Converting to the Standard Normal rv: Exponential PDF and CDF for x 0 Chapter 7: expected value of x

Size: px
Start display at page:

Download "Converting to the Standard Normal rv: Exponential PDF and CDF for x 0 Chapter 7: expected value of x"

Transcription

1 Key Formula Sheet ASU ECN 22 ASWCC Chapter : no key formulas Chapter 2: Relative Frequency=freq of the class/n Approx Class Width: =(largest value-smallest value) /number of classes Chapter 3: sample and population means x = x i /n and µ = x i /N Weighted mean and geometric mean Chapter 4 continued: P(A B) = P(A) + P(B) P(A B) P(A B) = P(A B) P(B) P(A B) = P(B)P(A B) = P(A)P(B A) Multiplication Law for Independent Events x = w i x i /w i and xg = [(x)(x2) (xn)] /n P(A B) = P(B)P(A) Interquartile Range: IQR = Q3 Q Population and sample variance σ 2 = (x i µ) 2 N and s 2 = (x i x) 2 n Population and sample standard deviation σ = σ 2 and s = s 2 Coefficient of Variation ( Standard deviation Mean z-score: z i = x i x s Population and Sample Covariance σxy = (x i µx)(y i µy N 00 ) % and sxy = (x i x)(y i ȳ n Population and Sample Pearson Correlation ρxy = σxy/(σxσy) and rxy = sxy/(sxsy) Bayes Theorem P(A i B) = P(A i )P(B A i ) P(A)P(B A) + P(A2)P(B A2) + + P(An)P(B An) Chapter 5: Discrete Uniform Probability Mass Function: f (x) = /n Expected Value of a Discrete R V: E(x) = µ = x f (x) Variance of a Discrete R V: Var(x) = σ 2 = (x µ) 2 f (x) Number of Experimental Outcomes Providing Exactly x Successes in n Trials ( n x ) = n! x!(n x)! Binomial Probability Mass Function ( ) n P(X = x) = f (x) = p x ( p) (n x) x Chapter 4: Counting Rule for Combinations ( ) N C n N N! = = n n!(n n)! Counting Rule for Permutations ( ) P n N = n! N n = N! (N n)! Expected Value for Binomial Distribution: E(x) = µ = np Variance for Binomial Distr: Var(x) = σ 2 = np( p) Poisson Probability Mass Function: P(X = x µ) = f (x) = µx e µ Hypergeometric Probability Mass Function and Expected Value: x! Probability Rules: P(A) = P(A C ) f (x) = (r x )(N r n x ) N ( N n ) and E(x) = µ = nr Chapter 5 continued: Variance for the Hypergeometric Distribution: Var(x) = σ 2 = n ( r N )( r N )( N n N ) Chapter 6: Uniform PDF f (x) = { b a if a x b 0 otherwise Normal PDF The density function is f (x) = ( exp 2πσ 2 (x ) µ)2 2σ 2 Converting to the Standard Normal rv: z = x µ σ Exponential PDF and CDF for x 0 f (x) = µ e x/µ and P(x x0) = e x 0/µ Chapter 7: expected value of x E( x) = µ Standard Deviation of x (Standard Error) σx = σ n Expected Value and Std Dev (Standard Error) of p E( p) = p and σ p = p( p) Finite Pop Correction Factor: (N n)/(n ) Chapter 8: Interval Estimate of Population Mean, σ known and unknown n x ± z α/2 n and x ± t α/2 n σ s Necessary Sample Size for Interval Estimate of µ n = (z α/2) 2 σ 2 E 2

2 Chapter 8 continued: Interval Estimate of p p( p) p ± z α/2 n Necessary Sample Size for Interval Estimate of p n = (z α/2) 2 p ( p ) E 2 Chapter 9: Test Statistic for Hypothesis Tests About µ, σ known and unknown z = x µ 0 σ/ n and t = x µ 0 s/ n Test Stat for Hypothesis About p z = p p 0 p 0( p0) n Chapter 0: Point Estimate and Standard Error for Difference in Two Population Means x x2 and σ x x2 = σ 2 n + σ2 2 n2 Interval Estimate and Test Statistic for Difference in Two Means with Known Variances x x2 ± z α/2 σ x x2 = σ 2 n + σ2 2 n2 σ 2 and z = x x2 D0 n + σ2 2 n2 Interval Estimate and Test Statistic for Difference in Two Means with Unknown Variances x x2 ± t α/2 σ x x2 = s 2 n + s2 2 n2 s 2 and t = x x2 D0 n + s2 2 n2 Degrees of Freedom for t, Two Independent Random Samples d f = n ( s 2 n + s2 2 n2 ( s 2 ) 2 n + n2 ) 2 ( s 2 2 n2 ) 2 Chapter 0 continued: Test Statistic (Matched Samples) d t = µ d s d / n ANOVA Related: x j = n j i= x ij n j s 2 j = n j i= (x ij x j ) 2 n j x = k j= n j nt i= x ij MSTR = SSTR k SSTR = k n j ( x j x) 2 MSE = SSE j= nt k SSE = k (n j )s 2 j F = MSTR/MSE j= SST = k j= n j (x ij x) 2 SST=SSTR+SSE i= Chapter : not covered in this course Chapter 2: y = β0 + βx + ɛ E(y) = β0 + βx ŷ = b0 + bx b0 = ȳ b x b = (x i x)(y i ȳ) (x i x) 2 SSE = (y i = ŷ i ) 2 SST = (y i ȳ) 2 SSR = (ŷ i ȳ) 2 SST=SSR+SSE r 2 = SSR SST r xy = (sign of b) r 2 s 2 = MSE = SSE n 2 Standard Error of the Estimate, s = MSE σ b = σ s (xi x) 2 b = s t = b (xi x) 2 s b For simple regression, MSR = SSR because there is only one independent variable F = MSR MSE sŷ = s n + (x x) 2 (x x) 2 Confidence Interval for E(y ): ŷ α/a s ŷ s pred = s + n + (x x) 2 (x x) 2 Chapter 2 continued: Prediction Interval for y : ŷ ± t α/a s pred Residual for Observation i: y i ŷ i Chapter 3: y = β0 + βx + β2x2 + + βpxp + ɛ E(y) = β0 + βx + β2x2 + + βpxp ŷ = b0 + bx + b2x2 + + bpxp SST = SSR + SSE R 2 = SSR SST R 2 a = ( R 2 ) n n p MSR = SSR p MSE = SSE n p F = MSR MSE t = b i s bi Other Math Rule Reminders: e x = exp(x) ln = 0 lne = x! = (x)(x )(x 2) (2)() 0! = x 0 =

