# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

2 Pages

### Lab 3 Scatter Diagrams, Covariance, Correlation and Regression

Course: RESEC 312, Fall 2009
School: UMass (Amherst)
Rating:

Word Count: 1035

#### Document Preview

3: Lab The Sampling Distribution, Interval Estimation, Correlation and Covariance Objectives: 1. Complete an example of a t-test, or two. 2. Still more practice with Excel: equations and functions. 3. Bivariate distributions relationship between two variables. 4. Introduce the concepts of covariance and correlation variables that are related to each other. 5. Descriptive measures of covariance and correlation....

Register Now

#### Unformatted Document Excerpt

Coursehero >> Massachusetts >> UMass (Amherst) >> RESEC 312

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

UMass (Amherst) - RESEC - 312
Covariance and Correlation:Covariance and correlation measure linear association between two variables, say X and Y.Covariance:Population Parameter: X ,Y = (Y iY)( X i X )NThe population parameter describes linear association betw
UMass (Amherst) - RESEC - 312
Excel Functions: + * ^Syntax =C5+D5 =C5-D5 =C5*D5 =C5^2Definition/Description Adds the values found in cells C5 and D5. Subtracts . Multiplies . Creates the square of the value in C5. The dollar sign locks cell addresses so that they do not chang
UMass (Amherst) - RESEC - 312
Excel Functions: + * ^Syntax =C5+D5 =C5-D5 =C5*D5 =C5^2Definition/Description Adds the values found in cells C5 and D5. Subtracts Multiplies Creates the square of the value in C5. The dollar sign locks cell addresses so that they do not change
UMass (Amherst) - RESEC - 312
DATE Bonds (Aaa) 1976 8.43 1977 8.02 1978 8.73 1979 9.63 1980 11.94 1981 14.17 1982 13.79 1983 12.04 1984 12.71 1985 11.37 1986 9.02 1987 9.38 1988 9.71 1989 9.26 1990 9.32 1991 8.77 1992 8.14 1993 7.22 1994 7.97 1995 7.59 1996 7.37 1997 7.27 1998 6.
UMass (Amherst) - RESEC - 312
Name: Lab Exam 1A - Distributions of DataOpen the Excel spreadsheet: Exam 1A found in the course folder. The Excel file contains a sample of Amherst home sales; the sample was collected for the year 2003. Complete the exercises below using these sam
UMass (Amherst) - RESEC - 312
Address 18 Trillium Way 52 Elf Hill Rd L2 Kestrel Ln Lot 25 Hop Brook Rd 26 Teaberry Ln L20 Hop Brook Rd 3 Bayberry Ln 585 Station Rd 30 Bridle Path 27 Woodlot Rd 23 Alyssum Dr Lot1 Arbor Way L42 WoodLot 1050 Bay Rd 45 Hills Rd 45 Phillips St 293 Bel
UMass (Amherst) - RESEC - 312
Address 234 Henry St 293 Belchertown Rd 143 South East St 34 Logtown Rd 340 Amity St 45 Phillips St 52 Elf Hill Rd 1299 Bay Rd 1050 Bay Rd 15 Cortland Dr 585 Station Rd Lot1 Arbor Way 30 Bridle Path 217 Aubinwood Rd 83 N Prospect St 18 Trillium Way 4
UMass (Amherst) - RESEC - 312
Lab 5: OLS Live and RegressionObjectives: 1. Review OLS. What does OLS mean? What does it accomplish? What do we choose? 2. Create a spreadsheet including the data manipulations needed to estimate a regression line. 3. Given sample data, use Excel t
UMass (Amherst) - RESEC - 312
X 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23E[Y] 21.5 32 41.5 50 57.5 64 69.5 74 77.5 80 81.5 82 81.5 80 77.5 74 69.5 64 57.5 50 41.5 32 21.5u -3.22 -3.9 2.25 4.95 2.22 6.85 -2.77 -1.66 -0.49 5.09 -3.27 -4.01 1.71 -0.41 -2.76 -0.
UMass (Amherst) - RESEC - 312
Lab 6: Sampling Distributions for OLS EstimatorsObjectives: ^ ^ The OLS estimators 0 and 1 are random variables they have sampling distributions. In order to proceed from point estimation to interval estimation, we need to know about the sampling
