Business Statistics /82 Spring 2011 Booth School of Business The University of Chicago Final Exam
|
|
- Myron Sharp
- 5 years ago
- Views:
Transcription
1 Business Statistics /82 Spring 2011 Booth School of Business The University of Chicago Final Exam Name You may use a calculator and two cheat sheets. You have 3 hours. I pledge my honor that I have not violated the Honor Code during this examination. Signature Question Points Total 100 Total 1
2 QUESTION 1 (10 points): vale: Vale S.A. daily returns in 2009 (223 obs.) ibovespa: IBOVESPA daily returns in obs.) Fitted regression: vale = a + b*ibovespa Standard Test Coefficient Estimate Error Statistic a (intercept) [ ] b (slope) [ ] a)(4) Fill the blanks. b)(2) Obtain the approximate 95% confidence interval for the slope. c)(2) Test the hypothesis that the intercept is equal to zero. d)(2) Test the hypothesis that the slope is equal to one. 2
3 QUESTION 2 (10 points): Two branches of a firm deliver daily advice regarding whether an asset is up or down. Their performances over the last several months are as follows: When the asset is up, branch A says it should be up 85% of the time, while branch B says it should be up 90% of the time. When the asset is down, branch A says it should be down 98% of the time, while branch B says it should be down 80% of the time. Suppose that the asset goes up 45% of the time and down 55% of the time. Suppose that branches A and B say that tomorrow the asset is going down. Which branch will produce more reliable forecast? Why? 3
4 QUESTION 3 (10 points): Below are time series plots of X1, X2, X3 and X4. Do any of the above data sets look like sample from any of the following distributions? a)(2) N(1.5,4) [ ] b)(2) N(20,1) [ ] c)(2) Binomial(3,0.5) [ ] d)(2) Bernoulli(0.5) [ ] e)(2) Binomial(3,0.8) [ ] 4
5 QUESTION 4 (10 points): Suppose you run a regression based on a couple of thousand observations to explain earnings in dollars (Y) in a particular industry as a function of the number of years of experience (X1) and whether or not you have an MBA degree (X2). Here X2=1 means the employee has an MBA degree. The fitted regression is Explain the meanings of a)(5) The intercept a. Y = a + b*x1 + c*x2. b)(5) The slope c. 5
6 QUESTION 5 (20 points): The following table partially shows the regression of wages on education level, where wage is measured in dollars per hour and education is measured in school years. a)(18)fill in the nine empty boxes. b)(2) Provide a 95% predictive interval for the wage of an employee with 10-years of education. 6
7 QUESTION 6: (10 points) Let W be the wage (in dollars per hour) and E a categorical variable such that E=0 when years of education is between 0 and 9; E=1 when years of education is between 10 and 14; and E=2 when years of education is between 15 and 18. It is known that (W E=0) ~ N(4.1,4.0) (W E=1) ~ N(5.3,9.0) (W E=2) ~ N(8.5,4.6) where N(a,b) stands for the normal distribution with mean a and variance b. Therefore, the standard deviation is equal to sqrt(b). It is also known that 11% of the employees fall into category E=0, 66% fall into category E=1 and, consequently, the remainder 23% fall into category E=2. Question: Suppose the wage of a new employee is between 5 and 7 dollars per hour. In which category he/she is most likely to fall into? 7
8 QUESTION 7: (10 points) Suppose we are in the business of making business cards, and that for a business card to be considered usable, say to fit your wallet or your business card holder, it must measure between 3.3 and 3.6 inches long. A total of 100 business cards were produced and lengths recorded: sample mean= and sample variance= a)(4) Compute a 95% confidence interval for the true average length of a business card. Let us now assume that X= length of business cards continuously produced is normally distributed with mean 3.5 inches and standard deviation of 0.1 inches; that is X ~ N(3.5,0.01). b)(4)compute the probability that a business card is usable, i.e. compute p = Pr(3.3<X<3.6). (Hint: use table 2) c) (2) Let D be the number of not usable business cards out of an i.i.d. sample of 1000 manufactured ones. What is the distribution of D? 8
9 QUESTION 8 (10 points): Suppose we want to test H0: p=0.5 against the alternative Ha: H0 is false, where p is the proportion of people unhappy with Obama s health plan. We collected n=30 observations and observed 9 successes, i.e. 9 of the 30 persons were unhappy with Obama s health plan. a)(4) Compute the P-value based on the normal approximation (table 2). b)(4) Compute the exact P-value (table 1). c)(2)comment on the similarity/difference between a) and b). 9
