AP Stats Exam Review: Probability

Slides:



Advertisements
Similar presentations
Chapter – 5.4: The Normal Model
Advertisements

CHAPTER 13: Binomial Distributions
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter The Normal Probability Distribution 7.
Biostatistics Unit 4 - Probability.
How do I know which distribution to use?
Discrete Probability Distribution
Statistics Normal Probability Distributions Chapter 6 Example Problems.
PROBABILITY DISTRIBUTIONS
Chapter 5 Sampling Distributions
Normal Distribution as Approximation to Binomial Distribution
HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 8 Continuous.
AP Statistics Chapter 9 Notes.
Chapter 8 The Binomial and Geometric Distributions YMS 8.1
Copyright ©2011 Nelson Education Limited The Normal Probability Distribution CHAPTER 6.
Probability The definition – probability of an Event Applies only to the special case when 1.The sample space has a finite no.of outcomes, and 2.Each.
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 11 Section 1 – Slide 1 of 34 Chapter 11 Section 1 Random Variables.
Follow-Up on Yesterday’s last problem. Then we’ll review. Sit in the groups below Brendan and Tim are playing in an MB golf tournament. Their scores vary.
Day 2 Review Chapters 5 – 7 Probability, Random Variables, Sampling Distributions.
Random Variables Numerical Quantities whose values are determine by the outcome of a random experiment.
AP Statistics Exam Review
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Chapter 6. Continuous Random Variables Reminder: Continuous random variable.
MTH3003 PJJ SEM I 2015/2016.  ASSIGNMENT :25% Assignment 1 (10%) Assignment 2 (15%)  Mid exam :30% Part A (Objective) Part B (Subjective)  Final Exam:
Biostat. 200 Review slides Week 1-3. Recap: Probability.
x For every x 1. 0 ≤ P(x) ≤ 1 2. ∑P(x)=1 #defects01234 p(x) ?
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by.
AP Statistics Chapter 2 Notes. Measures of Relative Standing Percentiles The percent of data that lies at or below a particular value. e.g. standardized.
Bernoulli Trials Two Possible Outcomes –Success, with probability p –Failure, with probability q = 1  p Trials are independent.
The Normal Distribution
Copyright © 2014, 2013, 2010 and 2007 Pearson Education, Inc. Chapter The Normal Probability Distribution 7.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Chapter 6 Continuous Random Variables.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 6 Random Variables 6.3 Binomial and Geometric.
AP Statistics Chapter 16. Discrete Random Variables A discrete random variable X has a countable number of possible values. The probability distribution.
Unit 4 Review. Starter Write the characteristics of the binomial setting. What is the difference between the binomial setting and the geometric setting?
AP Stats Review: Probability Unit Unit #2 – Chapters 6, 7, and Section
Review Day 2 May 4 th Probability Events are independent if the outcome of one event does not influence the outcome of any other event Events are.
AP Stats Exam Review: Probability TeacherWeb.com 1.
Unit 3: Probability.  You will need to be able to describe how you will perform a simulation  Create a correspondence between random numbers and outcomes.
Simulations and Normal Distribution Week 4. Simulations Probability Exploration Tool.
Section 6.2 Binomial Distribution
MATB344 Applied Statistics
CHAPTER 6 Random Variables
The Normal Probability Distribution
Binomial and Geometric Random Variables
CHAPTER 14: Binomial Distributions*
CHAPTER 6 Random Variables
Chapter 6. Continuous Random Variables
Mrs. Daniel Alonzo & Tracy Mourning Sr. High
Z-scores & Shifting Data
Chapter 5 Sampling Distributions
BIOS 501 Lecture 3 Binomial and Normal Distribution
Chapter 5 Sampling Distributions
Chapter 5 Sampling Distributions
Week 8 Chapter 14. Random Variables.
Mrs. Daniel Alonzo & Tracy Mourning Sr. High
Advanced Placement Statistics
CHAPTER 6 Random Variables
Chapter 5 Sampling Distributions
Probability Key Questions
Review
Chapter 6: Random Variables
Chapter 5 Sampling Distributions
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
The Normal Curve Section 7.1 & 7.2.
AP Statistics Chapter 16 Notes.
Probability.
CHAPTER 6 Random Variables
Day 46 Agenda: DG minutes.
Standard Deviation and the Normal Model
Probability and Statistics
Presentation transcript:

AP Stats Exam Review: Probability etharrington@wcpss.net TeacherWeb.com

The Normal Distribution Symmetric, mound-shaped Mean = median Area under the curve = 100% 68-95-99.7% rule - sometimes called the Empirical Rule Crazy formula – no need to know it!