Ill. Chicago - MCS - 441
1 HW12 1. a) f (0) = 0, f (1) = 1, f (2) = 1, f (3) = 0. b) 0, if n is even, f (n) = 1, otherwise. c) f (0) = 100, f (1) = 1000, f (2) = 10000. d) f (n) = n3 + n. 2. Let M = {x0 , . . . , xm-1 } and N = {y0 , . . . , yn-1 }. For k {0, . . . , nm -
UMass (Amherst) - RESEC - 312
Name: Random Seeds X 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 195 E[Y|X] u1 u2 Y1 Y2 x x-sq xY1 xY2Sample 1 Results: beta 0 hat: beta 1 hat: Sample 2 Results: beta 0 hat: beta 1 hat:sigma beta 1 hat:90% Co
UMass (Amherst) - RESEC - 312
Lab 7: Regression Topics: Confidence Intervals, Plots and Non-Linear ModelsObjectives: We now assume that the mean of our dependent variable changes as the independent variable changes: E[ Y X t ] = 0 + 1 X t . The primary objectives for this lab a
UMass (Amherst) - RESEC - 312
Year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966W 1.62 1.65 1.79 1.94 2.03 2.12 2.26 2.44 2.57 2.66 2.73 2.8 2.92 3.02 3.13 3.28 3.43 3.58wdotU 1 1.4 1.1 1 1.5 1.2 1 1.1 1.3 1.8 1.9 1.5 1.4 1.8 1.1
UMass (Amherst) - RESEC - 312
Year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966W 1.62 1.65 1.79 1.94 2.03 2.12 2.26 2.44 2.57 2.66 2.73 2.8 2.92 3.02 3.13 3.28 3.43 3.58wdotU 1 1.4 1.1 1 1.5 1.2 1 1.1 1.3 1.8 1.9 1.5 1.4 1.8 1.1
UMass (Amherst) - RESEC - 312
Year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966W 1.62 1.65 1.79 1.94 2.03 2.12 2.26 2.44 2.57 2.66 2.73 2.8 2.92 3.02 3.13 3.28 3.43 3.58wdotU 1 1.4 1.1 1 1.5 1.2 1 1.1 1.3 1.8 1.9 1.5 1.4 1.8 1.1
UMass (Amherst) - RESEC - 312
Lab 8: Multiple Regression: Excel and MinitabObjectives: We all agree that most economic models would require estimation of a multiple regression. The objective of todays lab is to estimate multiple regression models in Excel and introduce you to da
UMass (Amherst) - RESEC - 312
year 1971 1971 1972 1972 1972 1972 1973 1973 1973 1973 1974 1974 1974 1974 1975 1975quarter 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2sales prose pcarn dinc trend 11484 2.26 3.49 158.11 9348 2.54 2.85 173.36 8429 3.07 4.06 165.26 10079 2.91 3.64 172.92 9240
UMass (Amherst) - RESEC - 312
year 1971 1971 1972 1972 1972 1972 1973 1973 1973 1973 1974 1974 1974 1974 1975 1975quarter 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2sales prose 11484 9348 8429 10079 9240 8862 6216 8253 8038 7476 5911 7950 6134 5868 3160 5872pcarn 2.26 2.54 3.07 2.91 2.
Ill. Chicago - MCS - 441
Final Exam for MCS 441: Theory of Computation I May 7, 2008 A perfect score is given for the star problems and any 6 other problems (2+1+1+2). Solving additional problems is to your benefit. Good luck! 1) Write a RE which generates the even length wo
UMass (Amherst) - RESEC - 312
Descriptive Statistics: sales, prose, pcarn, dincVariable sales prose pcarn dinc N 16 16 16 16 Mean 7645 3.107 3.4319 180.53 SE Mean 511 0.134 0.0812 2.47 StDev 2043 0.538 0.3249 9.87 Minimum 3160 2.260 2.8500 158.11 Q1 5967 2.740 3.1475 173.94 Medi
UMass (Amherst) - RESEC - 312
year 1971 1971 1972 1972 1972 1972 1973 1973 1973 1973 1974 1974 1974 1974 1975 1975quarter 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2sales prose pcarn dinc trend 11484 2.26 3.49 158.11 9348 2.54 2.85 173.36 8429 3.07 4.06 165.26 10079 2.91 3.64 172.92 9240
UMass (Amherst) - RESEC - 312