10 QUESTION 9 (10 points): Suppose you produce umbrellas and that in any given day you select 100 umbrellas from your production line to monitor the proportion of defective items. Your experience tells you that the defective rate is 2%. Nonetheless, you are not so sure since you have recently hired new employees and acquired new machines. Then, you decided to collect some data to learn about the process and update your experience. Below are sample percentages for each one of 20 consecutive days. The first 10 days are right before the new acquisitions, while the last 10 days are right after the new acquisitions: First 10 days: 25 defective out of 1000 umbrellas Last 10 days: 29 defective out of 1000 umbrellas a)(4) Let p be the true population defective rate. Test the null hypothesis H0:p=0.02 based on the first 10 days. b)(4) Repeat a) but now based on the last 10 days. c)(2) What happens to H0:p=0.02 when all 20 days are combined? 10
11 TABLE 1: BINOMIAL PROBABILITIES X ~ Binomial(30,p) Rows: number of successes Columns: probability of success (p) Table entry: Pr(x) for x=0,1,,
12 TABLE 2: CUMULATIVE NORMAL PROBABILITIES Z
Panel A: Treated firm matched to one control firm. t + 1 t + 2 t + 3 Total CFO Compensation 5.03% 0.84% 10.27% [0.384] [0.892] [0.
Online Appendix 1 Table O1: Determinants of CMO Compensation: Selection based on both number of other firms in industry that have CMOs and number of other firms in industry with MBA educated executives
More informationwine 1 wine 2 wine 3 person person person person person
1. A trendy wine bar set up an experiment to evaluate the quality of 3 different wines. Five fine connoisseurs of wine were asked to taste each of the wine and give it a rating between 0 and 10. The order
More informationComparing R print-outs from LM, GLM, LMM and GLMM
3. Inference: interpretation of results, plotting results, confidence intervals, hypothesis tests (Wald,LRT). 4. Asymptotic distribution of maximum likelihood estimators and tests. 5. Checking the adequacy
More informationMultiple Imputation for Missing Data in KLoSA
Multiple Imputation for Missing Data in KLoSA Juwon Song Korea University and UCLA Contents 1. Missing Data and Missing Data Mechanisms 2. Imputation 3. Missing Data and Multiple Imputation in Baseline
More informationOnline Appendix to The Effect of Liquidity on Governance
Online Appendix to The Effect of Liquidity on Governance Table OA1: Conditional correlations of liquidity for the subsample of firms targeted by hedge funds This table reports Pearson and Spearman correlations
More informationWhich of your fingernails comes closest to 1 cm in width? What is the length between your thumb tip and extended index finger tip? If no, why not?
wrong 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 right 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 51 50 49 score 100 98.5 97.0 95.5 93.9 92.4 90.9 89.4 87.9 86.4 84.8 83.3 81.8 80.3 78.8 77.3 75.8 74.2
More informationAppendix Table A1 Number of years since deregulation
Appendix Table A1 Number of years since deregulation This table presents the results of -in-s models incorporating the number of years since deregulation and using data for s with trade flows are above
More informationOnline Appendix to. Are Two heads Better Than One: Team versus Individual Play in Signaling Games. David C. Cooper and John H.
Online Appendix to Are Two heads Better Than One: Team versus Individual Play in Signaling Games David C. Cooper and John H. Kagel This appendix contains a discussion of the robustness of the regression
More informationBiologist at Work! Experiment: Width across knuckles of: left hand. cm... right hand. cm. Analysis: Decision: /13 cm. Name
wrong 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 right 72 71 70 69 68 67 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 score 100 98.6 97.2 95.8 94.4 93.1 91.7 90.3 88.9 87.5 86.1 84.7 83.3 81.9
More informationReview for Lab 1 Artificial Selection
Review for Lab 1 Artificial Selection Lab 1 Artificial Selection The purpose of a particular investigation was to see the effects of varying salt concentrations of nutrient agar and its effect on colony
More informationPerspective of the Labor Market for security guards in Israel in time of terror attacks
Perspective of the Labor Market for security guards in Israel in time of terror attacks 2000-2004 By Alona Shemesh Central Bureau of Statistics, Israel March 2013, Brussels Number of terror attacks Number
More informationRelationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good
Relationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good Carol Miu Massachusetts Institute of Technology Abstract It has become increasingly popular for statistics
More informationMissing value imputation in SAS: an intro to Proc MI and MIANALYZE
Victoria SAS Users Group November 26, 2013 Missing value imputation in SAS: an intro to Proc MI and MIANALYZE Sylvain Tremblay SAS Canada Education Copyright 2010 SAS Institute Inc. All rights reserved.
