# 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.

3 Pages

### nearNeighborCounter

Course: CSC 250, Fall 2009
School: Union College
Rating:

#### Document Preview

Sorry, a summary is not available for this document. Register and Upgrade to Premier to view the entire document.

Register Now

#### Unformatted Document Excerpt

Coursehero >> Nebraska >> Union College >> CSC 250

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.
There is no excerpt for this document.
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:

Union College - CSC - 250
Union College - CSC - 250
Union College - CSC - 250
4B8A3.2D C-5
Union College - CSC - 250
-21-5-1 0 1 311
Cornell - CS - 100
27. Design &amp; Model ParametersGoogle PageRank (Cont'd) Optimizing Gear Ratio Distribution.2 .7A Network.32.1.6 .2.31.13.5Updating the State Vectorfunction w = Update(P,v) n = length(v); w = zeros(n,1); for i=1:n for j=1:n w(i) =
UMass (Amherst) - BE - 740
Multivariate Statistical Methods Fall 2003 Time: Tu/Th 1:00-2:15 Location: Arnold 120 Instructor: Edward Stanek E-mail: STANEK@SCHOOLPH.UMASS.EDU Office: Arnold 404 Office Hours: Tu/Th 4:00-5:15 or by appointment Course Home Page: http:/www-unix.oit.
UMass (Amherst) - BE - 740
An Introduction to Vectors and Matrices Ed Stanek Introduction We introduce vectors and matrices for use in statistics. The introduction is intended for an audience with no previous matrix background. Basic terms and operations are defined and illust
UMass (Amherst) - BE - 740
Example of How to Compute a Binomial Probability Using MinitabExample (from Daniels, 6th ed., page 90) Suppose that it is known that in a certain population 10% of the population is colorblind. If a random sample of 25 people is drawn from this popu
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p9.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;* ** *;* RE:Downlord the dataset from website and saved in *;* &quot;
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p1.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;* ** *;* RE:print dataset *;* INP
UMass (Amherst) - BE - 740
PROGRAM:JL03p10.SAS on 21 JAN 2003 by Jingsong LuTable 1. List the Values in beat pulse(beats/min)From students in BIOEPI 540 Fall 2002 Gender* Pulse*per Age*at* male(1)* Ave*resting* min*after*Re
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p1.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;* ** *;* RE:Read in dataset and *;*
UMass (Amherst) - BE - 740
PROGRAM:JL03p1.SAS on 21 JAN 2003 by Jingsong LuTable 1. Mean PLUSE0The MEANS Procedure Analysis Variable : pulse0 Ave*resting*pulse*per min*PULSE0 N Mean Std Dev Minimum Maximum31 67.6774194
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p2.SAS on 21 JAN 2003 by Jingsong Lu&quot;;* ** *;* RE:Read in dataset and test if mean pulse0 is 60 *;* INPU
UMass (Amherst) - BE - 740
PROGRAM:JL03p2.SAS on 21 JAN 2003 by Jingsong Lu Table 1. t-test of the null hypothesis the resting pulse is 60The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper C
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p3.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;* **;* RE:Create format for varible gender *;* INPUT
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p4.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;**;* RE:Use format for varible gender and two-sample *;* t
UMass (Amherst) - BE - 740
PROGRAM:JL03p4.SAS on 21 JAN 2003 by Jingsong LuTable 1.Compare the mean resting pulse in Male and FemaleThe TTEST Procedure Statistics Lower CL Upper CL Lower CLVariable
UMass (Amherst) - BE - 740