year 1971 1971 1972 1972 1972 1972 1973 1973 1973 1973 1974 1974 1974 1974 1975 1975quarter 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2sales prose 11484 9348 8429 10079 9240 8862 6216 8253 8038 7476 5911 7950 6134 5868 3160 5872pcarn 2.26 2.54 3.07 2.91 2.
UMass (Amherst) - RESEC - 312
Lab Exam 2A Please use the data set Home Prices.xls for all analyses below. The data set can be found in the ResEc 312 folder on the server. The data set includes the following variables: PRICE - the price of the home in dollars. TOWN - the town in w
UMass (Amherst) - RESEC - 312
OBSN HOME 39 96 Avenue F 40 370-42 Mill Valley 41 29 Avenue A 42 L82 Avenue B 43 94 Channel Dr 44 370 Mill Valley Rd #52 45 259 Ave G 46 533 Avenue L 47 162 Amherst Rd 48 638 Federal St 1 228 Henry St 49 622 Federal 2 110 Henry St 3 234 Henry St 50 9
UMass (Amherst) - RESEC - 312
OBSNHOME 1 228 Henry St 2 110 Henry St 3 234 Henry St 4 293 Belchertown Rd 5 143 South East St 6 34 Logtown Rd 7 340 Amity St 8 45 Phillips St 9 154 Summer St 10 295 Amity St 11 52 Elf Hill Rd 12 Lot 1 Old Farm Rd 13 1299 Bay Rd 14 1050 Bay Rd 15 1
UMass (Amherst) - RESEC - 312
Lab Exam 2A Please use the data set Home Prices.xls for all analyses below. The data set can be found in the ResEc 312 folder on the server. The data set includes the following variables: PRICE - the price of the home in dollars. TOWN - the town in w
Ill. Chicago - MCS - 441
Extra credit problem for Feb 11 Show that the language {0m 1n | m, n 0 and m = n} is not regular using directly the pumping lemma.1
UMass (Amherst) - RESEC - 312
OBSNHOME 1 228 Henry St 2 110 Henry St 3 234 Henry St 4 293 Belchertown Rd 5 143 South East St 6 34 Logtown Rd 7 340 Amity St 8 45 Phillips St 9 154 Summer St 10 295 Amity St 11 52 Elf Hill Rd 12 Lot 1 Old Farm Rd 13 1299 Bay Rd 14 1050 Bay Rd 15 1
UMass (Amherst) - RESEC - 312
Lab 9: Dummy Variables and Data ManipulationsObjectives: This lab will introduce you to data manipulations in Minitab, especially the creation of dummy variables and interaction variables. Well then use one of the most common data transformations in
Ill. Chicago - MCS - 441
HW for Mar 10 Write the sequence of congurations which the Turing machine below goes through on the following inputs. Remember that, if the head reads a symbol for which there is no arrow, by convention the machine goes to the rejecting state qr . (a
UMass (Amherst) - RESEC - 312
Lab 10: Data Manipulations and Non-Linear ModelsObjectives: It is not unusual to have to make some modifications to your data before estimation. In the first part of the lab, well use Minitab to do some data manipulations. The second part of the lab
UMass (Amherst) - RESEC - 312
Exam 2 Practice ProblemsIntro. Econometrics Res. Economics 3121. Using the data in the file CPS1985-NonLin.mtw estimate the model(s) required to address the following questions. These are actual survey data collected in 1985 from 534 U.S. citizen
UMass (Amherst) - RESEC - 312
Regression Analysis: wage versus ed, exThe regression equation is wage = - 4.90 + 0.926 ed + 0.105 ex Predictor Constant ed ex Coef -4.904 0.92595 0.10513 SE Coef 1.219 0.08140 0.01720 T -4.02 11.38 6.11 P 0.000 0.000 0.000S = 4.59914R-Sq = 20.2
UMass (Amherst) - RESEC - 312
Lab 11: MulticollinearityObjectives: In todays lab we will investigate the existence of a problem in the sample data - Multicollinearity. Multcollinearity is the existence of linear association among two or more independent variables. The mere exist
UMass (Amherst) - RESEC - 312
