The PRINCOMP Procedure
|
|
|
- Randall Gallagher
- 5 years ago
- Views:
Transcription
1 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Food production variables The PRINCOMP Procedure Observations 16 Variables 4 Simple Statistics PRECIP ndvi aet temp Mean StD Correlation Matrix PRECIP ndvi aet temp PRECIP ndvi aet temp Eigenvalues of the Correlation Matrix Eigenvalue Difference Proportion Cumulative
2 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Food production variables The PRINCOMP Procedure Eigenvectors foodprod1 foodprod2 foodprod3 foodprod4 PRECIP ndvi aet temp
3 Grizzly Bear Project - Coastal Sites - invci First 2 principal components scores 15:14 Friday, June 11, Tweedsmuir Grid Grid Kimdean Grid N Grid Second component for productivity score 0-1 Gmu 9b Taku South Coast Khutzeymateen Owikeno Kingcome Stikine Unuk-Bradfield -2 Berners Bay First component for productivity score
4 Grizzly Bear Project - Coastal Sites - invci Plot of density_corrected vs first food production principal component score 15:14 Friday, June 11, Khutzeymateen Berners Bay 60 Taku Grizzly Bear Density Gmu 9b Stikine Kimdean Unuk-Bradfield Kingcome Grid N Grid Tweedsmuir Grid Owikeno 10 South Coast Mid Coast Mainlan Grid First component for productivity score
5 Grizzly Bear Project - Coastal Sites - invci Plot of density_corrected vs first food production principal component score 15:14 Friday, June 11, Khutzeymateen Berners Bay 60 Taku Grizzly Bear Density Stikine Unuk-Bradfield Gmu 9b Kingcome Kimdean Owikeno Grid N Grid South Coast Mid Coast Mainlan Grid Grid Tweedsmuir Second component for productivity score
6 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs model n _p aicc_ delta_aicc aicc_weight 1 Temp_T25_salmon NDVI_salmon T25_salmon Temp_T25_salmon_Hum NDVI_T25_salmon Temp_T25_salmon_Hum_Live T AET_T25_salmon Prcp_NDVI_Temp_salmon_Hum_Live NDVI_T AET_T25_salmon_Hum NDVI_H50_T25_salmon Prcp_Temp_T25_salmon_Hum_Live H50_T25_salmon_Hum NDVI_Temp_T25_salmon_Hum_Live Prcp_NDVI_H50_T Prcp_NDVI_Temp_salmon_Hum Prcp_NDVI_Temp_T25_salmon_Hum_Live Prcp_Temp_H50_T25_salmon_Hum Prcp_NDVI_H50_T25_I AET_T25_salmon_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon Prcp_NDVI_AET_salmon_Hum Prcp_NDVI_Temp_T25_salmon_Hum Prcp_NDVI_Temp_H50_salmon_Hum_Live
7 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs model n _p aicc_ delta_aicc aicc_weight 26 Prcp_NDVI_H50_T25_Hum Prcp_NDVI_Temp_H50_T25_Hum H50_T25_salmon_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon_Live NDVI_Temp_H50_T25_salmon_Hum Prcp_Temp_H50_T25_salmon_Hum_Live NDVI_Temp_H50_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_Hum_I Prcp_NDVI_Temp_H50_T25_salmon_LHum_Live Prcp_NDVI_Temp_H50_salmon_Hum Prcp_AET_T25_salmon_Hum_Live Prcp_NDVI_AET_salmon_Hum_Live NDVI_AET_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_salmon_Hum Prcp_NDVI_AET_T25_salmon_Hum Prcp_AET_H50_T25_salmon_Hum Prcp_NDVI_Temp_H50_T25_salmon_Hum Prcp_NDVI_AET_H50_T25_salmon NDVI_AET_H50_T25_salmon_Hum Prcp_NDVI_H50_T25_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon_LHum Prcp_NDVI_AET_H50_salmon_Hum Prcp_NDVI_AET_H50_T25_Hum Prcp_NDVI_Temp_H50_T25_Hum_Live Prcp_NDVI_Temp_H50_T25_Salmon_Hum_Live
8 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs model n _p aicc_ delta_aicc aicc_weight 51 Prcp_NDVI_Temp_H50_T25_salmon_Hum_Live GLOBAL_Prcp_NDVI_Temp_AET_H50_T25_salmon_Hum Prcp_NDVI_AET_H50_salmon_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_Live Prcp_NDVI_H50_T25_salmon_Hum_Live Prcp_NDVI_AET_T25_salmon_Hum_Live Prcp_AET_H50_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_Hum_Live_I NDVI_AET_H50_T25_salmon_Hum_Live Prcp_NDVI_AET_H50_T25_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_LHum Prcp_NDVI_AET_H50_T25_salmon_Hum Prcp_NDVI_Temp_H50_T25_salmon_LHum_LLive Prcp_NDVI_AET_H50_T25_salmon_LHum_Live Prcp_NDVI_AET_H50_T25_salmon_LHum_LLive Prcp_NDVI_AET_H50_T25_Salmon_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_Hum_Live
9 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 1 Temp_T25_salmon NDVI_salmon T25_salmon Temp_T25_salmon_Hum NDVI_T25_salmon Temp_T25_salmon_Hum_Live T AET_T25_salmon Prcp_NDVI_Temp_salmon_Hum_Live NDVI_T AET_T25_salmon_Hum
10 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 12 NDVI_H50_T25_salmon Prcp_Temp_T25_salmon_Hum_Live H50_T25_salmon_Hum NDVI_Temp_T25_salmon_Hum_Live Prcp_NDVI_H50_T Prcp_NDVI_Temp_salmon_Hum Prcp_NDVI_Temp_T25_salmon_Hum_Live Prcp_Temp_H50_T25_salmon_Hum Prcp_NDVI_H50_T25_I AET_T25_salmon_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon
11 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 23 Prcp_NDVI_AET_salmon_Hum Prcp_NDVI_Temp_T25_salmon_Hum Prcp_NDVI_Temp_H50_salmon_Hum_Live Prcp_NDVI_H50_T25_Hum Prcp_NDVI_Temp_H50_T25_Hum H50_T25_salmon_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon_Live NDVI_Temp_H50_T25_salmon_Hum Prcp_Temp_H50_T25_salmon_Hum_Live NDVI_Temp_H50_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_Hum_I