3 ECN22 Exam A Spring 205, ASU-COX Choose the best answer Do not write letters in the margin or communicate with other students in any way If you have a question note it on your exam and ask for clarification when your exam is returned In the meantime choose the best answer Neither the proctors nor Dr Cox will answer questions during the exam Dr Cox will post a key the day after the exam or in the case of the final exam the day after all finals are given Grades will be posted on Bb after scores are returned from the testing center Please check each question and possible answers thoroughly as questions at the bottom of a page sometimes run onto the next page Relax You studied You know the material You can nail it The sample size (a) can be larger than the population size (b) can be larger or smaller than the population size (c) is always smaller than the population size (d) is always equal to the size of the population 2 Data collected over several time periods are (a) time controlled data (b) time series data (c) crossectional data (d) time crossectional data 3 Statistical inference (a) is the same as Data and Statistics (b) refers to the process of drawing inferences about the sample based on the characteristics of the population (c) is the same as a census (d) is the process of drawing inferences about the population based on the information taken from the sample 4 Product brand is an example of (a) categorical data (b) quantitative data (c) either categorical or quantitative data 3

4 (d) time series data 5 Consider the data set below: customer information customer $ spent days since billing tenure Which variables are quantitative? (a) $ spent, days since billing, and tenure (b) customer and $ spent (c) customer, $ spent and tenure (d) they are all quantitative 6 What is true about the data set below: customer information customer $ spent days since billing tenure (a) the data are time series (b) the data are time series and cross-sectional (c) the data are cross-sectional (d) some variables are time-series and some are cross sectional 7 Which of the following is a quantitative variable (a) gender (b) education level (c) employment status (d) interest rate 4

5 8 What is the mode in the following example? (a) 3 (b) 7 (c) 37 (d) 49 9 The display below is an example of a (a) histogram (b) bar chart (c) scatter plot (d) pie chart 0 The display below has 5

6 (a) too many classes (b) the right number of classes (c) too few classes (d) a class range that is too narrow The relative frequency of platinum cards in the portfolio is: charge/credit card type card frequency relative frequency blue 43 green 78 gold 76 platinum 25 (a) (b) 59% (c) 80 (d) The relative frequency of blue cards in the portfolio is: charge/credit card type card frequency relative frequency blue 43 green 78 gold 76 platinum 25 6

7 (a) 66 (b) 42 (c) 34% (d) The cumulative frequency of credit card accounts with a balance less than $30,000? credit card balance card cum freq cum relative freq <= 9, ,000-9, ,000-29, , (a) 25 (b) 94% (c) 397 (d) The cumulative relative frequency of cards with a balance under $20,000 is: credit card balance card cum freq cum relative freq <= 9, ,000-9, ,000-29, , (a) 76 (b) 42 (c) 339 (d) 94 5 A set of credit card accounts have the following estimated probabilities of default: 067, 743, 042, 022, 03, 94, 00 Find the 30th percentile (a) 2 (b) 036 (c) 03 7

8 (d) The variance of a set of cereal prices is 38 The standard deviation is (a) 7 (b) 90 (c) 38 (d) 69 7 Find the z-score for an observation with a value of 722 when the mean is 86 and the variance is 227 (a) -6 (b) 6 (c) -292 (d) The average of 22, 25, 26, 29 is (a) 255 (b) 25 (c) 26 (d) The average is always greater than the standard deviation (a) true (b) false (c) true for ratio data but false for interval data (d) false for ratio data but true for interval data 20 Suppose the covariance between two variables is 57 while their individual standard deviations are 8 and 452 The correlation coefficient is: (a) 403 (b) 57 (c) 635 (d) 00 8

9 2 Suppose WP Carey has 4 internships lined up with local companies Suppose that there are 24 applicant for the internships that meet the GPA requirement; WP Carey will not select an applicant that does not meet the GPA requirement How many possible combinations of students filling the internships are there? (a) (b) (c) (d) A really big number not given here 22 A study by the Institute for Higher Education Policy found the values in the joint probability table below The underlying data are for former college students that had taken out student loans The table shows whether the student received a college degree versus whether they are successfully making their student loan payments What is the probability that a former student completed their degree? satisfactory delinquent total (a) 58 (b) 42 (c) 26 (d) 6 23 A study by the Institute for Higher Education Policy found the values in the joint probability table below The underlying data are for former college students that had taken out student loans The table shows whether the student received a college degree versus whether they are successfully making their student loan payments What is the probability that a former student completed their degree and is currently satisfactory on their loan payments? satisfactory delinquent total (a) 26 9