year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982qchik 27.8 29.9 29.8 30.8 31.2 33.3 35.6 36.4 36.7 38.4 40.4 40.3 41.8 40.4 40.7 40.1 42.7 44.1 46.7 50.6 50.1 51.7 52.9dinc 39
UMass (Amherst) - RESEC - 312
Regression Analysis: qchik versus pchik, ppork, pbeef, dincThe regression equation is qchik = 37.2 - 0.611 pchik + 0.198 ppork + 0.0695 pbeef + 0.00501 dinc Predictor Constant pchik ppork pbeef dinc Coef 37.232 -0.6112 0.19841 0.06950 0.005011 SE Co
Ill. Chicago - MCS - 441
HW for Mar 12 1) Write the sequence of configurations which the Turing machine below goes through on the following inputs. Remember that, if the head reads a symbol for which there is no arrow, by convention the machine goes to the rejecting state qr
UMass (Amherst) - RESEC - 312
Name:Exam 1 B (15) Introductory Econometrics Resource Economics 3121. Explain the basic idea behind Dan's OLS Live spreadsheet and the OLS method. In particular: (1) What is the OLS criterion and how is it used in fitting a line? (2) What two thin
Ill. Chicago - MCS - 441
HW for Mar 14 1) Define a busy Turing Machine to be one which never stays, i.e. its transition function has the form : Q Q {L, R} (no S). Show that a language L is recognizable/decidable by a busy TM if and only if it is recognizable/decidable
UMass (Amherst) - RESEC - 312
Exam 2Resource Economics 312 Introductory Econometrics1. Early in this course, Dan uttered his battle cry: &quot;Estimators are random variables!&quot; (4) a. Explain clearly why OLS estimators for the population parameters of a regression model are random
Ill. Chicago - MCS - 441
HW for Mar 17 1) Let b 2 be an integer and let b = {1, 2, . . . , b}. Order the words from in the following way b , 1, 2, . . . , b, 11, 21, . . . , b1, 12, . . . , 22, . . . , 1b, . . . , bb, 111, 211, . . . More precisely we have the following. F
North Texas - BIOL - 1130
Chapter 1This lecture will help you understand: 0. The nature of environmental science 1. Natural resources and their importance 2. The scientific method and the scientific process 3. Pressures on the global environment 4. Sustainability The &quot;enviro
UMass (Amherst) - RESEC - 312
Exam 2 - KeyResource Economics 312 Introductory Econometrics1. Early in this course, Dan uttered his battle cry: &quot;Estimators are random variables!&quot; (4) a. Explain clearly why OLS estimators for the population parameters of a regression model are
North Texas - BIOL - 1130
This lecture will help you understand:0. 1. 2. Culture and worldviews Environmental ethics Classical economics and the environment 3. Economic growth and sustainability 4. Environmental and ecological economicsCentral Case: The Mirrar Clan Confron
UMass (Amherst) - RESEC - 312
Population ParametersEstimators (Sample)Univariate Measures (distribution of a single variable Y) Y = E [Y ] =Y=fi =1iNiYi =YYNiY =sY =Ynii (Y N=)2(Y=- Y)2n -1sY nSampling Distribution for YYY
North Texas - BIOL - 1130
Chapter 3 This lecture will help you understand:0. 1. 2. 3. 4. 5. Environmental policy's societal context U.S. environmental laws Different approaches to policy The policy process International environmental policy Transboundary issuesPolicy0. A
North Texas - BIOL - 1130
Chapter 4This lecture will help you understand:0. 1. 2. 3. 4. 5. Environmental chemistry Building blocks of life Energy and energy flow Photosynthesis, respiration, chemosynthesis Origin of life on Earth Early lifeCentral Case: Bioremediation of
UMass (Amherst) - RESEC - 312