PROGRAM:JL03p5.SAS on 21 JAN 2003 by Jingsong LuTable 1.Compare the pulse0 and pulse1The TTEST Procedure Statistics Lower CL Upper CL Lower CLDifference N
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p6.SAS on 21 JAN 2003 by Jingsong Lu&quot;;* **;* RE:graphic relationship between pulse0 and pulse1 *;
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p7.SAS on 21 JAN 2003 by Jingsong Lu&quot;;* **;* RE: Simple linear Regression *;
UMass (Amherst) - BE - 740
PROGRAM:JL03p7.SAS on 21 JAN 2003 by Jingsong LuTable 1. Linear Regression PULSE1 on PULSE0The REG ProcedureModel: MODEL1Dependent Variable: pulse1 Pulse*per min*after*Exercise*PULSE1 Analysis of Variance
UMass (Amherst) - BE - 740
OPTIONS PAGESIZE=55 LINESIZE=78 NODATE NOCENTER NONUMBER;*;* PROJECT: BE640 sample program *;TITLE1 &quot;PROGRAM:JL03p8.SAS on 21 JAN 2003 by Jingsong Lu&quot; ;**;* RE:Measure difference between pulse0 and pulse1 *;* usin
UMass (Amherst) - BE - 740
PROGRAM:JL03p8.SAS on 21 JAN 2003 by Jingsong LuTable 1.Test for difference between pulse0 and pulse0The GLM ProcedureNumber of observations 31PROGRAM:JL03p8.SAS on 21 JAN 2003 by Jingsong LuTable 1.Test for difference between pulse0 and
Rutgers - MMS - 21578
afghanistanalbania algeria american-samoaandorra angola anguillaantigua argentina arubaaustralia austria bahamas bahrain bangladesh barbadosbelgium belize beninbermuda bhutan bolivia botswanabrazil british-virgin-islands
Sveriges lantbruksuniversitet - ECON - 811
Econ. 811 Summer 2007 Homework Set 2 1. Vasicek interest rate model: In the Vasicek (1977) one factor interest rate model, the instantaneous risk-free rate is assumed to follow the Ito process dr = ( r) dt + dz with market price of r-risk being . A
University of Texas - CS - 329
@ColorName@|@ColorHexRGBValue@@aqua@|@#00FFFF@@black@|@#000000@@blue@|@#0000FF@@fuchsia@|@#FF00FF@@gray@|@#808080@@green@|@#008000@@lime@|@#00FF00@@maroon@|@#800000@@navy@|@#000080@@olive@|@#808000@@purple@|@#800080@@red@|@#FF0000@@silve
Iowa State - STAT - 401
state sat takers income years public expend rankIowa 1088 3 326 16.79 87.8 25.60 89.7SouthDakota 1075 2 264 16.07 86.2 19.95 90.6NorthDakota 1068 3 317 16.57 88.3 20.62 89.8Kansas
Iowa State - STAT - 401
a 29.9a 11.4b 26.6b 23.7a 25.3b 28.5b 14.2b 17.9a 16.5a 21.1b 24.3
Sveriges lantbruksuniversitet - IAT - 410
IAT 410: Advanced Game Design October 15, 2007 Lab 2 Deep Field David Milam Carmen Chow Royce Sin Yvonne Lei Michael ChuiPrototype EvaluationIAT 410: Advanced Game DesignPre- Playtesting: Evolution of our Game Concept1.We distilled the origin
Sveriges lantbruksuniversitet - IAT - 410
December 3, 2007 IAT 410: Advanced Game Design Bizarre Hassle Final Documentation David Milam Team Leader - Integration Specialist Royce Sin Lead Programmer Carmen Chow Sound Designer and Level Creator Michael Chui - Modeler Texture Artist Anim
UNC - MATH - 030
Angles and Radians of a Unit CirclePositive: sin, csc Negative: cos, tan, sec, cot(0,1)Positive: sin, cos, tan, csc, sec, tan Negative: none3 / 4(2 / 3 1/ 2, 3 / 2)( , + )II /290!tan 90! = undefined /3(5 / 62 / 2, 2 / 2)
Cornell - EE - 476
DAC0808 8-Bit D/A ConverterMay 1999DAC0808 8-Bit D/A ConverterGeneral DescriptionThe DAC0808 is an 8-bit monolithic digital-to-analog converter (DAC) featuring a full scale output current settling time of 150 ns while dissipating only 33 mW wit
IUPUI - CS - 240
CSCI 265 Project 8 Hilbert Curve 40 pointsProblem StatementThe purpose of this assignment is to display Hilbert Curves of orders 1 through 8 using Qt.Hilbert CurvesHilbert curves are space-filling curves the visit every point in a two-dimension