10 (b) 42 (c) 92 (d) A study by the Institute for Higher Education Policy found the values in the joint probability table below The underlying data are for former college students that had taken out student loans The table shows whether the student received a college degree versus whether they are successfully making their student loan payments What is the probability that a former student did not complete their degree and is currently delinquent on their loan payments? satisfactory delinquent total (a) 26 (b) 74 (c) 50 (d) Suppose you observe the following prices for cereals which are given in dollars, 3, 3, 5, 5, 7 where the $7 cereal is a high end organic granola Find the z-score of the organic granola (a) z=76, and it is an outlier (b) z=76, which means it is not an outlier (c) z=298, which means it is not an outlier (d) z=288, which means it is close to being an outlier 26 A study by the Institute for Higher Education Policy found the values in the joint probability table below The underlying data are for former college students that had taken out student loans The table shows whether the student received a college degree versus whether they are successfully making their student loan payments What is the probability that a former student did not complete their degree given that they are currently delinquent on their loan payments? satisfactory delinquent total

11 (a) 34 (b) 68 (c) 50 (d) Consider the Let s Make a Deal game we played in class Instead of three doors suppose that there are four doors, a, b, c and d What is the probability that you will win if you switch from your original guess after one door is opened? You can apply Bayes Rule or some other technique to solve this (a) /4 (b) /2 (c) 2/3 (d) 3/8 (e) 6/8 28 A study by the Institute for Higher Education Policy found the values in the joint probability table below The underlying data are for former college students that had taken out student loans The table shows whether the student received a college degree versus whether they are successfully making their student loan payments What is the probability that a former student with a loan is delinquent on their loan payments? satisfactory delinquent total (a) 6 (b) 34 (c) 50 (d) I have checked that my ID is bubbled in correctly If it is bubbled in incorrectly I will get this question wrong (a) True (b) False

12 Key c 2 b 3 d 4 a 5 a 6 c 7 d 8 c 9 d 0 c d To find the answer we need to complete the table Then we can read the answer out of the table charge/credit card type card frequency relative frequency blue green gold platinum d To find the answer we need to complete the table Then we can read the answer out of the table charge/credit card type card frequency relative frequency blue green gold platinum c The value is found from noting that there are a total of 422 observations and the values are found in the table: 2

13 credit card balance card cum freq cum relative freq <= 9, ,000-9, ,000-29, , a The value is found from noting that there are a total of 422 observations and the values are found in the table: credit card balance card cum freq cum relative freq <= 9, ,000-9, ,000-29, , c c n = 7 and p = 30 so the index point is i = (7)(30)/00 = 2 which we round up to 3 The third value is 03 6 a a 38 = 7 7 c c Find the z-score by using the formula and plugging in, z = = = a 9 b 20 c Compute: r = 57 (8)(452) = b b Use the formula ( ) 24 = 24! 4 0!4! = b b Take the value from the table satisfactory delinquent total

14 23 a a Take the value from the table satisfactory delinquent total d d Take the value from the table satisfactory delinquent total b b x = 66 and s = 589 so that z = = 76 and we would not consider it to be an outlier because 3 < z < 3 26 b b Take the values from the table to make the calculations P (A B) = P (A B) P (B) = 34 5 = 68 satisfactory delinquent total d d Suppose that you pick a (or any door) and switch to d (or any unrevealed door) after b or c is revealed (or whichever other two doors there are) P (d picked a (b or c revealed)) P (d)p (picked a (b or c revealed) d) = K where K = P (a)p (picked a (b or c rev) a) + P (b)p (picked a (b or c rev) b) + P (c)p (picked a (b or c rev) c) + P (d)p (picked a (b or c rev) d), = (/4)(/4) (/4)(/6) + (/4)(/8) + (/4)(/8) + (/4)(/4) = 3 8 4

15 A simpler way to get the same answer would be to fix the idea that an initial guess would give the prize door with probability /4 That means the other doors are right with probability 3/4 Then, after one is revealed there are only two left so picking one of them gives you (/2)(3/4)=3/8 as the probability you will get the right door 28 c c Take the value from the table satisfactory delinquent total a 5

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1 Chapter 1 1.1 Definitions Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2.

More information

Contents Part I Descriptive Statistics 1 Introduction and Framework Population, Sample, and Observations Variables Quali

Contents Part I Descriptive Statistics 1 Introduction and Framework Population, Sample, and Observations Variables Quali Part I Descriptive Statistics 1 Introduction and Framework... 3 1.1 Population, Sample, and Observations... 3 1.2 Variables.... 4 1.2.1 Qualitative and Quantitative Variables.... 5 1.2.2 Discrete and Continuous

More information

2011 Pearson Education, Inc

2011 Pearson Education, Inc Statistics for Business and Economics Chapter 4 Random Variables & Probability Distributions Content 1. Two Types of Random Variables 2. Probability Distributions for Discrete Random Variables 3. The Binomial

More information

Review for Final Exam Spring 2014 Jeremy Orloff and Jonathan Bloom

Review for Final Exam Spring 2014 Jeremy Orloff and Jonathan Bloom Review for Final Exam 18.05 Spring 2014 Jeremy Orloff and Jonathan Bloom THANK YOU!!!! JON!! PETER!! RUTHI!! ERIKA!! ALL OF YOU!!!! Probability Counting Sets Inclusion-exclusion principle Rule of product

More information

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise. Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x

More information

AP STATISTICS FALL SEMESTSER FINAL EXAM STUDY GUIDE

AP STATISTICS FALL SEMESTSER FINAL EXAM STUDY GUIDE AP STATISTICS Name: FALL SEMESTSER FINAL EXAM STUDY GUIDE Period: *Go over Vocabulary Notecards! *This is not a comprehensive review you still should look over your past notes, homework/practice, Quizzes,

More information

Math 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment

Math 2311 Bekki George Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Math 2311 Bekki George bekki@math.uh.edu Office Hours: MW 11am to 12:45pm in 639 PGH Online Thursdays 4-5:30pm And by appointment Class webpage: http://www.math.uh.edu/~bekki/math2311.html Math 2311 Class

More information

Chapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc.

Chapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc. 1 3.1 Describing Variation Stem-and-Leaf Display Easy to find percentiles of the data; see page 69 2 Plot of Data in Time Order Marginal plot produced by MINITAB Also called a run chart 3 Histograms Useful

More information

1/2 2. Mean & variance. Mean & standard deviation

1/2 2. Mean & variance. Mean & standard deviation Question # 1 of 10 ( Start time: 09:46:03 PM ) Total Marks: 1 The probability distribution of X is given below. x: 0 1 2 3 4 p(x): 0.73? 0.06 0.04 0.01 What is the value of missing probability? 0.54 0.16

More information

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial Lecture 23 STAT 225 Introduction to Probability Models April 4, 2014 approximation Whitney Huang Purdue University 23.1 Agenda 1 approximation 2 approximation 23.2 Characteristics of the random variable:

More information

Subject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018

Subject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018 ` Subject CS1 Actuarial Statistics 1 Core Principles Syllabus for the 2019 exams 1 June 2018 Copyright in this Core Reading is the property of the Institute and Faculty of Actuaries who are the sole distributors.

More information

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance

Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Estimating parameters 5.3 Confidence Intervals 5.4 Sample Variance Prof. Tesler Math 186 Winter 2017 Prof. Tesler Ch. 5: Confidence Intervals, Sample Variance Math 186 / Winter 2017 1 / 29 Estimating parameters

More information

4.2 Bernoulli Trials and Binomial Distributions

4.2 Bernoulli Trials and Binomial Distributions Arkansas Tech University MATH 3513: Applied Statistics I Dr. Marcel B. Finan 4.2 Bernoulli Trials and Binomial Distributions A Bernoulli trial 1 is an experiment with exactly two outcomes: Success and

More information

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions April 9th, 2018 Lecture 20: Special distributions Week 1 Chapter 1: Axioms of probability Week 2 Chapter 3: Conditional probability and independence Week 4 Chapters 4, 6: Random variables Week 9 Chapter

More information

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the

Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the Using the Central Limit Theorem It is important for you to understand when to use the CLT. If you are being asked to find the probability of the mean, use the CLT for the mean. If you are being asked to

More information

Review of the Topics for Midterm I

Review of the Topics for Midterm I Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the

More information

CHAPTER 2 Describing Data: Numerical

CHAPTER 2 Describing Data: Numerical CHAPTER Multiple-Choice Questions 1. A scatter plot can illustrate all of the following except: A) the median of each of the two variables B) the range of each of the two variables C) an indication of

More information

Exam 2 Spring 2015 Statistics for Applications 4/9/2015

Exam 2 Spring 2015 Statistics for Applications 4/9/2015 18.443 Exam 2 Spring 2015 Statistics for Applications 4/9/2015 1. True or False (and state why). (a). The significance level of a statistical test is not equal to the probability that the null hypothesis

More information

Contents. An Overview of Statistical Applications CHAPTER 1. Contents (ix) Preface... (vii)

Contents. An Overview of Statistical Applications CHAPTER 1. Contents (ix) Preface... (vii) Contents (ix) Contents Preface... (vii) CHAPTER 1 An Overview of Statistical Applications 1.1 Introduction... 1 1. Probability Functions and Statistics... 1..1 Discrete versus Continuous Functions... 1..

More information

Business Statistics 41000: Probability 3

Business Statistics 41000: Probability 3 Business Statistics 41000: Probability 3 Drew D. Creal University of Chicago, Booth School of Business February 7 and 8, 2014 1 Class information Drew D. Creal Email: dcreal@chicagobooth.edu Office: 404

More information

2 of PU_2015_375 Which of the following measures is more flexible when compared to other measures?

2 of PU_2015_375 Which of the following measures is more flexible when compared to other measures? PU M Sc Statistics 1 of 100 194 PU_2015_375 The population census period in India is for every:- quarterly Quinqennial year biannual Decennial year 2 of 100 105 PU_2015_375 Which of the following measures

More information

5.2 Random Variables, Probability Histograms and Probability Distributions

5.2 Random Variables, Probability Histograms and Probability Distributions Chapter 5 5.2 Random Variables, Probability Histograms and Probability Distributions A random variable (r.v.) can be either continuous or discrete. It takes on the possible values of an experiment. It

More information

Tests for the Difference Between Two Linear Regression Intercepts

Tests for the Difference Between Two Linear Regression Intercepts Chapter 853 Tests for the Difference Between Two Linear Regression Intercepts Introduction Linear regression is a commonly used procedure in statistical analysis. One of the main objectives in linear regression

More information

Basic Procedure for Histograms

Basic Procedure for Histograms Basic Procedure for Histograms 1. Compute the range of observations (min. & max. value) 2. Choose an initial # of classes (most likely based on the range of values, try and find a number of classes that

More information

STA 103: Final Exam. Print clearly on this exam. Only correct solutions that can be read will be given credit.

STA 103: Final Exam. Print clearly on this exam. Only correct solutions that can be read will be given credit. STA 103: Final Exam June 26, 2008 Name: } {{ } by writing my name i swear by the honor code Read all of the following information before starting the exam: Print clearly on this exam. Only correct solutions

More information

Statistics for Managers Using Microsoft Excel 7 th Edition

Statistics for Managers Using Microsoft Excel 7 th Edition Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 5 Discrete Probability Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 014 Pearson Education, Inc. Chap 5-1 Learning

More information

8.1 Binomial Distributions

8.1 Binomial Distributions 8.1 Binomial Distributions The Binomial Setting The 4 Conditions of a Binomial Setting: 1.Each observation falls into 1 of 2 categories ( success or fail ) 2 2.There is a fixed # n of observations. 3.All

More information

Problem max points points scored Total 120. Do all 6 problems.