Regression Analysis: Sales versus Prose, Pcarn, Disincome The regression equation is Sales = 13355 - 3628 Prose + 2634 Pcarn - 19.3 Disincom Predictor Constant Prose Pcarn Disincom S = 1076 Coef 13355 -3628.2 2634 -19.25 SE Coef 6485 635.6 ^ 1013 30
UMass (Amherst) - RESEC - 312
Upper percentage points of the F distribution. df for denominator N2 10 Pr 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05 0.01 0.25 0.10 0.05
North Texas - BIOL - 1130
Chapter 6This lecture will help you understand: Central Case: Black and White, and Spread All Over: Zebra Mussels Invade the Great Lakes0. The zebra mussel-a native of western Asia and eastern Europe-was discovered in the Great Lakes in 1988. 1. En
Ill. Chicago - MCS - 441
HW for Mar 31 Describe a 4-tape TM M which performs the task of determining the next state of a DFA N from its current state and the symbol it reads. More precisely (i) M starts in configuration (qs , (, w1 ), (, w2 ), (, w3 ), (, ) where w1 Rs Ra
UMass (Amherst) - RESEC - 312
Comparison of Linear and Nonlinear ModelsFunctional Form LinearP.R.E. Y = 0 + 1 X + uPartial Effect Y / XElasticity11X YQuadraticY = 0 + 1 X + 2 X 2 + u1 + 2 2 X( 1 + 2 2 X )X YReciprocalY = 0 + 11 + u X- 11 X2
North Texas - BIOL - 1130
&lt;?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?&gt; &lt;Error&gt;&lt;Code&gt;NoSuchKey&lt;/Code&gt;&lt;Message&gt;The specified key does not exist.&lt;/Message&gt;&lt;Key&gt;1b1d859d93ea895069c053aebab8175e7bd57bae.doc&lt;/Key&gt;&lt;RequestId&gt;5 B9C98CCF3A0A469&lt;/RequestId&gt;&lt;HostId&gt;E5YE+iLfvn+7XP84UM1CM+thRpl
North Texas - BIOL - 1130
This lecture will help you understand:0. Human population growth 1. How human population, affluence, and technology affect the environment 2. Demography 3. Demographic transition 4. How wealth, poverty, and status of women affect population growth 5
UMass (Amherst) - RESEC - 312
February 02 2009.GWB - Monday, February 02, 2009 - Page 1 of 15Captured on Mon Feb 02 2009 14:26:29February 02 2009.GWB - Monday, February 02, 2009 - Page 2 of 15Captured on Mon Feb 02 2009 14:30:34February 02 2009.GWB - Monday, February 02,
UMass (Amherst) - RESEC - 312
February 04 2009.GWB - Wednesday, February 04, 2009 - Page 1 of 14Captured on Wed Feb 04 2009 14:32:14February 04 2009.GWB - Wednesday, February 04, 2009 - Page 2 of 14Captured on Wed Feb 04 2009 14:35:33February 04 2009.GWB - Wednesday, Febr
North Texas - BIOL - 1130
This lecture will help you understand:0. 1. 2. 3. 4. 5. 6. 7. 8. Feeding a growing population &quot;Green revolution&quot; Pest management Pollination Genetically modified food Preserving crop diversity Feedlot agriculture Aquaculture Sustainable agriculture
UMass (Amherst) - RESEC - 312
February 09 2009.GWB - Monday, February 09, 2009 - Page 1 of 16Captured on Mon Feb 09 2009 14:32:37February 09 2009.GWB - Monday, February 09, 2009 - Page 2 of 16Captured on Mon Feb 09 2009 14:36:06February 09 2009.GWB - Monday, February 09,
UMass (Amherst) - RESEC - 312
Hypothesis Tests for Population Mean - x Standardized Testsz test: Population is normally distributed or sample size is large (n \$ 30). x is known t test:Population is normally distributed or sample size is large (n \$ 30). x is not known
North Texas - BIOL - 1130
This lecture will help you understand:0. 1. 2. 3. 4. 5. 6. 7. The scope of biodiversity on Earth Measuring biodiversity Extinction rates and mass extinction periods Causes of biodiversity loss Benefits of biodiversity Conservation biology Island bio