12 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 34 Prcp_NDVI_Temp_H50_T25_salmon_LHum_Live Prcp_NDVI_Temp_H50_salmon_Hum Prcp_AET_T25_salmon_Hum_Live Prcp_NDVI_AET_salmon_Hum_Live NDVI_AET_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_salmon_Hum Prcp_NDVI_AET_T25_salmon_Hum Prcp_AET_H50_T25_salmon_Hum Prcp_NDVI_Temp_H50_T25_salmon_Hum Prcp_NDVI_AET_H50_T25_salmon NDVI_AET_H50_T25_salmon_Hum
13 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 45 Prcp_NDVI_H50_T25_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon_LHum Prcp_NDVI_AET_H50_salmon_Hum Prcp_NDVI_AET_H50_T25_Hum Prcp_NDVI_Temp_H50_T25_Hum_Live Prcp_NDVI_Temp_H50_T25_Salmon_Hum_Live Prcp_NDVI_Temp_H50_T25_salmon_Hum_Live GLOBAL_Prcp_NDVI_Temp_AET_H50_T25_salmon_Hum Prcp_NDVI_AET_H50_salmon_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_Live Prcp_NDVI_H50_T25_salmon_Hum_Live
14 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 56 Prcp_NDVI_AET_T25_salmon_Hum_Live Prcp_AET_H50_T25_salmon_Hum_Live Prcp_NDVI_H50_T25_Hum_Live_I NDVI_AET_H50_T25_salmon_Hum_Live Prcp_NDVI_AET_H50_T25_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_LHum Prcp_NDVI_AET_H50_T25_salmon_Hum Prcp_NDVI_Temp_H50_T25_salmon_LHum_LLive Prcp_NDVI_AET_H50_T25_salmon_LHum_Live Prcp_NDVI_AET_H50_T25_salmon_LHum_LLive
15 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model selection results - Tobit Model - invci Obs Intercept PRECIP ndvi temp aet herb50 tree25 SALMON_AVG hum_den _Sigma Obs model livestock_den log_hum_den log_livestock_den ISti aicc_weight 66 Prcp_NDVI_AET_H50_T25_Salmon_Hum_Live Prcp_NDVI_AET_H50_T25_salmon_Hum_Live
16 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Model averaged predictions Obs Name Year Model averaged pred density Berners Bay Gmu 9b Grid Grid Grid N Grid Khutzeymateen Kimdean Kingcome Mid Coast Mainland Only Owikeno South Coast Stikine Taku Tweedsmuir Unuk-Bradfield
17 Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, Predictions from best Tobit model - invci - Temp_T25_salmon Obs Name Type Year Bias Density_corrected temp tree25 SALMON_AVG Predicted value of Density_corrected Residual of Density_corrected 1 Berners Bay T 2006 L Gmu 9b M 2000 U Grid T 2004 U Grid T 2005 U Grid N T 2006 U Grid T 2007 U Khutzeymateen T 1991 H Kimdean T 1999 L Kingcome M 1997 U Owikeno T 1998 L South Coast T 2004 L Stikine M 2004 U Taku T 2003 L Tweedsmuir T 1993 L Unuk-Bradfield T 2005 L Mid Coast Mainland Only M 2009 L
18 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Khutzeymateen 20 Taku 10 Residual 0-10 Stikine Unuk-Bradfield Predicted Grizzly Bear Density
19 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Average Precipitation
20 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Average Temperature
21 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Proportion of pixels with >50% herb coverage
22 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Livestock density
23 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Amount of salmon
24 Grizzly Bear - Coastal Model Residual plot from best model fitted using the Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Residual Human density
25 Grizzly Bear - Coastal Model 15:14 Friday, June 11, Normal q-q plot. Careful about points near zero The CAPABILITY Procedure Variable: Resid_Density_corrected (Residual of Density_corrected) Moments N 16 Sum Weights 16 Mean Sum Observations Std Deviation Variance Skewness Kurtosis Uncorrected SS Corrected SS Coeff Variation Std Error Mean Basic Statistical Measures Location Variability Mean Std Deviation Median Variance Mode. Range Interquartile Range Tests for Location: Mu0=0 Test Statistic p Value Student's t t Pr > t Sign M 0 Pr >= M Signed Rank S 2 Pr >= S
26 Grizzly Bear - Coastal Model 15:14 Friday, June 11, Normal q-q plot. Careful about points near zero The CAPABILITY Procedure Variable: Resid_Density_corrected (Residual of Density_corrected) Quantiles (Definition 5) Quantile Estimate 100% Max % % % % Q % Median % Q % % % % Min Extreme Observations Lowest Highest Value Obs Value Obs
27 Grizzly Bear - Coastal Model Normal q-q plot. Careful about points near zero 15:14 Friday, June 11, Residual of Density_corrected Normal Quantiles
28 Grizzly Bear - Coastal Model Observed vs predicted for best model fitted using the tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Observed corrected density 100 Khutzeymateen 50 Taku Stikine Unuk-Bradfield Predicted Grizzly Bear Density
29 Grizzly Bear - Coastal Model Observed vs predicted for best model fitted using the tobit model - invci - ci from observed overlaid temp tree25 salmon_avg 15:14 Friday, June 11, Observed corrected density Predicted Grizzly Bear Density