Problem max points points scored Total 120. Do all 6 problems. Solutions to (modified) practice exam 4 Statistics 224 Practice exam 4 FINAL Your Name Friday 12/21/07 Professor Michael Iltis (Lecture 2) Discussion section (circle yours) : section: 321 (3:30 pm M) 322

More information

BIOS 4120: Introduction to Biostatistics Breheny. Lab #7. I. Binomial Distribution. RCode: dbinom(x, size, prob) binom.test(x, n, p = 0.

BIOS 4120: Introduction to Biostatistics Breheny. Lab #7. I. Binomial Distribution. RCode: dbinom(x, size, prob) binom.test(x, n, p = 0. BIOS 4120: Introduction to Biostatistics Breheny Lab #7 I. Binomial Distribution P(X = k) = ( n k )pk (1 p) n k RCode: dbinom(x, size, prob) binom.test(x, n, p = 0.5) P(X < K) = P(X = 0) + P(X = 1) + +

More information

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4

7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -

More information

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4

Week 7. Texas A& M University. Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Week 7 Oğuz Gezmiş Texas A& M University Department of Mathematics Texas A& M University, College Station Section 3.2, 3.3 and 3.4 Oğuz Gezmiş (TAMU) Topics in Contemporary Mathematics II Week7 1 / 19

More information

1.017/1.010 Class 19 Analysis of Variance

1.017/1.010 Class 19 Analysis of Variance .07/.00 Class 9 Analysis of Variance Concepts and Definitions Objective: dentify factors responsible for variability in observed data Specify one or more factors that could account for variability (e.g.

More information

INF FALL NATURAL LANGUAGE PROCESSING. Jan Tore Lønning, Lecture 3, 1.9

INF FALL NATURAL LANGUAGE PROCESSING. Jan Tore Lønning, Lecture 3, 1.9 1 INF5830 2015 FALL NATURAL LANGUAGE PROCESSING Jan Tore Lønning, Lecture 3, 1.9 Today: More statistics 2 Recap Probability distributions Categorical distributions Bernoulli trial Binomial distribution

More information

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial. Lecture 21,22, 23 Text: A Course in Probability by Weiss 8.5 STAT 225 Introduction to Probability Models March 31, 2014 Standard Sums of Whitney Huang Purdue University 21,22, 23.1 Agenda 1 2 Standard

More information

Describing Uncertain Variables

Describing Uncertain Variables Describing Uncertain Variables L7 Uncertainty in Variables Uncertainty in concepts and models Uncertainty in variables Lack of precision Lack of knowledge Variability in space/time Describing Uncertainty

More information

Statistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient

Statistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient Statistics & Flood Frequency Chapter 3 Dr. Philip B. Bedient Predicting FLOODS Flood Frequency Analysis n Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs)

More information

4 Random Variables and Distributions

4 Random Variables and Distributions 4 Random Variables and Distributions Random variables A random variable assigns each outcome in a sample space. e.g. called a realization of that variable to Note: We ll usually denote a random variable

More information

Chapter 8 Homework Solutions Compiled by Joe Kahlig. speed(x) freq 25 x < x < x < x < x < x < 55 5

Chapter 8 Homework Solutions Compiled by Joe Kahlig. speed(x) freq 25 x < x < x < x < x < x < 55 5 H homework problems, C-copyright Joe Kahlig Chapter Solutions, Page Chapter Homework Solutions Compiled by Joe Kahlig. (a) finite discrete (b) infinite discrete (c) continuous (d) finite discrete (e) continuous.

More information

7 THE CENTRAL LIMIT THEOREM

7 THE CENTRAL LIMIT THEOREM CHAPTER 7 THE CENTRAL LIMIT THEOREM 373 7 THE CENTRAL LIMIT THEOREM Figure 7.1 If you want to figure out the distribution of the change people carry in their pockets, using the central limit theorem and

More information

This is very simple, just enter the sample into a list in the calculator and go to STAT CALC 1-Var Stats. You will get

This is very simple, just enter the sample into a list in the calculator and go to STAT CALC 1-Var Stats. You will get MATH 111: REVIEW FOR FINAL EXAM SUMMARY STATISTICS Spring 2005 exam: 1(A), 2(E), 3(C), 4(D) Comments: This is very simple, just enter the sample into a list in the calculator and go to STAT CALC 1-Var

More information

Chapter 7. Sampling Distributions and the Central Limit Theorem

Chapter 7. Sampling Distributions and the Central Limit Theorem Chapter 7. Sampling Distributions and the Central Limit Theorem 1 Introduction 2 Sampling Distributions related to the normal distribution 3 The central limit theorem 4 The normal approximation to binomial

More information

CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates

CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates A point estimate is a single number, a confidence interval provides additional information about the variability of the estimate Lower

More information

Commonly Used Distributions

Commonly Used Distributions Chapter 4: Commonly Used Distributions 1 Introduction Statistical inference involves drawing a sample from a population and analyzing the sample data to learn about the population. We often have some knowledge

More information

Chapter 4 Random Variables & Probability. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables

Chapter 4 Random Variables & Probability. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Chapter 4.5, 6, 8 Probability for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random variable =

More information

Econ 250 Fall Due at November 16. Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling

Econ 250 Fall Due at November 16. Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling Econ 250 Fall 2010 Due at November 16 Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling 1. Suppose a firm wishes to raise funds and there are a large number of independent financial

More information

INF FALL NATURAL LANGUAGE PROCESSING. Jan Tore Lønning, Lecture 3, 1.9

INF FALL NATURAL LANGUAGE PROCESSING. Jan Tore Lønning, Lecture 3, 1.9 INF5830 015 FALL NATURAL LANGUAGE PROCESSING Jan Tore Lønning, Lecture 3, 1.9 Today: More statistics Binomial distribution Continuous random variables/distributions Normal distribution Sampling and sampling