More informationMissing Data Treatments
Missing Data Treatments Lindsey Perry EDU7312: Spring 2012 Presentation Outline Types of Missing Data Listwise Deletion Pairwise Deletion Single Imputation Methods Mean Imputation Hot Deck Imputation Multiple
More informationThe Wild Bean Population: Estimating Population Size Using the Mark and Recapture Method
Name Date The Wild Bean Population: Estimating Population Size Using the Mark and Recapture Method Introduction: In order to effectively study living organisms, scientists often need to know the size of
More informationInternet Appendix for CEO Personal Risk-taking and Corporate Policies TABLE IA.1 Pilot CEOs and Firm Risk (Controlling for High Performance Pay)
TABLE IA.1 Pilot CEOs and Firm Risk (Controlling for High Performance Pay) OLS regressions with annualized standard deviation of firm-level monthly stock returns as the dependent variable. A constant is
More informationMastering Measurements
Food Explorations Lab I: Mastering Measurements STUDENT LAB INVESTIGATIONS Name: Lab Overview During this investigation, you will be asked to measure substances using household measurement tools and scientific
More informationActivity 10. Coffee Break. Introduction. Equipment Required. Collecting the Data
. Activity 10 Coffee Break Economists often use math to analyze growth trends for a company. Based on past performance, a mathematical equation or formula can sometimes be developed to help make predictions
More informationBuying Filberts On a Sample Basis
E 55 m ^7q Buying Filberts On a Sample Basis Special Report 279 September 1969 Cooperative Extension Service c, 789/0 ite IP") 0, i mi 1910 S R e, `g,,ttsoliktill:torvti EARs srin ITQ, E,6
More informationHW 5 SOLUTIONS Inference for Two Population Means
HW 5 SOLUTIONS Inference for Two Population Means 1. The Type II Error rate, β = P{failing to reject H 0 H 0 is false}, for a hypothesis test was calculated to be β = 0.07. What is the power = P{rejecting
More informationSTA Module 6 The Normal Distribution
STA 2023 Module 6 The Normal Distribution Learning Objectives 1. Explain what it means for a variable to be normally distributed or approximately normally distributed. 2. Explain the meaning of the parameters
More informationSTA Module 6 The Normal Distribution. Learning Objectives. Examples of Normal Curves
STA 2023 Module 6 The Normal Distribution Learning Objectives 1. Explain what it means for a variable to be normally distributed or approximately normally distributed. 2. Explain the meaning of the parameters
More informationGail E. Potter, Timo Smieszek, and Kerstin Sailer. April 24, 2015
Supplementary Material to Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions, published in Network Science Gail E.