30 Grizzly Bear - Coastal Model Observed vs predicted for best model fitted using the tobit model - invci - ci on predicted overlaid temp tree25 salmon_avg 15:14 Friday, June 11, Observed corrected density Predicted Grizzly Bear Density
31 Grizzly Bear - Coastal Model 15:14 Friday, June 11, Best model predictions and prediction limits - data used in fitting - invci temp tree25 salmon_avg Obs NAME Density_corrected MY_PRED MY_PRED_SEP MY_LCL MY_UCL 1 Berners Bay Gmu 9b Grid Grid Grid N Grid Khutzeymateen Kimdean Kingcome Mid Coast Mainland Only Owikeno South Coast Stikine Taku Tweedsmuir Unuk-Bradfield
32 Grizzly Bear - Coastal Model Approximate 95% prediction limits from best Tobit model - invci temp tree25 salmon_avg 15:14 Friday, June 11, Observed corrected density 100 Khutzeymateen 50 Taku Stikine Unuk-Bradfield Prediced corrected density
33 Grizzly Bear - Coastal Model Compare model averaged vs best model predictions 15:14 Friday, June 11, Khutzeymateen Model averaged corrected density Tweedsmuir Best model corrected density
34 Grizzly Bear - Coastal Model 15:14 Friday, June 11, APPROXIMATE leverage plots Based on min aicc model: temp tree25 salmon_avg The REG Procedure Model: MODEL1 Dependent Variable: Density_corrected Number of Observations Read 16 Number of Observations Used 16 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > t Intercept temp tree SALMON_AVG
35 Grizzly Bear - Coastal Model 15:14 Friday, June 11, APPROXIMATE leverage plots Based on min aicc model: temp tree25 salmon_avg The REG Procedure Model: MODEL1 Dependent Variable: Density_corrected
36 Grizzly Bear - Coastal Model 15:14 Friday, June 11, APPROXIMATE leverage plots Based on min aicc model: temp tree25 salmon_avg The REG Procedure Model: MODEL1 Dependent Variable: Density_corrected
37 Grizzly Bear - Coastal Model 15:14 Friday, June 11, APPROXIMATE leverage plots Based on min aicc model: temp tree25 salmon_avg The REG Procedure Model: MODEL1 Partial Regression Residual Plot
EXST7034 Multiple Regression Geaghan Chapter 11 Bootstrapping (Toluca example) Page 1
Chapter 11 Bootstrapping (Toluca example) Page 1 Toluca Company Example (Problem from Neter, Kutner, Nachtsheim & Wasserman 1996,1.21) A particular part needed for refigeration equipment replacement parts
fruitfly fecundity example summary Tuesday, July 17, :13:19 PM 1
fruitfly fecundity example summary Tuesday, July 17, 2018 02:13:19 PM 1 The UNIVARIATE Procedure Variable: fecund line = NS Basic Statistical Measures Location Variability Mean 33.37200 Std Deviation 8.94201
Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
Guatemalan cholesterol example summary
Guatemalan cholesterol example summary Wednesday, July 11, 2018 02:04:06 PM 1 The UNIVARIATE Procedure Variable: level = rural Basic Statistical Measures Location Variability Mean 157.0204 Std Deviation
Stat 401 B Lecture 31
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
HASIL OUTPUT SPSS. Reliability Scale: ALL VARIABLES
139 HASIL OUTPUT SPSS Reliability Scale: ALL VARIABLES Case Processing Summary N % 100 100.0 Cases Excluded a 0.0 Total 100 100.0 a. Listwise deletion based on all variables in the procedure. Reliability
Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif
182 Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif Frequencies Statistics Kinerja Guru Sikap Guru Thdp Kepsek Motivasi Kerja Guru Kompetensi Pedagogik Guru N Valid 64 64 64 64 Missing
delivery<-read.csv(file="d:/chilo/regression 4/delivery.csv", header=t) delivery
Regression Analysis lab 4 1 Model Adequacy Checking 1.1 Import data delivery
tool<-read.csv(file="d:/chilo/regression 7/tool.csv", header=t) tool
Regression nalysis lab 7 1 Indicator variables 1.1 Import data tool
Stat 301 Lecture 26. Model Selection. Indicator Variables. Explanatory Variables
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan There is an indicator for Pickup but there are no pickups
Stat 401 B Lecture 27
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan There is an indicator for Pickup but there are no pickups
Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018
Question 1a output Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018 Question 1f output Basic Statistical Measures Location Variability Mean -2.32429 Std Deviation
. Enter. Model Summary b. Std. Error. of the. Estimate. Change. a. Predictors: (Constant), Emphaty, reliability, Assurance, responsive, Tangible
LAMPIRAN Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 Emphaty, reliability, Assurance, responsive, Tangible a. Enter a. All requested variables entered. b. Dependent Variable:
SEM over time. Changes in Structure, changes in Means
SEM over time Changes in Structure, changes in Means Measuring at two time points Is the structure the same Do the means change (is there growth) Create the data x.model