More information

Chapter 7. Sampling Distributions and the Central Limit Theorem

Chapter 7. Sampling Distributions and the Central Limit Theorem Chapter 7. Sampling Distributions and the Central Limit Theorem 1 Introduction 2 Sampling Distributions related to the normal distribution 3 The central limit theorem 4 The normal approximation to binomial

More information

Statistics, Measures of Central Tendency I

Statistics, Measures of Central Tendency I Statistics, Measures of Central Tendency I We are considering a random variable X with a probability distribution which has some parameters. We want to get an idea what these parameters are. We perfom

More information

Statistics and Probability

Statistics and Probability Statistics and Probability Continuous RVs (Normal); Confidence Intervals Outline Continuous random variables Normal distribution CLT Point estimation Confidence intervals http://www.isrec.isb-sib.ch/~darlene/geneve/

More information

Business Statistics 41000: Probability 4

Business Statistics 41000: Probability 4 Business Statistics 41000: Probability 4 Drew D. Creal University of Chicago, Booth School of Business February 14 and 15, 2014 1 Class information Drew D. Creal Email: dcreal@chicagobooth.edu Office:

More information

15.063: Communicating with Data Summer Recitation 4 Probability III

15.063: Communicating with Data Summer Recitation 4 Probability III 15.063: Communicating with Data Summer 2003 Recitation 4 Probability III Today s Content Normal RV Central Limit Theorem (CLT) Statistical Sampling 15.063, Summer '03 2 Normal Distribution Any normal RV

More information

Sampling and sampling distribution

Sampling and sampling distribution Sampling and sampling distribution September 12, 2017 STAT 101 Class 5 Slide 1 Outline of Topics 1 Sampling 2 Sampling distribution of a mean 3 Sampling distribution of a proportion STAT 101 Class 5 Slide

More information

CH 5 Normal Probability Distributions Properties of the Normal Distribution

CH 5 Normal Probability Distributions Properties of the Normal Distribution Properties of the Normal Distribution Example A friend that is always late. Let X represent the amount of minutes that pass from the moment you are suppose to meet your friend until the moment your friend

More information

4-1. Chapter 4. Commonly Used Distributions by The McGraw-Hill Companies, Inc. All rights reserved.

4-1. Chapter 4. Commonly Used Distributions by The McGraw-Hill Companies, Inc. All rights reserved. 4-1 Chapter 4 Commonly Used Distributions 2014 by The Companies, Inc. All rights reserved. Section 4.1: The Bernoulli Distribution 4-2 We use the Bernoulli distribution when we have an experiment which

More information

Chapter 4: Commonly Used Distributions. Statistics for Engineers and Scientists Fourth Edition William Navidi

Chapter 4: Commonly Used Distributions. Statistics for Engineers and Scientists Fourth Edition William Navidi Chapter 4: Commonly Used Distributions Statistics for Engineers and Scientists Fourth Edition William Navidi 2014 by Education. This is proprietary material solely for authorized instructor use. Not authorized

More information

The Binomial and Geometric Distributions. Chapter 8

The Binomial and Geometric Distributions. Chapter 8 The Binomial and Geometric Distributions Chapter 8 8.1 The Binomial Distribution A binomial experiment is statistical experiment that has the following properties: The experiment consists of n repeated

More information

Theoretical Foundations

Theoretical Foundations Theoretical Foundations Probabilities Monia Ranalli monia.ranalli@uniroma2.it Ranalli M. Theoretical Foundations - Probabilities 1 / 27 Objectives understand the probability basics quantify random phenomena

More information

The binomial distribution p314

The binomial distribution p314 The binomial distribution p314 Example: A biased coin (P(H) = p = 0.6) ) is tossed 5 times. Let X be the number of H s. Fine P(X = 2). This X is a binomial r. v. The binomial setting p314 1. There are

More information

Counting Basics. Venn diagrams

Counting Basics. Venn diagrams Counting Basics Sets Ways of specifying sets Union and intersection Universal set and complements Empty set and disjoint sets Venn diagrams Counting Inclusion-exclusion Multiplication principle Addition

More information

Standard Normal, Inverse Normal and Sampling Distributions

Standard Normal, Inverse Normal and Sampling Distributions Standard Normal, Inverse Normal and Sampling Distributions Section 5.5 & 6.6 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 9-3339 Cathy

More information

Section Distributions of Random Variables

Section Distributions of Random Variables Section 8.1 - Distributions of Random Variables Definition: A random variable is a rule that assigns a number to each outcome of an experiment. Example 1: Suppose we toss a coin three times. Then we could

More information

KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI

KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI 88 P a g e B S ( B B A ) S y l l a b u s KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI Course Title : STATISTICS Course Number : BA(BS) 532 Credit Hours : 03 Course 1. Statistical

More information

1 Inferential Statistic

1 Inferential Statistic 1 Inferential Statistic Population versus Sample, parameter versus statistic A population is the set of all individuals the researcher intends to learn about. A sample is a subset of the population and

More information

STATISTICAL DISTRIBUTIONS AND THE CALCULATOR

STATISTICAL DISTRIBUTIONS AND THE CALCULATOR STATISTICAL DISTRIBUTIONS AND THE CALCULATOR 1. Basic data sets a. Measures of Center - Mean ( ): average of all values. Characteristic: non-resistant is affected by skew and outliers. - Median: Either

More information

Statistics 431 Spring 2007 P. Shaman. Preliminaries

Statistics 431 Spring 2007 P. Shaman. Preliminaries Statistics 4 Spring 007 P. Shaman The Binomial Distribution Preliminaries A binomial experiment is defined by the following conditions: A sequence of n trials is conducted, with each trial having two possible

