Punctuated Equilibrium and Institutional Friction in Comparative Perspective
|
|
|
- Kevin Nelson
- 6 years ago
- Views:
Transcription
1 Punctuated Equilibrium and Institutional Friction in Comparative Perspective Frank R. Baumgartner, Pennsylvania State University Christian Breunig, University of Washington Christoffer Green-Pedersen, Aarhus University Bryan D. Jones, University of Washington Peter B. Mortensen, Aarhus University Michiel Nuytemans, University of Antwerp Stefaan Walgrave, University of Antwerp Web Appendix This appendix reports additional information excluded for reasons of space from the main body of our paper. The appendix is organized as follows*: 1. Data descriptions and variable names. This explains the structure of the related data file. 2. Descriptive statistics for each variable 3. Frequency distributions for each variable 4. Log-log and semi-log plots for each variable Note that sections 3 and 4 consist of a series of graphs, one series per page. Since there are 30 series, these sections are 90 pages long. These graphs are provided for completeness and need not be read in order to understand the paper. * Note that we added the Danish radio news data at the end of the appendix.
2 Comparative Institutional Friction Web Appendix Variable country year Year subcode item topic net R prop dif R dlc Perce dlp Perce Description 1=DK, 2=BEL, and 3=US ID for each country s series see below Categories of the series Label of the category aw value Proportion of annual agenda aw change nt-count change nt-percent change Table 1. Variables and their description ID Description Data Source and Identity 1 El ections Statistic Denmark, Befolkning og valg, Copenhagen, In terpellations Danish Agendas Project, 24 categories, Quest ions Danish Agendas Project, 24 categories, Laws Danish Agendas Project, 24 categories, Ap propriations Breunig, 22 categories, Spe nding Mortensen, 13 categories, Table 2. List of Danish series ID Description Data Source and Identity 1 Elections Federal Public Services, Demonstrations Belgian Political Agenda-Setting Project, 30 categories, Newspapers Belgian Political Agenda-Setting Project, 30 categories, TV Coverage Belgian Political Agenda-Setting Project, 30 categories, Party platforms Belgian Political Agenda-Setting Project, 30 categories, Interpellations Belgian Political Agenda-Setting Project, 30 categories, Government agreement Belgian Political Agenda-Setting Project, 30 categories, Written Questions Belgian Political Agenda-Setting Project, 30 categories, Bills Belgian Political Agenda-Setting Project, 30 categories, Laws Belgian Political Agenda-Setting Project, 30 categories, Executive Orders Belgian Political Agenda-Setting Project, 30 categories, Budgets Belgian Political Agenda-Setting Project, 30 categories, Table 3. List of Belgian series 2
3 Comparative Institutional Friction Web Appendix ID Description Data Source and Identity 1 Presidential vote Nardulli, , by state 2 House vote King, , by district 3 Senate vote Highton, , by seat 4 NYT stories Policy Agendas Project, , 19 categories 5 Bills House Wilkerson, (with missing), 19 categories 6 Bills Senate Wilkerson, , 19 categories 7 Hearings - House Policy Agendas Project, , 19 categories 8 Hearings - Senate Policy Agendas Project, , 19 categories 9 Executive Orders Policy Agendas Project, , 19 categories 10 CQ Stories Policy Agendas Project, , 19 categories 11 Statutes Policy Agendas Project, , 19 categories 12 Total Budget Outlays Policy Agendas Project, , 1 category 13 Budget Authority Policy Agendas Project, , 60 categories Descriptive Statistics Table 4. List of United States series Descriptive statistics for each variable follow. Note that all variables are calculated in terms of the percent of the total agenda at time t. Percent changes are then calculated from this base. (So if a given item is 10 percent of the total at time t and 11 percent at time t+1, then it scores as a 10 percent growth for time t+1.) Election figures, which are already expressed in terms of percentages, are not transformed. Finally, in order to avoid biasing our results in our favor, we have excluded any percent change figures that are over +1,000 percent. These are listed as missing data in the dataset. Categories with zero counts in either year are also deleted. (There are two reasons for this: First, if we multiplied the number of categories which had no observations for many time periods; each of these would show no change from time to time, thus inflating the apparent incidence of no change and the size of the central peak of the distribution. Second, percentages based on a baseline of zero are undefined.) Both of these decisions to exclude data are designed to be as conservative as possible so that any results confirming our hypotheses cannot possibly be related to these decisions; each works towards deflating rather than increasing the reported kurtosis scores. The tables below provides the basic descriptive 3