Lampiran 1. Data Perusahaan
Lampiran. Data Perusahaan NO PERUSH MV EARN DIV CFO LB.USAHA TOT.ASS ACAP 3 9 8 5 369 9678 376 ADES 75-35 - 6 3559-5977 7358 3 AQUA 5 368 65 335 797 678 53597 BATA 88 5 9 863 958 93 5 BKSL 5.3 -. 9-9 5
female male help("predict") yhat age
30 40 50 60 70 female male 1.0 help("predict") 0.5 yhat 0.0 0.5 1.0 30 40 50 60 70 age 30 40 50 60 70 1.5 1.0 female male help("predict") 0.5 yhat 0.0 0.5 1.0 1.5 30 40 50 60 70 age 2 Wald Statistics Response:
Drilling Example: Diagnostic Plots
Math 3080 1. Treibergs Drilling Example: Diagnostic Plots Name: Example March 1, 2014 This data is taken from Penner & Watts, Mining Information, American Statistician 1991, as quoted by Levine, Ramsey
Motor Trend Yvette Winton September 1, 2016
Motor Trend Yvette Winton September 1, 2016 Executive Summary Objective In this analysis, the relationship between a set of variables and miles per gallon (MPG) (outcome) is explored from a data set of
Model Information Data Set. Response Variable (Events) Summe Response Variable (Trials) N Response Distribution Binomial Link Function
02:32 Donnerstag, November 03, 2016 1 Model Information Data Set WORK.EXP Response Variable (Events) Summe Response Variable (Trials) N Response Distribution inomial Link Function Logit Variance Function
Análisis de la varianza (II)
01:43 Thursday, March 10, 2011 1 AGRO 6005 Análisis de la varianza (II) 1. Evaluación de supuestos. Los siguientes datos son recuentos de un áfido en trigo en 6 semanas diferentes. En cada ocasión se muestrearon
TRY OUT 25 Responden Variabel Kepuasan / x1
1 TRY OUT 25 Responden Variabel Kepuasan / x1 Case Processing Summary N % 25 100.0 Cases Excluded a 0.0 Total 25 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
The Coefficient of Determination
The Coefficient of Determination Lecture 46 Section 13.9 Robb T. Koether Hampden-Sydney College Tue, Apr 13, 2010 Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13,
Regression Models Course Project, 2016
Regression Models Course Project, 2016 Venkat Batchu July 13, 2016 Executive Summary In this report, mtcars data set is explored/analyzed for relationship between outcome variable mpg (miles for gallon)
TRY OUT 30 Responden Variabel Kompetensi/ x1
1 TRY OUT 30 Responden Variabel Kompetensi/ x1 Case Processing Summary N % 30 100.0 Cases Excluded a 0.0 Total 30 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
Descriptive Statistics
Chapter 2 Descriptive Statistics 2-1 Overview 2-2 Summarizing Data 2-3 Pictures of Data 2-4 Measures of Central Tendency 2-5 Measures of Variation 2-6 Measures of Position 2-7 Exploratory Data Analysis
Motor Trend MPG Analysis
Motor Trend MPG Analysis SJ May 15, 2016 Executive Summary For this project, we were asked to look at a data set of a collection of cars in the automobile industry. We are going to explore the relationship
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS 5.1 Indicator-specific methodology The construction of the weight-for-length (45 to 110 cm) and weight-for-height (65 to 120 cm)
Cumulative Frequency Diagrams Question Paper 1
Frequency Diagrams Question Paper 1 Level Subject Exam Board IGCSE Maths Edexcel Topic Handling Data Statistics Sub Topic Frequency Diagrams(Graphical representation of data) Booklet Question Paper 1 Time
UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI
1 UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI Case Processing Summary N % 20 100.0 Cases Excluded a 0.0 Total 20 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
Modeling Ignition Delay in a Diesel Engine
Modeling Ignition Delay in a Diesel Engine Ivonna D. Ploma Introduction The object of this analysis is to develop a model for the ignition delay in a diesel engine as a function of four experimental variables:
Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size
blu38582_if_1-8.qxd 9/27/10 9:19 PM Page 1 Important Formulas Chapter 3 Data Description Mean for individual data: Mean for grouped data: Standard deviation for a sample: X2 s X n 1 or Standard deviation
Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018
Review of Linear Regression I Statistics 211 - Statistical Methods II Presented January 9, 2018 Estimation of The OLS under normality the OLS Dan Gillen Department of Statistics University of California,
Universitas Sumatera Utara
LAMPIRAN I LAMPRIAN PDRB Harga Berlaku NO KAB/KOTA 2005 2006 2007 2008 2009 2010 1 Asahan 15527794210 6429147880 8174125380 9505603030 10435935630 11931676610 2 Dairi 2303591460 2552751860 2860204810 3116742540
Booklet of Code and Output for STAD29/STA 1007 Final Exam
Booklet of Code and Output for STAD29/STA 1007 Final Exam List of Figures in this document by page: List of Figures 1 Raisins data.............................. 2 2 Boxplot of raisin data........................
