Practice Workbooks EMPower /


1. Background information The ASA/NCTM Joint Committee on the Curriculum in Statistics and Probability and the American Statistical Associations Center.

information! 6. Teacher Support Materials Common Core State Standards Grade 7 & 8: Statistics and Probability High School Statistics and Probability: Interpreting Categorical and Quantitative Data High School Statistics and Probability: Making Inferences and Justifying Conclusions High School Statistics and Probability: Conditional Probability and the Rules of Probability High School Statistics and Probability: Using Probability to Make Decisions Standards for the 21 st Century Learner Standards for the 21/


Discrete Probability Distributions To accompany Hawkes lesson 5.1 Original content by D.R.S.

Hawkes lesson 5.1 Original content by D.R.S. Examples of Probability Distributions/4, 5, 6 rolled on a die Continuous All real numbers in some interval An age between 10 and 80 (10.000000 and 80.000000) A dollar amount A height or weight Discrete is our focus for now Discrete A countable number/4952/1000 Win fourth prize $953/1000 Loser$ -5993/1000 Total1000/1000 Expected Value Problems Statistics The mean of this probability is $ - 0.70, a negative value. This is also called “Expected Value”. Interpretation/


Welcome to the Upper School Information e-Booklet

to History Home Page Next page  So what do students actually do in history lessons? You’ll find yourself doing role plays… constructing a reasoned argument both in writing and spoken aloud… playing a variety of fun simulations designed to make ideas easier to grasp/information on each area of study please click below: 1 Number 2 Algebra 3 Ratio, proportion and rates of change 4 Geometry and measures 5 Probability 6 Statistics Useful websites: www.mymaths.co.uk www.emaths.co.uk www.bbcbitsize.co.uk www.gcse./


Welcome to the Upper School Information e-Booklet

on each area of study please click below: 1 Number 2 Algebra 3 Ratio, proportion and rates of change 4 Geometry and measures 5 Probability 6 Statistics Useful websites: www.mymaths.co.uk www.emaths.co.uk www.bbcbitsize.co.uk www./a science context Unit 4: Controlled Assessment Investigative Skills Assignment – this consists of two written assessments plus two lessons for practical work and data processing Previous Page “Striving for Excellence” Exit Back to Core Subjects Page Back to Core Subjects Page The/


BA 452 Lesson C.1 Risk Simulation 1 Review We will spend up to 30 minutes reviewing Exam 1 Know how your answers were graded.Know how your answers were.

n The Hewlett Packard problem allows the expected value to be computed exactly, but not other statistics, such as the probability of a loss. Risk Analysis BA 452 Lesson C.1 Risk Simulation 32 n Hewlett Packard believes possible values of c 1 (direct labor / possible values of c 2 (parts cost for each unit) depend on the general economy, the overall demand for parts, and the pricing policy of Hewlett Packard’s parts suppliers. Specifically, they believe c 2 has a continuous uniform distribution that ranges/


1 Lesson 6.2.2 Expressing Probability. 2 Lesson 6.2.2 Expressing Probability California Standard: Statistics, Data Analysis and Probability 3.3 Represent.

1 Lesson 6.2.2 Expressing Probability 2 Lesson 6.2.2 Expressing Probability California Standard: Statistics, Data Analysis and Probability 3.3 Represent probabilities as ratios, proportions, decimals between 0 and 1, and percentages between 0 and 100 and verify that the probabilities computed are reasonable; know that if P is the probability of an event, 1– P is the probability of an event not occurring. What it means for you: You’ll meet and use/


Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson8-1 Lesson 8: One-Sample Tests of Hypothesis.

, based on sample evidence and probability theory, used to determine whether the hypothesis is a reasonable statement and should not be rejected, or is unreasonable and should be rejected. Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson8-8 Hypothesis Testing Step 1: state null and alternative hypothesis Step 2: select a level of significance Step 3: identify the test statistic Step 4: formulate a/


How confident can we be in our analysis?. Unit Plan – 10 lessons  Recap on CLT and Normal Distribution  Confidence intervals for the mean  Confidence.

 If such a confidence interval does not enclose zero then it is unlikely that the two means are equal. There is probably a difference between the two means. Who Drives Faster?  Our Conclusion:  Since _______ lies within the confidence interval, there / parameter π with the sample statistic p. Starter lesson 7: Two independent populations have means of 85.4 and 64.3 respectively and standard deviations are 8.7 and 6.4. A random sample of 64 is drawn from the first population and 36 from the second. /


Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 April 4, 2012.