More informationINSTITUTE AND FACULTY OF ACTUARIES CURRICULUM 2019 SPECIMEN SOLUTIONS. Subject CS1B Actuarial Statistics
INSTITUTE AND FACULTY OF ACTUARIES CURRICULUM 2019 SPECIMEN SOLUTIONS Subject CS1B Actuarial Statistics Question 1 (i) # Data entry before
More informationLabor Supply of Married Couples in the Formal and Informal Sectors in Thailand
Southeast Asian Journal of Economics 2(2), December 2014: 77-102 Labor Supply of Married Couples in the Formal and Informal Sectors in Thailand Chairat Aemkulwat 1 Faculty of Economics, Chulalongkorn University
More informationUsing Standardized Recipes in Child Care
Using Standardized Recipes in Child Care Standardized recipes are essential tools for implementing the Child and Adult Care Food Program meal patterns. A standardized recipe identifies the exact amount
More informationESTIMATING ANIMAL POPULATIONS ACTIVITY
ESTIMATING ANIMAL POPULATIONS ACTIVITY VOCABULARY mark capture/recapture ecologist percent error ecosystem population species census MATERIALS Two medium-size plastic or paper cups for each pair of students
More informationName: Adapted from Mathalicious.com DOMINO EFFECT
Activity A-1: Domino Effect Adapted from Mathalicious.com DOMINO EFFECT Domino s pizza is delicious. The company s success is proof that people enjoy their pizzas. The company is also tech savvy as you
More informationInternet Appendix for Does Stock Liquidity Enhance or Impede Firm Innovation? *
Internet Appendix for Does Stock Liquidity Enhance or Impede Firm Innovation? * This Internet Appendix provides supplemental analyses and robustness tests to the main results presented in Does Stock Liquidity
More informationFinal Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006
Dr. Roland Füss Winter Term 2005/2006 Final Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006 Note the following important information: 1. The total disposal time is 60 minutes.
More information1.3 Box & Whisker Plots
1.3 Box & Whisker Plots Box and Whisker Plots or box plots, are useful for showing the distribution of values in a data set. The box plot below is an example. A box plot is constructed from the five-number
More informationInternet Appendix to. The Price of Street Friends: Social Networks, Informed Trading, and Shareholder Costs. Jie Cai Ralph A.
Internet Appendix to The Price of Street Friends: Social Networks, Informed Trading, and Shareholder Costs Jie Cai Ralph A. Walkling Ke Yang October 2014 1 A11. Controlling for s Logically Associated with
More informationPSYC 6140 November 16, 2005 ANOVA output in R
PSYC 6140 November 16, 2005 ANOVA output in R Type I, Type II and Type III Sums of Squares are displayed in ANOVA tables in a mumber of packages. The car library in R makes these available in R. This handout
More informationThe SAS System 09:38 Wednesday, December 2, The CANDISC Procedure
The SAS System 09:38 Wednesday, December 2, 2009 63 Observations 67 DF Total 66 Variables 43 DF Within Classes 65 Classes 2 DF Between Classes 1 Class Level Information Variable SPECIES Name Frequency
More informationReturn to wine: A comparison of the hedonic, repeat sales, and hybrid approaches
Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches James J. Fogarty a* and Callum Jones b a School of Agricultural and Resource Economics, The University of Western Australia,
More informationPEEL RIVER HEALTH ASSESSMENT
PEEL RIVER HEALTH ASSESSMENT CONTENTS SUMMARY... 2 Overall River Health Scoring... 2 Overall Data Sufficiency Scoring... 2 HYDROLOGY... 3 Overall Hydrology River Health Scoring... 3 Hydrology Data Sufficiency...
More informationTHE INTERNATIONAL OLIVE COUNCIL
1991R2568 EN 01.10.2008 022.001 75 ANNEX XII THE INTERNATIONAL OLIVE COUNCIL S METHOD FOR THE ORGANOLEPTIC ASSESSMENT OF VIRGIN OLIVE OIL 1. PURPOSE AND SCOPE This method is based on Decision No DEC-21/95-V/2007
More informationProblem Set #3 Key. Forecasting
Problem Set #3 Key Sonoma State University Business 581E Dr. Cuellar The data set bus581e_ps3.dta is a Stata data set containing annual sales (cases) and revenue from December 18, 2004 to April 2 2011.