Statistics for Social Research
Facoltà di Scienze della Formazione, Scienze Politiche e Sociali Statistics for Social Research Lesson 2: Descriptive Statistics Prof.ssa Monica Palma a.a. 2016-2017 DESCRIPTIVE STATISTICS How do we describe
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas akpi ,97 51,04 40,
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas 2013 2014 2015 2013 2014 2015 2013 2014 2015 akpi 34 8 9 50,97 51,04 40,67 1.029.336.000.000 1.035.846.000.000 1.107.566.000.000 asii 216 216 177
Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL
Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL 87 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL 5.1 INTRODUCTION Maintenance is usually carried
Review of Upstate Load Forecast Uncertainty Model
Review of Upstate Load Forecast Uncertainty Model Arthur Maniaci Supervisor, Load Forecasting & Energy Efficiency New York Independent System Operator Load Forecasting Task Force June 17, 2011 Draft for
Relating your PIRA and PUMA test marks to the national standard
Relating your PIRA and PUMA test marks to the national standard We have carried out a detailed statistical analysis between the results from the PIRA and PUMA tests for Year 2 and Year 6 and the scaled
Relating your PIRA and PUMA test marks to the national standard
Relating your PIRA and PUMA test marks to the national standard We have carried out a detailed statistical analysis between the results from the PIRA and PUMA tests for Year 2 and Year 6 and the scaled
Identify Formula for Throughput with Multi-Variate Regression
DECISION SCIENCES INSTITUTE Using multi-variate regression and simulation to identify a generic formula for throughput of flow manufacturing lines with identical stations Samrawi Berhanu Gebermedhin and
R-Sq criterion Data : Surgical room data Chap 9
Chap 9 - For controlled experiments model reduction is not very important. P 347 - For exploratory observational studies, model reduction is important. Criteria for model selection p353 R-Sq criterion
DATA SAMPEL TAHUN 2006
DATA SAMPEL TAHUN 2006 No Nama Emiten CGPI Kode Saham Harga Saham EPS PER Laba Bersih 1 Bank Niaga 89.27 BNGA 920 54 17.02 647,732 2 Bank Mandiri 83.66 BMRI 2,900 118 24.65 2,422,472 3 Astra International
The Session.. Rosaria Silipo Phil Winters KNIME KNIME.com AG. All Right Reserved.
The Session.. Rosaria Silipo Phil Winters KNIME 2016 KNIME.com AG. All Right Reserved. Past KNIME Summits: Merging Techniques, Data and MUSIC! 2016 KNIME.com AG. All Rights Reserved. 2 Analytics, Machine
Antonio Olmos Priyalatha Govindasamy Research Methods & Statistics University of Denver
Antonio Olmos Priyalatha Govindasamy Research Methods & Statistics University of Denver American Evaluation Association Conference, Chicago, Ill, November 2015 AEA 2015, Chicago Ill 1 Paper overview Propensity
Lampiran 1. Penjualan PT Honda Mandiri Bogor
LAMPIRAN 64 Lampiran 1. Penjualan PT Honda Mandiri Bogor 29-211 PENJUALAN 29 TYPE JAN FEB MAR APR MEI JUNI JULI AGT SEP OKT NOV DES TOTA JAZZ 16 14 22 15 23 19 13 28 15 28 3 25 248 FREED 23 25 14 4 13
Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh
Statistic Methods in in Data Mining Business Understanding Data Understanding Data Preparation Deployment Modelling Evaluation Data Mining Process (Part 2) 2) Professor Dr. Gholamreza Nakhaeizadeh Professor
Preface... xi. A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content...
Contents Preface... xi A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content... xii Chapter 1 Introducing Partial Least Squares...