%) All purple columns statistically significantly different at p<0.05 Overall Model Goodness Full data set: School explains 1.1% of variance in gaming Hours 3-8: School explains 1.7% of variance in gaming Student, tutor lesson, and problem all found to predict significantly larger proportion of variance (Baker, 2007; Baker et al, 2009; Muldner et al, 2010) Slip Probability Urban school Suburban/


Lecture Topic 9: Risk and Return Lessons from Market History Presentation to Cox MBA Students FINA 6214: International Financial Markets Presentation to.

returns from the past That is, we have some observations drawn from the probability distribution –We can estimate the variance and expected return using the arithmetic mean of past returns and the sample variance Risk Statistics Calculating sample statistics –Mean, or Average, Return –Sample Variance –Sample Standard Deviation Risk Statistics Example: Return, Variance, and Standard Deviation YearActual Return Average Return Deviation from the Mean Squared Deviation 1/


Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 5-1 2nd Lesson Probability and Sampling Distributions.

: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 5-1 2nd Lesson Probability and Sampling Distributions Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 5-2 Probability Distributions Continuous Probability Distributions Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions Normal Chi Square Fisher MultinomialStudent-t Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 5-3 A discrete/


Empirical Methods for AI (and CS) CS1573: AI Application Development (after a tutorial by Paul Cohen, Ian P. Gent, and Toby Walsh)

dependent variable (e.g., run time) is the sum of causal factors and random noise. Statistical methods assign parts of this variability to the factors and the noise. 21 Lesson: Keep the big picture in mind Why are you studying this? Load / zLike proof by contradiction: Assert the opposite (the coin is fair) show that the sample result (≥ 8 heads) has low probability p, reject the assertion, with residual uncertainty related to p. zEstimate p with a sampling distribution. 44 The logic of hypothesis testing/


Probability & Statistics – Bell Ringer  Make a list of all the possible places where you encounter probability or statistics in your everyday life. 1.

EXERCISES 2 CHAPTER ONE THE NATURE AND PROBABILITY OF STATISTICS 3 LESSON ESSENTIAL QUESTION  Why is it important to study statistics? 4 Key Term  Statistics: the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions. 5 Why Study Statistics? 1. Students and professionals must be able to read and understand statistical studies performed in their field.  Requires knowledge of vocabulary, symbols, concepts, and statistical procedures used in these studies. 6/


Statistics A Basic Introduction and Review. Statistics Objectives By the end of this session you will have a working understanding of the following statistical.

small samples from populations that are not approximately normal. Chi-Square Distribution The distribution of the chi-square statistic is called the chi- square distribution. In this lesson, we learn to compute the chi-square statistic and find the probability associated with the statistic. Suppose we conduct the following statistical experiment. We select a random sample of size n from a normal population, having a standard deviation/


7.1 WELCOME TO COMMON CORE HIGH SCHOOL MATHEMATICS LEADERSHIP 2014-2015 SCHOOL YEAR SESSION 7 3 DEC 2014 PROGRESSING THROUGH STATISTICS (IN LINEAR AND.

CRITERIA We are learning to…  Identify productive struggle in our learning and teaching  Create models for non-linear data  Describe the progression of statistics and probability concepts in Grades 6-8 of CCSSM  Plan, teach, and reflect on a probability and statistics-focused lesson that embodies the Mathematics Teaching Practices 7.4 LEARNING INTENTIONS AND SUCCESS CRITERIA We will be successful when we can:  Connect productive struggle to student outcomes/


Unit 3 Extraordinary Teachers English Song — Teacher I Need You *Before Reading _main* The Lines to Praise the Teacher in Movies Helen Keller and Her Teacher.

grades of my life in college. I never believed I would do that well and probably wouldn’t have if it had not been for Professor Fine’s encouragement. For two years, I looked forward to taking statistics. When the time finally arrived, I did something that I had never done /days Gonna tell him I dream of him every night One of these days Gonna show him I care, Gonna teach him a lesson alright I was in a trance when I kissed the teacher Suddenly I took the chance when I kissed the teacher Unit 3 Extraordinary/