University of Florida - MAR - 5621
Acadia, LA 3.67 Ada, ID 9.25 Adams, CO 7.49 Adams, IN 7.82 Aiken, SC 6.45 Alachua, FL 8.24 Alamance, NC 8.58 Alameda, CA 7.63 Albany, NY 10.64 Albemarle, VA 3.17 Alexander, NC 3.8 Alexandria, VA 12.11 Allegan, MI 5.26 Allegany, MD 7.68 Allegheny, PA
University of Florida - MAR - 5621
City Los Angeles-Long Beach Denver San Francisco-Oakland Dallas-Fort Worth Miami Atlanta Houston Seattle New York Memphis New Orleans Cleveland Chicago Detroit Minneapolis-St Paul Baltimore Philadelphia BostonYield (Y)Loan/Mrtg (X1) Distance (X2)
University of Florida - STA - 6207
Index (I) DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA DJIA POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR POOR NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE NYSE
University of Florida - STA - 3024
40Chapter 15: Logistic RegressionIntroductionThe simple and multiple linear regression methods we studied in Chapters 10 and 11 are used to model the relationship between a quantitative response variable and one or more explanatory variables. A
University of Florida - STA - 6127
Essyqual Attract 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2Score 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 8.76 24.26 13.38 25.3 16.6
University of Florida - STA - 6127
Repeated Measures ANOVA US County Per Capita Income by Region 1969,1979,1989Response Variable _ &quot;Grouping&quot; Factor _ # Levels _ &quot;Subject&quot; Factor __ #Levels/Group _ &quot;Time&quot; Factor _ # Levels _Region\Year 1 2 3 4 Overall196919791989OverallAn
University of Florida - STA - 6127
Analysis of Covariance Combines linear regression and ANOVA Can be used to compare g treatments, after controlling for quantitative factor believed to be related to response (e.g. pre-treatment score) Can be used to compare regression equations am
University of Florida - STA - 6127
Model Diagnostics Agricultural Intensity DataANOVAb Model 1 Sum of Squares 4.000 .849 4.848 df 5 23 28 Mean Square .800 .037 F 21.680 Sig. .000aRegression Residual Totala. Predictors: (Constant), POPDRY, ALLUV, POP, POPLVSTK, DRYSSN b. Dependent
University of Florida - STA - 6127
yearengines seriesyr type 1925 67713 0 1925 1 0 1927 64843 1 1927 14 1 1929 60572 2 1929 25 2 1931 57820 3 1931 80 3 1933 53302 4 1933 85 4 1935 48477 5 1935 130 5 1937 46342 6 1937 293 6 1939 43604 7 1939 639 7 1941 41911 8 1941 1517 8 1943 41983
University of Florida - STA - 6127
1 186 4951 181 4771 176 4251 149 3221 184 4821 190 5871 158 3701 139 3221 175 4791 148 3751 152 3301 111 3001 141 3861 153 4011 190
University of Florida - STA - 6166
0 20-1.15449 -0.01962 0.84735 -0.90070 0.03715 1.17119 -0.99054 0.69877 -0.20065 -0.77485 0.33693 0.70562 0.43193 1.01906 0.86323 0.80548 0.28411 0.31660 0.86323 2.01519 -1.06649 1.64971 -0.56523 2.21538 0.46944 0.68130 -0.17317 -2.38477 -0.93355 -0
University of Florida - STA - 6166
Station WNHC WHTN WHAM WTVT WSVA KOSA WDAM KOB WJZ KOVR WKJG WBRC WDAF KMOX KOCO KNAC WJDM WTVC KCTV WAGM WLBZ WWTV WTRF WKTV KFRE WPRO WLOS WSOC WCAU WVUE WITIY 3620 257 900 2246 449 145 13 630 1854 898 765 5156 3375 2681 1258 233 38 547 70 264 21
University of Florida - STA - 6166
Chapter 1: Statistics and the Scientic Method1.1a. The population of interest is the weight of shrimp maintained on the specic diet for a period of 6 months. b. The sample is the 100 shrimp selected from the pond and maintained on the specic diet
University of Florida - STA - 6166
Chapter 2: Collecting Data Using Surveys and Scientic Studies2.1 The relative merits of the dierent types of sampling units depends on the availability of a sampling frame for individuals, the desired precision of the estimates from the sample to th