The 68-95-99.7% Rule The following shows what the 68-95-99.7 Rule tells us:

For example: 1998 #4 (partial): A company is considering implementing one of two quality control plans – plan P and plan Q – for monitoring the weights of car batteries that it manufactures. If the manufacturing process is working properly, the battery weights are approximately Normally distributed with mean = 2.7 lbs and standard deviation = 0.1 lbs. (a) Quality Control Plan P calls for rejecting a battery as defective if its weight falls more than 1 standard deviation below the mean. If a battery is selected at random, what is the probability that it will be rejected by plan P?

This is the area we’re interested in! Solution to 1998 #4 (a): This is the area we’re interested in! The answer: About 16%

Finding Normal Probabilities When a data value doesn’t fall exactly 1, 2, or 3 standard deviations from the mean, we used to use a Z Table to find the area to the left of the value. Now, we mostly just use our calculators to find areas under a normal curve.

Finding Normal Probabilities (cont.) Table Z is the standard Normal table. We have to convert our values to z-scores before using the table. The figure below shows how to find the area to the left when we have a z-score of 1.80:

Calculator Use Normalcdf – gives area under a normal curve. You can use z-scores or raw data values: normcdf (lower, upper, mean, st dev) invNorm – gives z-score or raw data value associated with a percentile: invNorm(area to left as decimal, mean, st dev) Provide complete communication on FR questions – use drawings and/or identify the values in your calculator syntax.

For example, 2003 #3: #3: Men’s shirt sizes are determined by their neck sizes. Suppose that men’s neck sizes are approximately normally distributed with mean 15.7” and standard deviation 0.7”. A retailer sells men’s shirts in sizes S, M, L, and XL, where the shirt sizes are defined in the table below: Shirt Size Neck Size S 14”-15” (b) Using a sketch of a normal M 15”-16” curve, illustrate and calculate L 16”-17” the proportion of men whose XL 17”-18” shirt size is Medium.

Solution to 2003, #3 (b) (shirt sizes): Normalcdf (15, 16, 15.7, 0.7) = 0.5072

Question: When do we multiply? Answer: when we want to find the probability of a series of consecutive events Example: We just found that 0.5072 of all men wear size Medium shirts. Suppose we select 3 men at random from a huge population. What is the probability that all three of them wear Medium shirts? Answer:

The BINOMIAL Distribution! What if we are counting how many successes will occur in repeated trials? The BINOMIAL Distribution! (1) Fixed number of trials (“n”) (2) Fixed probability of success on each trial (“p”) (3) Two outcomes per trials (success or failure) (4) Trials are independent of each other

Binomial on the Calculator Binomialpdf(n,p,x-value) > only the probability of that specific x-value Binomialcdf (n,p,x-value) > the sum of the probabilities of x=0 up through and including that specific x-value Example: We found that 50.72% of all shirts are mediums. If we select 12 shirts at random, what is the probability that… Exactly 4 of them are Mediums? No more than 4 of them are Mediums? At least 4 of them are Mediums? 0.1139 0.1802 0.9337

More Binomial! For a Binomially-distributed variable, the parameters are… “n” > the number of repeated trials “p” > the success probability in each trials If X is a Binomially-distributed variable, then µ = E(x) = np & σ = sqrt {n·p·(1-p)} ** We found that 0.5072 of men wear Medium shirts. If n=12 are repeatedly sampled, what is the mean and standard deviation of X, the # of Mediums that we find?

Question: When do we add? Answer: when the question can be construed as an “OR” situation Example: We found that 0.5072 of all men wear size Medium shirts. Out of a random sample of 12 men, what is the probability that either 3 or 4 (exactly) are Mediums? Pr(x=3 or x=4) = Pr(x=3) + Pr(x=4) Do each of these as a binomialpdf = 0.049 + 0.1139 = 0.1629

When ADDING, be careful about over-counting! Example: Using the chart on the board, if I select one bird at random, what is the probability that the bird I select is either a chick or is yellow? Pr(chick or yellow) = Pr(chick) + Pr(yellow) minus Pr(both chick & yellow) = 18/25 + 13/25 – 8/25 = 23/25

Note: If there hadn’t been any yellow chicks, then there wouldn’t be anything to subtract, because we wouldn’t have overcounted. The characteristics of “yellow” and “chick” would then be called MUTUALLY EXCLUSIVE.

Combining Random Variables X+Y or X-Y Means will be added or subtracted just like the random variables Variances will be added ONLY IF VARIABLES ARE INDEPENDENT! Remember: Variance = standard deviation squared ADD variances even if subtracting variables

Simulations When describing a simulation, be sure to include details about: How you will assign random digits to represent outcomes How you will move through the table How duplicates will be handled What will be recorded for each trial When will the simulation be stopped

The 2017 A.P. Statistics Exam! Thursday, May 11, at noon