Appendix B STATISTICAL TABLES OVERVIEW
Appendix B STATISTICAL TABLES OVERVIEW Table B.1: Proportions of the Area Under the Normal Curve Table B.2: 1200 Two-Digit Random Numbers Table B.3: Critical Values for Student s t-test Table B.4: Power
Start Time. LOCATION: Scotts Valley Dr QC JOB #: SPECIFIC LOCATION: 0 ft from Tabor St. DIRECTION: EB/WB CITY/STATE: Scotts Valley, CA
Tube Counts Type of report: Tube Count - Volume Data LOCATION: Scotts Valley Dr QC JOB #: 245667 SPECIFIC LOCATION: ft from Tabor St DIRECTION: EB/WB CITY/STATE: Scotts Valley, CA DATE: Mar 2 24 - Mar
The Dynamics of Plug-in Electric Vehicles in the Secondary Market
The Dynamics of Plug-in Electric Vehicles in the Secondary Market Dr. Gil Tal gtal@ucdavis.edu Dr. Tom Turrentine Dr. Mike Nicholas Sponsored by the California Air Resources Board Population and Sampling
Technical Papers supporting SAP 2009
Technical Papers supporting SAP 29 A meta-analysis of boiler test efficiencies to compare independent and manufacturers results Reference no. STP9/B5 Date last amended 25 March 29 Date originated 6 October
AIC Laboratory R. Leaf November 28, 2016
AIC Laboratory R. Leaf November 28, 2016 In this lab we will evaluate the role of AIC to help us understand how this index can assist in model selection and model averaging. We will use the mtcars data
Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR
Tutorial 1 Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR Dataset for running Correlated Component Regression This tutorial 1 is based on data provided by Michel Tenenhaus and
Basic SAS and R for HLM
Basic SAS and R for HLM Edps/Psych/Soc 589 Carolyn J. Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Spring 2019 Overview The following will be demonstrated in
Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method
Econometrics for Health Policy, Health Economics, and Outcomes Research Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method
Linking the North Carolina EOG Assessments to NWEA MAP Growth Tests *
Linking the North Carolina EOG Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association
Robust alternatives to best linear unbiased prediction of complex traits
Robust alternatives to best linear unbiased prediction of complex traits WHY BEST LINEAR UNBIASED PREDICTION EASY TO EXPLAIN FLEXIBLE AMENDABLE WELL UNDERSTOOD FEASIBLE UNPRETENTIOUS NORMALITY IS IMPLICIT
Linking the Kansas KAP Assessments to NWEA MAP Growth Tests *
Linking the Kansas KAP Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. February 2016 Introduction Northwest Evaluation Association (NWEA
Table 2: Ethnic Diversity and Local Public Goods (Kenya and Tanzania) Annual school spending/ pupil, USD
Table 2: Ethnic Diversity and Local Public Goods (Kenya and Tanzania) Annual school spending/ pupil, USD desks/ pupil latrines/ pupil classrooms/ pupil Proportion wells with normal flow Explanatory variable
Linking the New York State NYSTP Assessments to NWEA MAP Growth Tests *
Linking the New York State NYSTP Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association
Interstate Freight in Australia,
Interstate Freight in Australia, 1972 2005 Leo Soames, Afzal Hossain and David Gargett Bureau of Transport and Regional Economics, Department of Transport and Regional Services, Canberra, ACT, Australia
DEFECT DISTRIBUTION IN WELDS OF INCOLOY 908
PSFC/RR-10-8 DEFECT DISTRIBUTION IN WELDS OF INCOLOY 908 Jun Feng August 10, 2010 Plasma Science and Fusion Center Massachusetts Institute of Technology Cambridge, MA 02139, USA This work was supported
Exercises An Introduction to R for Epidemiologists using RStudio SER 2014
Exercises An Introduction to R for Epidemiologists using RStudio SER 2014 S Mooney June 18, 2014 1. (a) Create a vector of numbers named odd.numbers with all odd numbers between 1 and 19, inclusive odd.numbers
The Incubation Period of Cholera: A Systematic Review Supplement. A. S. Azman, K. E. Rudolph, D.A.T. Cummings, J. Lessler
The Incubation Period of Cholera: A Systematic Review Supplement A. S. Azman, K. E. Rudolph, D.A.T. Cummings, J. Lessler 1 Basic Model Our models follow the approach for analysis of coarse data from Reich
Descriptive Statistics Practice Problems (99-04)
Descriptive Statistics Practice Problems (99-04) 1. One thousand candidates sit an examination. The distribution of marks is shown in the following grouped frequency table. Marks 1 10 11 20 21 30 31 40
4th VALUE Training School Validation of Regional Climate Change Projections. Pitfalls II
4th VALUE Training School Validation of Regional Climate Change Projections Pitfalls II Douglas Maraun Wegener Center for Climate and Global Change Douglas Maraun MOS ICTP, Trieste 1 / 53 Outline Perfect
Linking the Virginia SOL Assessments to NWEA MAP Growth Tests *
Linking the Virginia SOL Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association (NWEA