University of Florida - STA - 6166
Chapter 3: Summarizing Data3.1a. Pie Chart should be plotted. b. Bar Graph should be plotted.3.2a. A pie chart would not be appropriate since we are not proportionally allocating a sample or population into a number of categories. b. Bar Grap
University of Florida - STA - 6166
Chapter 4: Probability and Probability Distributions4.1 a. Subjective probability b. Relative frequency c. Classical d. Relative frequency e. Subjective probability f. Subjective probability g. Classical 4.2 Answers will vary depending on persons ex
University of Florida - STA - 6166
Chapter 5: Inferences about Population Central Values5.1 5.2a. All registered voters in the state. b. Simple random sample from a list of registered voters. We might think that the actual average lifetime is less than the proposed 1500 hours. a.
University of Florida - STA - 6166
Chapter 6: Inferences Comparing Two Population Central Values6.1 a. Reject Ho if |t| 2.064 b. Reject Ho if t 2.624 c. Reject Ho if t -1.860 6.2 Ho : 1 - 2 0 versus Ha : 1 - 2 &lt; 0; Reject Ho if t -1.703 71.5-79.8 1 1 = -2.6722 &lt; -1.703 Reject
University of Florida - STA - 6166
Chapter 7: Inferences about Population Variances7.1 a. 0.01 b. 0.90 c. 1 - 0.99 = 0.01 d. 1 - 0.01 - 0.01 = 0.98 7.2 Let 2 be the upper 100apercentile from a Chi-square distribution. a a. 21.92 b. 3.186 7.3 a. Let y be the quantity in a randomly sel
University of Florida - STA - 6166
Chapter 8: The Completely Randomized Design8.1 a. Yes, the mean for Device A is considerably (relative to the standard deviations) smaller than the mean for Device D. b. Ho : A = B = C = D versus Ha : Dierence in s Reject Ho if F F.05,3,20 = 3.10
University of Florida - STA - 6166
Chapter 9: More Complicated Experimental Designs9.1 a. The F-test from the ANOVA table tests 2-sided alternatives: Test Ho : Attend = DidN ot vs Ho : Attend = DidN ot The ANOVA table is given here: Source Pair Treatment Error Total DF 5 1 5 11 SS 13
University of Florida - STA - 6166
Chapter 10: Categorical Data10.1 a. Yes, because n = 30 &gt; 5 and n(1 ) = 120 &gt; 5. Samples with n &lt; 25 would be suspect. b. .2 1.645 (.2)(.8)/150 (0.15, 0.25) is a 90% C.I. for .10.2 When n &gt; 5 and n(1 ) &gt; 5. 10.3 a. = 1202/1504 = 0.8 95% C
University of Florida - STA - 6166
Chapter 11: Linear Regression and Correlation11.1 A scatterplot of the data is given here:4045353025y20 10 515 51015 x202511.2 The calculations are give here: i 1 2 3 4 5 6 Total xi 5 10 12 15 18 24 84 yi 10 19 2
University of Florida - STA - 6166
Chapter 12: Multiple Regression12.1 a. A scatterplot of the data is given here:Plot of Drug Potency versus Dose Level3025 Potency1520 10 0055101520253035Dose Levelb. y = 8.667 + 0.575x c. From t
University of Florida - STA - 4321
Useful Rules From CalculusSeriescr m cr = 1 - r n=m nr &lt;1(Geomeric Series)xn e = n = 0 n!x e = 2.718281828.ln(1 + x) = (-1)n =1 nn +1xn = nx 2 x3 x - + + . 2 3 n n! = x x!(n - x)! (Binomial Series) (Taylor Series) n
University of Florida - STA - 4321
Number 0 2 14 35 23 4 16 33 21 6 18 31 19 8 12 29 25 10 27 00 1 13 36 24 3 15 34 22 5 17 32 20 7 11 30 26 9 28Color Green Black Red Black Red Black Red Black Red Black Red Black Red Black Red Black Red Black Red Green Red Black Red Black Red Black
University of Florida - STA - 4321
Number 00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36Color Green Green Red Black Red Black Red Black Red Black Red Black Black Red Black Red Black Red Black Red Red Black Red Black Red Black