More informationCurtis Miller MATH 3080 Final Project pg. 1. The first question asks for an analysis on car data. The data was collected from the Kelly
Curtis Miller MATH 3080 Final Project pg. 1 Curtis Miller 4/10/14 MATH 3080 Final Project Problem 1: Car Data The first question asks for an analysis on car data. The data was collected from the Kelly
More informationOnline Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform
Online Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform This document contains several additional results that are untabulated but referenced
More informationSTAT 5302 Applied Regression Analysis. Hawkins
Homework 3 sample solution 1. MinnLand data STAT 5302 Applied Regression Analysis. Hawkins newdata
More informationThe Financing and Growth of Firms in China and India: Evidence from Capital Markets
The Financing and Growth of Firms in China and India: Evidence from Capital Markets Tatiana Didier Sergio Schmukler Dec. 12-13, 2012 NIPFP-DEA-JIMF Conference Macro and Financial Challenges of Emerging
More informationIT 403 Project Beer Advocate Analysis
1. Exploratory Data Analysis (EDA) IT 403 Project Beer Advocate Analysis Beer Advocate is a membership-based reviews website where members rank different beers based on a wide number of categories. The
More informationQUARTELY MAIZE MARKET ANALYSIS & OUTLOOK BULLETIN 1 OF 2015
QUARTELY MAIZE MARKET ANALYSIS & OUTLOOK BULLETIN 1 OF 2015 INTRODUCTION The following discussion is a review of the maize market environment. The analysis is updated on a quarterly 1 basis and the interval
More informationLevel 2 Mathematics and Statistics, 2016
91267 912670 2SUPERVISOR S Level 2 Mathematics and Statistics, 2016 91267 Apply probability methods in solving problems 9.30 a.m. Thursday 24 November 2016 Credits: Four Achievement Achievement with Merit
More informationMBA 503 Final Project Guidelines and Rubric
MBA 503 Final Project Guidelines and Rubric Overview There are two summative assessments for this course. For your first assessment, you will be objectively assessed by your completion of a series of MyAccountingLab
More informationOn-line Appendix for the paper: Sticky Wages. Evidence from Quarterly Microeconomic Data. Appendix A. Weights used to compute aggregate indicators
Hervé LE BIHAN, Jérémi MONTORNES, Thomas HECKEL On-line Appendix for the paper: Sticky Wages. Evidence from Quarterly Microeconomic Data Not intended for publication Appendix A. Weights ud to compute aggregate
More informationTHE STATISTICAL SOMMELIER
THE STATISTICAL SOMMELIER An Introduction to Linear Regression 15.071 The Analytics Edge Bordeaux Wine Large differences in price and quality between years, although wine is produced in a similar way Meant
More informationCan You Tell the Difference? A Study on the Preference of Bottled Water. [Anonymous Name 1], [Anonymous Name 2]
Can You Tell the Difference? A Study on the Preference of Bottled Water [Anonymous Name 1], [Anonymous Name 2] Abstract Our study aims to discover if people will rate the taste of bottled water differently
More informationPredicting Wine Quality
March 8, 2016 Ilker Karakasoglu Predicting Wine Quality Problem description: You have been retained as a statistical consultant for a wine co-operative, and have been asked to analyze these data. Each
More informationAJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship
AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship Juliano Assunção Department of Economics PUC-Rio Luis H. B. Braido Graduate School of Economics Getulio
More information*p <.05. **p <.01. ***p <.001.
Table 1 Weighted Descriptive Statistics and Zero-Order Correlations with Fatherhood Timing (N = 1114) Variables Mean SD Min Max Correlation Interaction time 280.70 225.47 0 1095 0.05 Interaction time with
More informationStatistics 5303 Final Exam December 20, 2010 Gary W. Oehlert NAME ID#
Statistics 5303 Final Exam December 20, 2010 Gary W. Oehlert NAME ID# This exam is open book, open notes; you may use a calculator. Do your own work! Use the back if more space is needed. There are nine
More informationFaculty of Science FINAL EXAMINATION MATH-523B Generalized Linear Models
Faculty of Science FINAL EXAMINATION MATH-523B Generalized Linear Models Examiner: Professor K.J. Worsley Associate Examiner: Professor A. Vandal Date: Tuesday, April 20, 2004 Time: 14:00-17:00 hours INSTRUCTIONS:
More informationMethod for the imputation of the earnings variable in the Belgian LFS
Method for the imputation of the earnings variable in the Belgian LFS Workshop on LFS methodology, Madrid 2012, May 10-11 Astrid Depickere, Anja Termote, Pieter Vermeulen Outline 1. Introduction 2. Imputation
More informationStatistics: Final Project Report Chipotle Water Cup: Water or Soda?
Statistics: Final Project Report Chipotle Water Cup: Water or Soda? Introduction: For our experiment, we wanted to find out how many customers at Chipotle actually get water when they order a water cup.