1 A. Hoecker: Statistical IssuesCAT Physics meeting, Feb 9, 2007 Talking Statistics  Impressions from the ATLAS Statistics WS, Jan 2007  Andreas Hoecker.

of an event is fixed but not known and cannot be known The tools of frequentist statistics tell us what to expect, under the assumption of certain probabilities, about hypothetical repeated observations Frequentist confidence levels (CLs/includes shape information 13 A. Hoecker: Statistical IssuesCAT Physics meeting, Feb 9, 2007 Example: Lessons from TEVATRON Tom Junk gave an interesting talk about lessons from Tevatron. Many concrete examples of statistics use cases and pitfalls (some touched in this ré/


Greenmount Primary School Our Curriculum Our aim is to provide a creative, exciting and engaging curriculum which will enable our children to gain the.

, we teach the following discrete English lessons each week: One lesson focusing on Reading Comprehension and love of literature One lesson focusing on Grammar and Punctuation Two lessons focussing on Writing, linked to the class/Negative and positive numbers Place value Estimation Rounding Long multiplication Fractions, Decimals and Percentages 2D and 3D shape Regular / irregular polygons Perimeter and Area Co-ordinates Translation and Rotation Reflection Ratio and Proportion Probability Statistics Digital /


ALGEBRA 2; AGENDA; DAY 47; MON. NOV. 02, 2015 (2 nd 9-Week) SEE BELL RINGER; PROBABILITY RULE; (CONDITIONAL) OBJECTIVE: SWBAT: MAFS.912.S-CP.1.5 – Recognize.

and intervals of increasing/decreasing of graphs. HOME LEARNING: ALGEBRA 2; AGENDA; DAY 63: MON. NOV. 30, 2015 (2 nd 9-Week)[ODD-DAY] DISTRICT TESTING [PERIODS 1 & 5] OBJECTIVE: SWBAT: MAFS.912.F-IF.2.4;2.5;3.9: Domain & Range, intercepts, maximum, minimum, increasing/decreasing intervals, end behavior, composition of functions, inverse functions. Statistics, probability/ CORE ALGEBRA II; THEN UNIT 5 SEQUENCES AND SERIES.., Click LESSON 1: “SEQUENCES”, WATCH VIDEO and complete pages 1 – 4; Submit pages /


Data Analysis, Statistics and Probability CHAPTER 17 Tina Rye Sloan To accompany Helping Children Learn Math10e, Reys et al. ©2012 John Wiley & Sons.

provide opportunities for children to simulate many trials with dice and spinners, and to integrate them into lessons: Go to the Math Forum Web site at http://mathforum.org/mathtools and then go to Math Topics. Under Probability and Statistics you will find lessons focusing on various models, including randomness and probability models. A number of applets (Bar Graph Sorter and Circle Graph) for different grade levels are available. National Library/


Chapter 1 Software and Software Engineering - Dual role of software - Software questions havent changed - A definition of software - Differences between.

approaches, the planner uses lessons learned to estimate an optimistic, most likely, and pessimistic size value for each/probability that a software application is operating according to requirements at a given point in time –Availability = [MTTF/ (MTTF + MTTR)] * 100% –Example: Avail. = [68 days / (68 days + 3 days)] * 100 % = 96% 623 Software Safety Focuses on identification and assessment of potential hazards to software operation It differs from software reliability –Software reliability uses statistical/


Comparing Two Proportions Lesson 1 and Lesson 2: Section 10.1.

Lesson 1 and Lesson 2: Section 10.1 objectives Lesson 1:  Describe the characteristics of the sampling distribution of  Calculate the probabilities using the sampling distribution of  Determine whether the conditions for performing inference are met.  Construct and interpret a confidence interval to compare two proportions. Lesson/the two studies, Apple introduced the iPod. If the results of the test are statistically significant, can we blame iPods for the increased hearing loss in teenagers? example 4:/


AP Stats Chapter 4 Part 3 Displaying and Summarizing Quantitative Data.

Plot your data Dotplot, Stemplot, Histogram Interpret what you see: Shape, Outliers, Center, Spread ©2013 All rights reserved. CCSS 6 th Grade Statistics and Probability 2.0 Describe the distribution of a data set. Lesson to be used by EDI-trained teachers only. 1. Shape: Center: Spread: 2. Shape: Center: Spread: The distribution of a data set shows the arrangement of values in the/


Chapter 4 Displaying and Summarizing Quantitative Data.