More information

PROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS

PROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS PROBABILITY AND STATISTICS CHAPTER 4 NOTES DISCRETE PROBABILITY DISTRIBUTIONS I. INTRODUCTION TO RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS A. Random Variables 1. A random variable x represents a value

More information

DATA SUMMARIZATION AND VISUALIZATION

DATA SUMMARIZATION AND VISUALIZATION APPENDIX DATA SUMMARIZATION AND VISUALIZATION PART 1 SUMMARIZATION 1: BUILDING BLOCKS OF DATA ANALYSIS 294 PART 2 PART 3 PART 4 VISUALIZATION: GRAPHS AND TABLES FOR SUMMARIZING AND ORGANIZING DATA 296

More information

M249 Diagnostic Quiz

M249 Diagnostic Quiz THE OPEN UNIVERSITY Faculty of Mathematics and Computing M249 Diagnostic Quiz Prepared by the Course Team [Press to begin] c 2005, 2006 The Open University Last Revision Date: May 19, 2006 Version 4.2

More information

Unit 5: Sampling Distributions of Statistics

Unit 5: Sampling Distributions of Statistics Unit 5: Sampling Distributions of Statistics Statistics 571: Statistical Methods Ramón V. León 6/12/2004 Unit 5 - Stat 571 - Ramon V. Leon 1 Definitions and Key Concepts A sample statistic used to estimate

More information

The Normal Probability Distribution

The Normal Probability Distribution 1 The Normal Probability Distribution Key Definitions Probability Density Function: An equation used to compute probabilities for continuous random variables where the output value is greater than zero

More information

II - Probability. Counting Techniques. three rules of counting. 1multiplication rules. 2permutations. 3combinations

II - Probability. Counting Techniques. three rules of counting. 1multiplication rules. 2permutations. 3combinations II - Probability Counting Techniques three rules of counting 1multiplication rules 2permutations 3combinations Section 2 - Probability (1) II - Probability Counting Techniques 1multiplication rules In

More information

continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence

continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence continuous rv Let X be a continuous rv. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that for any two numbers a and b with a b, P(a X b) = b a f (x)dx.

More information

Unit 5: Sampling Distributions of Statistics

Unit 5: Sampling Distributions of Statistics Unit 5: Sampling Distributions of Statistics Statistics 571: Statistical Methods Ramón V. León 6/12/2004 Unit 5 - Stat 571 - Ramon V. Leon 1 Definitions and Key Concepts A sample statistic used to estimate

More information

A.REPRESENTATION OF DATA

A.REPRESENTATION OF DATA A.REPRESENTATION OF DATA (a) GRAPHS : PART I Q: Why do we need a graph paper? Ans: You need graph paper to draw: (i) Histogram (ii) Cumulative Frequency Curve (iii) Frequency Polygon (iv) Box-and-Whisker

More information

IEOR 165 Lecture 1 Probability Review

IEOR 165 Lecture 1 Probability Review IEOR 165 Lecture 1 Probability Review 1 Definitions in Probability and Their Consequences 1.1 Defining Probability A probability space (Ω, F, P) consists of three elements: A sample space Ω is the set

More information

. 13. The maximum error (margin of error) of the estimate for μ (based on known σ) is:

. 13. The maximum error (margin of error) of the estimate for μ (based on known σ) is: Statistics Sample Exam 3 Solution Chapters 6 & 7: Normal Probability Distributions & Estimates 1. What percent of normally distributed data value lie within 2 standard deviations to either side of the

More information

Introduction to Computational Finance and Financial Econometrics Descriptive Statistics

Introduction to Computational Finance and Financial Econometrics Descriptive Statistics You can t see this text! Introduction to Computational Finance and Financial Econometrics Descriptive Statistics Eric Zivot Summer 2015 Eric Zivot (Copyright 2015) Descriptive Statistics 1 / 28 Outline

More information

NCC5010: Data Analytics and Modeling Spring 2015 Exemption Exam

NCC5010: Data Analytics and Modeling Spring 2015 Exemption Exam NCC5010: Data Analytics and Modeling Spring 2015 Exemption Exam Do not look at other pages until instructed to do so. The time limit is two hours. This exam consists of 6 problems. Do all of your work

More information

STT315 Chapter 4 Random Variables & Probability Distributions AM KM

STT315 Chapter 4 Random Variables & Probability Distributions AM KM Before starting new chapter: brief Review from Algebra Combinations In how many ways can we select x objects out of n objects? In how many ways you can select 5 numbers out of 45 numbers ballot to win

More information

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal Econ 6900: Statistical Problems Instructor: Yogesh Uppal Email: yuppal@ysu.edu Lecture Slides 4 Random Variables Probability Distributions Discrete Distributions Discrete Uniform Probability Distribution

More information

**BEGINNING OF EXAMINATION** A random sample of five observations from a population is:

**BEGINNING OF EXAMINATION** A random sample of five observations from a population is: **BEGINNING OF EXAMINATION** 1. You are given: (i) A random sample of five observations from a population is: 0.2 0.7 0.9 1.1 1.3 (ii) You use the Kolmogorov-Smirnov test for testing the null hypothesis,

More information

MgtOp 215 TEST 1 (Golden) Spring 2016 Dr. Ahn. Read the following instructions very carefully before you start the test.