More information2 nd Midterm Exam-Solution
2 nd Midterm Exam- اس تعن ابهلل وكن عىل يقني بأ ن لك ما ورد يف هذه الورقة تعرفه جيدا وقد تدربت عليه مبا فيه الكفاية Question #1: Answer the following with True or False: 1. The non-parametric input modeling
More informationCaffeine And Reaction Rates
Caffeine And Reaction Rates Topic Reaction rates Introduction Caffeine is a drug found in coffee, tea, and some soft drinks. It is a stimulant used to keep people awake when they feel tired. Some people
More informationUpdate to A Comprehensive Look at the Empirical Performance of Equity Premium Prediction
Update to A Comprehensive Look at the Empirical Performance of Equity Premium Prediction Amit Goyal UNIL Ivo Welch UCLA September 17, 2014 Abstract This file contains updates, one correction, and links
More informationWine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts
Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts When you need to understand situations that seem to defy data analysis, you may be able to use techniques
More informationActivity 26.1 Who Should Do What?
Comparative Advantage Lesson 26 Activity 26.1 Who Should Do What? Nino owns a pizza shop. He is very good at what he does. In one hour, he can make 9 pizzas or prepare 36 salads. His business is growing
More informationThe multivariate piecewise linear growth model for ZHeight and zbmi can be expressed as:
Bi-directional relationships between body mass index and height from three to seven years of age: an analysis of children in the United Kingdom Millennium Cohort Study Supplementary material The multivariate
More informationFigure 1: Quartely milk production and gross value
Million Litres Million Rands QUARTERLY DAIRY MARKET ANALYSIS BULLETIN 1 OF 215 1. INTRODUCTION The following discussion is a review of the dairy market environment. The analysis is updated on a quarterly
More informationOnline Appendix. for. Female Leadership and Gender Equity: Evidence from Plant Closure
Online Appendix for Female Leadership and Gender Equity: Evidence from Plant Closure Geoffrey Tate and Liu Yang In this appendix, we provide additional robustness checks to supplement the evidence in the
More informationThis appendix tabulates results summarized in Section IV of our paper, and also reports the results of additional tests.
Internet Appendix for Mutual Fund Trading Pressure: Firm-level Stock Price Impact and Timing of SEOs, by Mozaffar Khan, Leonid Kogan and George Serafeim. * This appendix tabulates results summarized in
More informationQuestions and Answers about Smart Snacks in School
Questions and Answers about Smart Snacks in School Applicability Q1: Do Smart Snacks nutrition standards apply to events on the weekend, for example food sales during a sporting event? A: No. The Smart
More informationNotes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Capacity Utilization. Last Updated: December 21, 2016
1 Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Capacity Utilization Last Updated: December 21, 2016 I. General Comments This file provides documentation for the Philadelphia
More informationThe age of reproduction The effect of university tuition fees on enrolment in Quebec and Ontario,
The age of reproduction The effect of university tuition fees on enrolment in Quebec and Ontario, 1946 2011 Benoît Laplante, Centre UCS de l INRS Pierre Doray, CIRST-UQAM Nicolas Bastien, CIRST-UQAM Research
More informationCOMPARISON OF CORE AND PEEL SAMPLING METHODS FOR DRY MATTER MEASUREMENT IN HASS AVOCADO FRUIT
New Zealand Avocado Growers' Association Annual Research Report 2004. 4:36 46. COMPARISON OF CORE AND PEEL SAMPLING METHODS FOR DRY MATTER MEASUREMENT IN HASS AVOCADO FRUIT J. MANDEMAKER H. A. PAK T. A.
More information1ACE Exercise 2. Name Date Class
1ACE Exercise 2 Investigation 1 2. Use the totals in the last row of the table on page 16 for each color of candies found in all 15 bags. a. Make a bar graph for these data that shows the percent of each
More informationActivity Sheet Chapter 6, Lesson 6 Using Chemical Change to Identify an Unknown
Activity Sheet Chapter 6, Lesson 6 Using Chemical Change to Identify an Unknown Name Date DEMONSTRATION 1. Your teacher poured iodine solution on top of two white powders. How do you know that these two
More informationImputation of multivariate continuous data with non-ignorable missingness
Imputation of multivariate continuous data with non-ignorable missingness Thais Paiva Jerry Reiter Department of Statistical Science Duke University NCRN Meeting Spring 2014 May 23, 2014 Thais Paiva, Jerry
More informationB756: Factors Affecting the Unit Costs of Milk Distribution
The University of Maine DigitalCommons@UMaine Bulletins Maine Agricultural and Forest Experiment Station 1979 B756: Factors Affecting the Unit Costs of Milk Distribution Homer B. Metzger Follow this and
More informationAlgebra 2: Sample Items
ETO High School Mathematics 2014 2015 Algebra 2: Sample Items Candy Cup Candy Cup Directions: Each group of 3 or 4 students will receive a whiteboard, marker, paper towel for an eraser, and plastic cup.