Linking the Georgia Milestones Assessments to NWEA MAP Growth Tests *
Linking the Georgia Milestones Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. February 2016 Introduction Northwest Evaluation Association
Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data
Portland State University PDXScholar Center for Urban Studies Publications and Reports Center for Urban Studies 7-1997 Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data
LAMPIRAN A. Tabulasi Data Perusahaan Sample
LAMPIRAN A Tabulasi Data Perusahaan Sample Current Ratio (%) NO Kode Emiten Nama Perusahaan ASII Astra International Tbk. 2 AUTO Astra Otoparts Tbk. 3 BATA Sepatu Bata Tbk. 4 BRAM Indo Kordsa Tbk 5 BRNA
Introduction. Traffic data collection. Introduction. Introduction. Traffic stream parameters
Introduction Traffic data collection Transportation Systems Engineering Outline Point measurement Measurement over a short stretch Measurement over a long stretch Measurement over an area 20080813 Traffic
FRICTION POTENTIAL AND SAFETY : PREDICTION OF
BRITE EURAM PROJECT BRPR CT97 0461 2 ND INTERNATIONAL COLLOQUIUM ON VEHICLE TYRE ROAD INTERACTION FRICTION POTENTIAL AND SAFETY : PREDICTION OF HANDLING BEHAVIOR FLORENCE, FEBRUARY 23 rd 2001 Title : Correlations
IASI Conference Nov., 2007 Atlanthal Hotel Anglet, France
MAIA Cloud Detection, IASI/AVHRR Cloud Clearing & Their Impact on Sounding Retrieval Allen Huang, Hong Zhang, Elisabeth Weisz, Hal Woolf & Jun Li Cooperative Institute for Meteorological Satellite Studies
Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran
Statistics and Quantitative Analysis U4320 Segment 8 Prof. Sharyn O Halloran I. Introduction A. Overview 1. Ways to describe, summarize and display data. 2.Summary statements: Mean Standard deviation Variance
LAMPIRAN 1. Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari Februari 2012
LAMPIRAN 1 Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari 2011 29 Februari 2012 No Tanggal Indeks Harga Saham No Tanggal Indeks Harga Saham 1 20-Jan-011 2.35 138 05-Agst-011 1.95 2
Reliability and Validity of Seat Interface Pressure to Quantify Seating Comfort in Motorcycles
Reliability and Validity of Seat Interface Pressure to Quantify Seating Comfort in Motorcycles Sai Praveen Velagapudi a,b, Ray G. G b a Research & Development, TVS Motor Company, INDIA; b Industrial Design
DRIVER SPEED COMPLIANCE WITHIN SCHOOL ZONES AND EFFECTS OF 40 PAINTED SPEED LIMIT ON DRIVER SPEED BEHAVIOURS Tony Radalj Main Roads Western Australia
DRIVER SPEED COMPLIANCE WITHIN SCHOOL ZONES AND EFFECTS OF 4 PAINTED SPEED LIMIT ON DRIVER SPEED BEHAVIOURS Tony Radalj Main Roads Western Australia ABSTRACT Two speed surveys were conducted on nineteen
Linking the Alaska AMP Assessments to NWEA MAP Tests
Linking the Alaska AMP Assessments to NWEA MAP Tests February 2016 Introduction Northwest Evaluation Association (NWEA ) is committed to providing partners with useful tools to help make inferences from
Lampiran 1. Tabel Sampel Penelitian
Lampiran 1 Tabel Sampel Penelitian No Kode Emiten Nama Perusahaan Tanggal IPO 1 APLN Agung Podomoro Land Tbk 11 Nov 2010 2 ASRI Alam Sutera Reality Tbk 18 Dec 2007 3 BAPA Bekasi Asri Pemula Tbk 14 Jan
TABLE 4.1 POPULATION OF 100 VALUES 2
TABLE 4. POPULATION OF 00 VALUES WITH µ = 6. AND = 7.5 8. 6.4 0. 9.9 9.8 6.6 6. 5.7 5. 6.3 6.7 30.6.6.3 30.0 6.5 8. 5.6 0.3 35.5.9 30.7 3.. 9. 6. 6.8 5.3 4.3 4.4 9.0 5.0 9.9 5. 0.8 9.0.9 5.4 7.3 3.4 38..6
Lecture 3: Measure of Central Tendency
Lecture 3: Measure of Central Tendency Donglei Du (ddu@unb.edu) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) ADM 2623: Business Statistics
LAMPIRAN DAFTAR SAMPEL PENELITIAN. Kriteria No. Nama Perusahaan. Sampel Emiten
LAMPIRAN DAFTAR SAMPEL PENELITIAN Kode Kriteria No. Nama Perusahaan Sampel Emiten 1 2 3 1. AGRO PT. Bank Agroniaga, Tbk 1 2. BABP PT. Bank ICB Bumiputera Indonesia, Tbk X - 3. BBCA PT. Bank Central Asia,
ACCIDENT MODIFICATION FACTORS FOR MEDIAN WIDTH
APPENDIX G ACCIDENT MODIFICATION FACTORS FOR MEDIAN WIDTH INTRODUCTION Studies on the effect of median width have shown that increasing width reduces crossmedian crashes, but the amount of reduction varies
LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile No Kode Nama Perusahaan Hasil z-score FD Non-FD
87 LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile 2010-2014 No Kode Nama Perusahaan Hasil z-score FD Non-FD 1 ADMG PT Polychem Indonesia Tbk 1,39 1 2 ARGO PT Argo Pantes Tbk 0,93 1 3 CTNX PT
Ecography. Supplementary material
Ecography ECOG-01322 Karger, D. N., Tuomisto, H., Amoroso, V. B., Darnaedi, D., Hidayat, A., Abrahamczyk, S., Kluge, J., Lehnert, M. and Kessler, M. 2015. The importance of species pool size for community
LET S ARGUE: STUDENT WORK PAMELA RAWSON. Baxter Academy for Technology & Science Portland, rawsonmath.
