Today (1/19/15) Learning objectives:

Slides:



Advertisements
Similar presentations
AP Statistics 51 Days until the AP Exam
Advertisements

NORMAL OR GAUSSIAN DISTRIBUTION Chapter 5. General Normal Distribution Two parameter distribution with a pdf given by:
STA291 Statistical Methods Lecture 13. Last time … Notions of: o Random variable, its o expected value, o variance, and o standard deviation 2.
Physics 114: Lecture 9 Probability Density Functions Dale E. Gary NJIT Physics Department.
Binomial Distributions
Randomized Algorithms Randomized Algorithms CS648 Lecture 8 Tools for bounding deviation of a random variable Markov’s Inequality Chernoff Bound Lecture.
Engineering experiments involve the measuring of the dependent variable as the independent one has been altered, so as to determine the relationship between.
Review of Basic Probability and Statistics
Descriptive statistics Experiment  Data  Sample Statistics Sample mean Sample variance Normalize sample variance by N-1 Standard deviation goes as square-root.
Maximum likelihood Conditional distribution and likelihood Maximum likelihood estimations Information in the data and likelihood Observed and Fisher’s.
Probability theory 2010 Outline  The need for transforms  Probability-generating function  Moment-generating function  Characteristic function  Applications.
K. Desch – Statistical methods of data analysis SS10 2. Probability 2.3 Joint p.d.f.´s of several random variables Examples: Experiment yields several.
511 Friday Feb Math/Stat 511 R. Sharpley Lecture #15: Computer Simulations of Probabilistic Models.
Continuous Random Variables and Probability Distributions
Math Minutes 1/20/ Write the equation of the line.
Statistical Treatment of Data Significant Figures : number of digits know with certainty + the first in doubt. Rounding off: use the same number of significant.
The joint probability distribution function of X and Y is denoted by f XY (x,y). The marginal probability distribution function of X, f X (x) is obtained.
Maximum likelihood (ML)
The Lognormal Distribution
Lecture II-2: Probability Review
Continuous Probability Distribution  A continuous random variables (RV) has infinitely many possible outcomes  Probability is conveyed for a range of.
Standard error of estimate & Confidence interval.
Distribution Function properties. Density Function – We define the derivative of the distribution function F X (x) as the probability density function.
CIS 2033 based on Dekking et al. A Modern Introduction to Probability and Statistics, 2007 Instructor Longin Jan Latecki Chapter 7: Expectation and variance.
The Binomial Distribution. Binomial Experiment.
6.3 (Green) Approximate Binomial Distributions PERFORMANCE EXAM: Friday! (topics are posted online)
Poisson Random Variable Provides model for data that represent the number of occurrences of a specified event in a given unit of time X represents the.
Bernoulli Trials Two Possible Outcomes –Success, with probability p –Failure, with probability q = 1  p Trials are independent.
Copyright © 2014 Pearson Education, Inc. All rights reserved Chapter 6 Modeling Random Events: The Normal and Binomial Models.
AP Statistics Chapter 8 Notes. The Binomial Setting If you roll a die 20 times, how many times will you roll a 4? Will you always roll a 4 that many times?
Chapter 5.6 From DeGroot & Schervish. Uniform Distribution.
Chapter 7 Sampling and Sampling Distributions ©. Simple Random Sample simple random sample Suppose that we want to select a sample of n objects from a.
1 Since everything is a reflection of our minds, everything can be changed by our minds.
ELEC 303 – Random Signals Lecture 18 – Classical Statistical Inference, Dr. Farinaz Koushanfar ECE Dept., Rice University Nov 4, 2010.
Math b (Discrete) Random Variables, Binomial Distribution.
Lecture 9. Continuous Probability Distributions David R. Merrell Intermediate Empirical Methods for Public Policy and Management.
Probability Distributions, Discrete Random Variables
Q1: Standard Deviation is a measure of what? CenterSpreadShape.
1 Keep Life Simple! We live and work and dream, Each has his little scheme, Sometimes we laugh; sometimes we cry, And thus the days go by.
Continuous Random Variables and Probability Distributions
Chapter 20 Statistical Considerations Lecture Slides The McGraw-Hill Companies © 2012.
+ Chapter 8 Day 3. + Warm - Up Shelly is a telemarketer selling cookies over the phone. When a customer picks up the phone, she sells cookies 25% of the.
R. Kass/W03 P416 Lecture 5 l Suppose we are trying to measure the true value of some quantity (x T ). u We make repeated measurements of this quantity.
Course Review. Distributions What are the important aspects needed to describe a distribution of one variable? List three types of graphs that could be.
Sums of Random Variables and Long-Term Averages Sums of R.V. ‘s S n = X 1 + X X n of course.
Continuous Random Variables
MTH 161: Introduction To Statistics
Cumulative distribution functions and expected values
Propagating Uncertainty In POMDP Value Iteration with Gaussian Process
Statistical Methods For Engineers
Review of Hypothesis Testing
Today (1/21/15) Learning objectives:
Lecture Slides Elementary Statistics Twelfth Edition
The Binomial Distribution
ASV Chapters 1 - Sample Spaces and Probabilities
Lecture Slides Elementary Statistics Twelfth Edition
3.2 Approximating Non-Linear Functions
2.3 Estimating PDFs and PDF Parameters
Continuous distributions
5.1 Introduction to Curve Fitting why do we fit data to a function?
3.0 Functions of One Random Variable
3.3 Experimental Standard Deviation
Continuous Random Variables
Virtual University of Pakistan
Handout Ch 4 實習.
Bernoulli Trials Two Possible Outcomes Trials are independent.
The Binomial Distributions
Berlin Chen Department of Computer Science & Information Engineering
Introduction to Probability: Solutions for Quizzes 4 and 5
Statistics 101 Chapter 8 Section 8.1 c and d.
Presentation transcript:

Today (1/19/15) Learning objectives: propagation of moments with linear and nonlinear functions. Given y=(x) and a distribution in x with x and x, what are y and y? determining the pdf for linear and non-linear functions. Given y=(x) and a distribution in x according to the pdf f(x), what is g(y)? 2.0 : 1/11

Question 1 Consider ions passing through a pinhole and hitting a 1D detector as shown below. Given that the angular probability distribution of ions from the pinhole is given by f() = cos(), find the functional form of the pdf, g(y), measured at the linear strip detector. y L  where 0    /2 2.0 : 4/11

Question 2 Consider an MS experiment based on ion counting with a swept source (e.g., quadrupole). In a given sweep, a count is recorded every time one or more ions is detected in a given m/z channel, such that the measured counts will be binomially distributed. -Derive an expression to recover the mean number of ions µ in a single sweep from the measured probability of observing a count p. -What value of p corresponds to a mean bias of 1% from the true number of counts? 2.0 : 1/11

Thursday (1/21/15) Independent reading: 3.2 – Approximating nonlinear functions In-class lecture for 1/21/15: 3.1 – Sums of random variables 3.3 – Experimental standard deviation 2.0 : 1/11