More informationCAUTION!!! Do not eat anything (Skittles, cylinders, dishes, etc.) associated with the lab!!!
Physical Science Period: Name: Skittle Lab: Conversion Factors Date: CAUTION!!! Do not eat anything (Skittles, cylinders, dishes, etc.) associated with the lab!!! Estimate: Make an educated guess about
More information5 Populations Estimating Animal Populations by Using the Mark-Recapture Method
Name: Period: 5 Populations Estimating Animal Populations by Using the Mark-Recapture Method Background Information: Lincoln-Peterson Sampling Techniques In the field, it is difficult to estimate the population
More informationOnline Appendix for. To Buy or Not to Buy: Consumer Constraints in the Housing Market
Online Appendix for To Buy or Not to Buy: Consumer Constraints in the Housing Market By Andreas Fuster and Basit Zafar, Federal Reserve Bank of New York 1. Main Survey Questions Highlighted parts correspond
More information1. Determine methods that can be used to form curds and whey from milk. 2. Explain the Law of Conservation of Mass using quantitative observations.
Food Explorations Lab: Maintaining Mass STUDENT LAB INVESTIGATIONS Name: Lab Overview In this investigation, you will make qualitative and quantitative observations as you test three possible methods of
More informationLab 2-1: Measurement in Chemistry
Name: Lab Partner s Name: Lab 2-1: Measurement in Chemistry Lab Station No. Introduction Most chemistry lab activities involve the use of various measuring instruments. The three variables you will measure
More informationGrowth in early yyears: statistical and clinical insights
Growth in early yyears: statistical and clinical insights Tim Cole Population, Policy and Practice Programme UCL Great Ormond Street Institute of Child Health London WC1N 1EH UK Child growth Growth is
More informationPerformance Task: FRACTIONS! Names: Date: Hour:
Performance Task: FRACTIONS! Names: Date: Hour: Cookies! Baking! FRACTIONS! Imagine you are treating yourself to a warm, fresh out of the oven chocolate chip cookie. Or perhaps it is an oatmeal raisin
More informationNotes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Indexes of Aggregate Weekly Hours. Last Updated: December 22, 2016
1 Notes on the Philadelphia Fed s Real-Time Data Set for Macroeconomists (RTDSM) Indexes of Aggregate Weekly Hours Last Updated: December 22, 2016 I. General Comments This file provides documentation for
More informationHow Rest Area Commercialization Will Devastate the Economic Contributions of Interstate Businesses. Acknowledgements
How Rest Area Commercialization Will Devastate the Economic Contributions of Interstate Businesses Acknowledgements The NATSO Foundation, a charitable 501(c)(3) organization, is the research and educational
More informationCredit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications. Web Appendix
Credit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications By GABRIEL JIMÉNEZ, STEVEN ONGENA, JOSÉ-LUIS PEYDRÓ, AND JESÚS SAURINA Web Appendix APPENDIX A -- NUMBER
More informationMidterm Economics 181 International Trade Fall 2005
Midterm Economics 181 International Trade Fall 2005 Please answer all parts. Please show your work as much as possible. Part I (20 points). Short Answer. Please give a full answer. If you need to indicate
More informationOF THE VARIOUS DECIDUOUS and
(9) PLAXICO, JAMES S. 1955. PROBLEMS OF FACTOR-PRODUCT AGGRE- GATION IN COBB-DOUGLAS VALUE PRODUCTIVITY ANALYSIS. JOUR. FARM ECON. 37: 644-675, ILLUS. (10) SCHICKELE, RAINER. 1941. EFFECT OF TENURE SYSTEMS
More informationPower and Priorities: Gender, Caste, and Household Bargaining in India
Power and Priorities: Gender, Caste, and Household Bargaining in India Nancy Luke Associate Professor Department of Sociology and Population Studies and Training Center Brown University Nancy_Luke@brown.edu
More information: star-ng sample of lemon juice of 100ml =[H+]/100mL 100mL* 10-2 =[H+]
Apple Oxida+on Purpose: One of the problems in making fruit salad is keeping the apples looking fresh. Many cooks use lemon juice to keep the apples from turning brown. Apples turn brown because of oxida+on.