MgtOp 215 TEST 1 (Golden) Spring 2016 Dr. Ahn. Read the following instructions very carefully before you start the test. MgtOp 15 TEST 1 (Golden) Spring 016 Dr. Ahn Name: ID: Section (Circle one): 4, 5, 6 Read the following instructions very carefully before you start the test. This test is closed book and notes; one summary

More information

MATH 118 Class Notes For Chapter 5 By: Maan Omran

MATH 118 Class Notes For Chapter 5 By: Maan Omran MATH 118 Class Notes For Chapter 5 By: Maan Omran Section 5.1 Central Tendency Mode: the number or numbers that occur most often. Median: the number at the midpoint of a ranked data. Ex1: The test scores

More information

KING FAHD UNIVERSITY OF PETROLEUM & MINERALS DEPARTMENT OF MATHEMATICAL SCIENCES DHAHRAN, SAUDI ARABIA. Name: ID# Section

KING FAHD UNIVERSITY OF PETROLEUM & MINERALS DEPARTMENT OF MATHEMATICAL SCIENCES DHAHRAN, SAUDI ARABIA. Name: ID# Section KING FAHD UNIVERSITY OF PETROLEUM & MINERALS DEPARTMENT OF MATHEMATICAL SCIENCES DHAHRAN, SAUDI ARABIA STAT 11: BUSINESS STATISTICS I Semester 04 Major Exam #1 Sunday March 7, 005 Please circle your instructor

More information

Central Limit Theorem (cont d) 7/28/2006

Central Limit Theorem (cont d) 7/28/2006 Central Limit Theorem (cont d) 7/28/2006 Central Limit Theorem for Binomial Distributions Theorem. For the binomial distribution b(n, p, j) we have lim npq b(n, p, np + x npq ) = φ(x), n where φ(x) is

More information

Statistics for Managers Using Microsoft Excel 7 th Edition

Statistics for Managers Using Microsoft Excel 7 th Edition Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 7 Sampling Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 2014 Pearson Education, Inc. Chap 7-1 Learning Objectives

More information

Objective: To understand similarities and differences between geometric and binomial scenarios and to solve problems related to these scenarios.

Objective: To understand similarities and differences between geometric and binomial scenarios and to solve problems related to these scenarios. AP Statistics: Geometric and Binomial Scenarios Objective: To understand similarities and differences between geometric and binomial scenarios and to solve problems related to these scenarios. Everything

More information

Example - Let X be the number of boys in a 4 child family. Find the probability distribution table:

Example - Let X be the number of boys in a 4 child family. Find the probability distribution table: Chapter8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables tthe value of the result of the probability experiment is a RANDOM VARIABLE. Example - Let X be the number

More information

Lecture 2. Probability Distributions Theophanis Tsandilas

Lecture 2. Probability Distributions Theophanis Tsandilas Lecture 2 Probability Distributions Theophanis Tsandilas Comment on measures of dispersion Why do common measures of dispersion (variance and standard deviation) use sums of squares: nx (x i ˆµ) 2 i=1

More information

Probability and Random Variables A FINANCIAL TIMES COMPANY

Probability and Random Variables A FINANCIAL TIMES COMPANY Probability Basics Probability and Random Variables A FINANCIAL TIMES COMPANY 2 Probability Probability of union P[A [ B] =P[A]+P[B] P[A \ B] Conditional Probability A B P[A B] = Bayes Theorem P[A \ B]

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution January 31, 2018 Contents The Binomial Distribution The Normal Approximation to the Binomial The Binomial Hypothesis Test Computing Binomial Probabilities in R 30 Problems The

More information

Lecture 5: Fundamentals of Statistical Analysis and Distributions Derived from Normal Distributions

Lecture 5: Fundamentals of Statistical Analysis and Distributions Derived from Normal Distributions Lecture 5: Fundamentals of Statistical Analysis and Distributions Derived from Normal Distributions ELE 525: Random Processes in Information Systems Hisashi Kobayashi Department of Electrical Engineering

More information

MAS1403. Quantitative Methods for Business Management. Semester 1, Module leader: Dr. David Walshaw

MAS1403. Quantitative Methods for Business Management. Semester 1, Module leader: Dr. David Walshaw MAS1403 Quantitative Methods for Business Management Semester 1, 2018 2019 Module leader: Dr. David Walshaw Additional lecturers: Dr. James Waldren and Dr. Stuart Hall Announcements: Written assignment

More information

Discrete Probability Distributions and application in Business

Discrete Probability Distributions and application in Business http://wiki.stat.ucla.edu/socr/index.php/socr_courses_2008_thomson_econ261 Discrete Probability Distributions and application in Business By Grace Thomson DISCRETE PROBALITY DISTRIBUTIONS Discrete Probabilities

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distribution January 31, 2019 Contents The Binomial Distribution The Normal Approximation to the Binomial The Binomial Hypothesis Test Computing Binomial Probabilities in R 30 Problems The

More information

A useful modeling tricks.

A useful modeling tricks. .7 Joint models for more than two outcomes We saw that we could write joint models for a pair of variables by specifying the joint probabilities over all pairs of outcomes. In principal, we could do this

More information

Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc.

Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Chapter 8 Measures of Center Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Data that can only be integer

More information

Introduction to Statistics I

Introduction to Statistics I Introduction to Statistics I Keio University, Faculty of Economics Continuous random variables Simon Clinet (Keio University) Intro to Stats November 1, 2018 1 / 18 Definition (Continuous random variable)

More information

Chapter 8 Estimation

Chapter 8 Estimation Chapter 8 Estimation There are two important forms of statistical inference: estimation (Confidence Intervals) Hypothesis Testing Statistical Inference drawing conclusions about populations based on samples

More information

Statistics for Business and Economics: Random Variables:Continuous

Statistics for Business and Economics: Random Variables:Continuous Statistics for Business and Economics: Random Variables:Continuous STT 315: Section 107 Acknowledgement: I d like to thank Dr. Ashoke Sinha for allowing me to use and edit the slides. Murray Bourne (interactive

More information