LET S ARGUE: STUDENT WORK PAMELA RAWSON Baxter Academy for Technology & Science Portland, Maine pamela.rawson@gmail.com @rawsonmath rawsonmath.com Contents Student Movie Data Claims (Cycle 1)... 2 Student
FINAL REPORT AP STATISTICS CLASS DIESEL TRUCK COUNT PROJECT
FINAL REPORT AP STATISTICS CLASS 2017-2018 DIESEL TRUCK COUNT PROJECT Authors: AP Statistics Class 2017-2018 Table of Contents SURVEY QUESTION...p. 2 AIR QUALITY...p. 3-4 TOTAL TRUCK COUNTS.p. 5 TRUCK
Improving Analog Product knowledge using Principal Components Variable Clustering in JMP on test data.
Improving Analog Product knowledge using Principal Components Variable Clustering in JMP on test data. Yves Chandon, Master BlackBelt at Freescale Semiconductor F e b 2 7. 2015 TM External Use We Touch
Supplementary Online Content
Supplementary Online Content Tilburt JC, Wynia MK, Sheeler RD, et al. Views of US physicians about controlling health care costs. JAMA. doi:10.1001/jama.2013.8278. Appendix A. Survey Items from Physicians,
APPENDIX A. A.1 Sample of Indian MFIs
APPENDIX A A.1 Sample of Indian MFIs S.N. Sample of Indian MFIs Inception Legal Form 1 SEWA 1974 BANK 2 CRESA 1980 NGO 3 SKDRDP 1982 NGO 4 GRAMA VIDYALAY 1983 NGO 5 JANODAYA 1987 NGO 6 RASS 1989 NGO 7
Using Statistics To Make Inferences 6. Wilcoxon Matched Pairs Signed Ranks Test. Wilcoxon Rank Sum Test/ Mann-Whitney Test
Using Statistics To Make Inferences 6 Summary Non-parametric tests Wilcoxon Signed Ranks Test Wilcoxon Matched Pairs Signed Ranks Test Wilcoxon Rank Sum Test/ Mann-Whitney Test Goals Perform and interpret
1 - Inventory regime changes: An application of Hamilton s state space model
GLOBAL INSIGHT S WORLD ECONOMIC OUTLOOK CONFERENCE Washington D.C., October 26 1 - Inventory regime changes: An application of Hamilton s state space model 2 Steel Demand Trends: To focus on main drivers
Assignment 3 solutions
Assignment 3 solutions Question 1: SVM on the OJ data (a) [2 points] Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations. library(islr)
Linking the Mississippi Assessment Program to NWEA MAP Tests
Linking the Mississippi Assessment Program to NWEA MAP Tests February 2017 Introduction Northwest Evaluation Association (NWEA ) is committed to providing partners with useful tools to help make inferences
Sharif University of Technology. Graduate School of Management and Economics. Econometrics I. Fall Seyed Mahdi Barakchian
Sharif University of Technology Graduate School of Management and Economics Econometrics I Fall 2010 Seyed Mahdi Barakchian Textbook: Wooldridge, J., Introductory Econometrics: A Modern Approach, South
Linking the Indiana ISTEP+ Assessments to the NWEA MAP Growth Tests. February 2017 Updated November 2017
Linking the Indiana ISTEP+ Assessments to the NWEA MAP Growth Tests February 2017 Updated November 2017 2017 NWEA. All rights reserved. No part of this document may be modified or further distributed without
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC)
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC) FULLY AUTOMATED ASTM D2983 CONDITIONING AND TESTING ON THE CANNON TESC SYSTEM WHITE PAPER A critical performance parameter for transmission, gear, and hydraulic
Linking the Florida Standards Assessments (FSA) to NWEA MAP
Linking the Florida Standards Assessments (FSA) to NWEA MAP October 2016 Introduction Northwest Evaluation Association (NWEA ) is committed to providing partners with useful tools to help make inferences
Improving CERs building
Improving CERs building Getting Rid of the R² tyranny Pierre Foussier pmf@3f fr.com ISPA. San Diego. June 2010 1 Why abandon the OLS? The ordinary least squares (OLS) aims to build a CER by minimizing