More informationFlexible Working Arrangements, Collaboration, ICT and Innovation
Flexible Working Arrangements, Collaboration, ICT and Innovation A Panel Data Analysis Cristian Rotaru and Franklin Soriano Analytical Services Unit Economic Measurement Group (EMG) Workshop, Sydney 28-29
More informationLabor Requirements and Costs for Harvesting Tomatoes. Zhengfei Guan, 1 Feng Wu, and Steven Sargent University of Florida
Labor Requirements and Costs for ing Tomatoes Zhengfei Guan, 1 Feng Wu, and Steven Sargent University of Florida Introduction Florida accounted for 30% to 40% of all commercially produced fresh-market
More informationCambridge International Examinations Cambridge International General Certificate of Secondary Education
Cambridge International Examinations Cambridge International General Certificate of Secondary Education *5342618795* BIOLOGY 0610/63 Paper 6 Alternative to Practical October/November 2017 1 hour Candidates
More informationClassifying the Edible Parts of Plants
SUPPLEMENTARY LESSON: EXTENSION OF FRUIT OR NOT? Classifying the Edible Parts of Plants After completing the lesson Fruit or Not? (page 23) students will have been introduced to one of the six edible parts
More informationFleurieu zone (other)
Fleurieu zone (other) Incorporating Southern Fleurieu and Kangaroo Island wine regions, as well as the remainder of the Fleurieu zone outside all GI regions Regional summary report 2006 South Australian
More informationFrom VOC to IPA: This Beer s For You!
From VOC to IPA: This Beer s For You! Joel Smith Statistician Minitab Inc. jsmith@minitab.com 2013 Minitab, Inc. Image courtesy of amazon.com The Data Online beer reviews Evaluated overall and: Appearance
More informationInvestigation 1: Ratios and Proportions and Investigation 2: Comparing and Scaling Rates
Comparing and Scaling: Ratios, Rates, Percents & Proportions Name: Per: Investigation 1: Ratios and Proportions and Investigation 2: Comparing and Scaling Rates Standards: 7.RP.1: Compute unit rates associated
More informationModule 6. Yield and Fruit Size. Presenter: Stephan Verreynne
Presenter: Stephan Verreynne definition Yield Yield refers to the amount of fruit produced, and can be expressed in terms of: Tree yield kg per tree kg/tree Orchard yield tons per hectare t/ha Export yield
More informationHandling Missing Data. Ashley Parker EDU 7312
Handling Missing Data Ashley Parker EDU 7312 Presentation Outline Types of Missing Data Treatments for Handling Missing Data Deletion Techniques Listwise Deletion Pairwise Deletion Single Imputation Techniques
More informationGuatemala. 1. Guatemala: Change in food prices
Appendix I: Impact on Household Welfare: Guatemala 1. Guatemala: Change in food prices Group dp1 dp2 1. Rice 12.87% 10.00% 2. Corn 5.95% 10.00% 3. Bread and dried 29.17% 10.00% 4. Beans, roots, vegetables
More informationAnna Adamecz-Völgyi, Márton Csillag, Tamás Molnár & Ágota Scharle. 5.4 Might training programmes...
5.4 Might training programmes... 5.4 MIGHT TRAINING PROGRAMMES EASE LABOUR SHORTAGE? THE TARGETING AND EFFECTIVENESS OF TRAINING PROGRAMMES ORGANISED OR FINANCED BY LOCAL EMPLOYMENT OFFICES OF THE HUNGARIAN
More informationAfter your yearly checkup, the doctor has bad news and good news.
Modeling Belief How much do you believe it will rain? How strong is your belief in democracy? How much do you believe Candidate X? How much do you believe Car x is faster than Car y? How long do you think
More information