Plot your data Dotplot, Stemplot, Histogram Interpret what you see: Shape, Outliers, Center, Spread ©2013 All rights reserved. CCSS 6 th Grade Statistics and Probability 2.0 Describe the distribution of a data set. Lesson to be used by EDI-trained teachers only. 1. Shape: Center: Spread: 2. Shape: Center: Spread: The distribution of a data set shows the arrangement of values in the/


Contents of today’s lesson 1.Frequentist probabilities of Poisson-distributed data -with and without nuisances 2.Weighted average in presence of correlations.

today’s lesson 1.Frequentist probabilities of Poisson-distributed data -with and without nuisances 2.Weighted average in presence of correlations -Peeles pertinent puzzle 3.Finding the right model: Fishers F-test 4.Confidence intervals: the Neyman construction -bounded parameter, Gaussian measurement -flip-flopping and undercoverage 5.Hypothesis testing and the Higgs Search – Bump hunting – Look-elsewhere effect – The LHC Higgs search test statistic 1 – Probabilities of/


Design and Data Analysis in Psychology I English group (A) Salvador Chacón Moscoso Susana Sanduvete Chaves Milagrosa Sánchez Martín School of Psychology.

Milagrosa Sánchez Martín School of Psychology Dpt. Experimental Psychology 1 Lesson 5 Sampling and sampling distribution 2  The statistical inference presents two categories: Estimation theory (lesson 6):  Given an index in the sample, the aim is /1. Sampling distribution of the mean. Standardization Sample Sampling distribution Population  Standardization allows to calculate probabilities (if you know the probability model that has the distribution). We can consider normal distribution when n≥30. 26 4.1./


AP STATISTICS LESSON 6 - 1 THE IDEA OF PROBABILITY.

AP STATISTICS LESSON 6 - 1 THE IDEA OF PROBABILITY ESSENTIAL QUESTION: How is probability used in Statistics? Objectives:  To develop a working understanding of Probability.  To understand what is meant by “Random,” and what it’s characteristics are in the long run. Introduction Probability is a branch of mathematics that describes the pattern of chance outcomes. Probability is a branch of mathematics that describes the pattern of chance outcomes. The/


Independent and Dependent Events

the probability of getting a cold reduced, increased, or not affected by the vitamins? In this lesson, we will learn how to answer this and other similar questions.             Theory – Intro flipping a coin and getting heads rolling a die and getting 2 Event A and /in PreCalc 40S, and 30% failed Math 101. Statistics show that 10% of the students had an A in PreCalc 40S and still failed Math 101. Are getting an A in PreCalc and failing Math 101 independent events? Solution: Let A and B represent the /


Utah Association of Local Health Departments

Sampling Methods Classified as either Probability or Non-Probability. Probability samples, each member of the population has a known non-zero probability of being selected. The advantage of probability sampling is that sampling error / of lies: Lies, Damned Lies, and Statistics Descriptive Statistics The majority of our data collection will be done through sampling Populations versus Samples Population parameters: μ and σ Sample statistics: Х and s Descriptive Statistics Measures of Central Tendency: Mean – /


CE 4101W-01 Project Management and Economics

At project start Over and over, repeatedly, again and again, until project end Risk Management Probability of risk occurrence (P) How likely is the risk event? Can be classified by judgment Can be classified by statistical tools Risk Management Impact /does organization structure affect how this is handled? Project Closure – Post Mortem Gather lessons learned Sometimes called “post mortem” Analyze what went right and what went wrong on project Analyze what would have been done differently in hindsight/


Concepts of Statistical Inference: A Randomization-Based Curriculum Allan Rossman, Beth Chance, John Holcomb Cal Poly – San Luis Obispo, Cleveland State.

/Dolp hins/Dolphins.html http://www.rossmanchance.com/applets/Dolp hins/Dolphins.html 14 Conclusion Experimental result is statistically significant  And what is the logic behind that? Observed result very unlikely to occur by chance (random assignment) alone/in the details” Conclusions/Lessons Learned Don’t overlook null model in the simulation Simulation vs. Real study Plausible vs. Possible How much worry about being a tail probability How much worry about p-value = probability that null hypothesis is /


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