4 Comparative Institutional Friction Web Appendix statistics and Normality tests for all the series of each country (Tables 5-7). The descriptive statistics include the minimum, median, mean, maximum, inter-quartile range, variance, and the number of cases. The Normality tests compass Pearson s kurtosis measure, Hosking s L- kurtosis, the Kolmogorov-Smirnov test (D), and the Shapiro-Wilk Normality test (W). ID Description min median mean max IQR var N 1 Elections Interpellations Questions Laws Appropriations Spending Table 5a. Descriptive statistics for the Danish series. ID Description kurtosis L-kurtosis D p W p 1 Elections Interpellations Questions Laws Appropriations Spending Table 5b. Normality tests for the Danish series. ID Description min median mean max IQR var N 1 Elections Demonstrations Newspapers TV Coverage Party platforms Interpellations Government agreement Written Questions Bills Laws Executive Orders Budgets Table 6a. Descriptive statistics for the Belgian series. 4
5 Comparative Institutional Friction Web Appendix ID Description kurtosis L-kurtosis D p W p 1 Elections Demonstrations Newspapers TV Coverage Party platforms Interpellations Government agreement Written Questions Bills Laws Executive Orders Budgets Table 6b. Normality tests for the Belgian series. ID Description min median mean max IQR var N 1 Presid ential vote Ho use vote Senat e vote NYT stories Bills House Bills Senate Hearings - House Hearings - Senate Exec utive Orders C Q Stories St atutes To tal Budget Outlays Bu dget Authority Table 7a. Descriptive statistics for the United States series. ID Description kurtosis L-kurtosis D p W p 1 Presid ential vote Ho use vote Senat e vote NYT stories Bills House Bills Senate Hearings - House Hearings - Senate Exec utive Orders C Q Stories St atutes To tal Budget Outlays Bu dget Authority Table 7b. Normality tests for the United States series. 5
6 Comparative Institutional Friction Web Appendix Frequency Distributions Each of the plots in this section presents one series of data as a frequency distribution. The optimum bin size is calculated using the Sheather and Jones (1991) kernel density estimation. A Normal distribution with identical mean and variance is overlaid. The dashes at the top of the graphs indicate the observations at each value. 6
7 Histogram for Elections in Denmark Probability Density L kurtosis: 0.255
8 Histogram for Interpellations in Denmark Probability Density L kurtosis: 0.355
9 Histogram for Questions in Denmark Probability Density L kurtosis: 0.265
10 Histogram for Laws in Denmark Probability Density L kurtosis: 0.263
11 Histogram for Appropriations in Denmark Probability Density L kurtosis: 0.493
12 Histogram for Spending in Denmark Probability Density L kurtosis: 0.432
13 Histogram for Elections in Belgium Probability Density L kurtosis: 0.139
14 Histogram for Demonstrations in Belgium Probability Density L kurtosis: 0.296
15 Histogram for Newspapers in Belgium Probability Density L kurtosis: 0.192
16 Histogram for TV coverage in Belgium Probability Density L kurtosis: 0.305
17 Histogram for Party platforms in Belgium Probability Density L kurtosis: 0.256
18 Histogram for Interpellations in Belgium Probability Density L kurtosis: 0.304
19 Histogram for Government agreements in Belgium Probability Density L kurtosis: 0.379
20 Histogram for Written questions in Belgium Probability Density L kurtosis: 0.229
21 Histogram for Bills in Belgium Probability Density L kurtosis: 0.323
22 Histogram for Laws in Belgium Probability Density L kurtosis: 0.287
23 Histogram for Executive orders in Belgium Probability Density L kurtosis: 0.322
24 Histogram for Budgets in Belgium Probability Density L kurtosis: 0.642
25 Histogram for Presidential elections in the United States Probability Density L kurtosis: 0.252
26 Histogram for House elections in the United States Probability Density L kurtosis: 0.301
27 Histogram for Senate elections in the United States Probability Density L kurtosis: 0.219
28 Histogram for NYT stories in the United States Probability Density L kurtosis: 0.283
29 Histogram for House bills in the United States Probability Density L kurtosis: 0.206
30 Histogram for Senate bills in the United States Probability Density L kurtosis: 0.231
31 Histogram for House hearings in the United States Probability Density L kurtosis: 0.33
32 Histogram for Senate hearings in the United States Probability Density L kurtosis: 0.27
33 Histogram for Executive orders in the United States Probability Density L kurtosis: 0.254
34 Histogram for CQ stories in the United States Probability Density L kurtosis: 0.29
35 Histogram for Statutes in the United States Probability Density L kurtosis: 0.253
36 Histogram for Total outlays in the United States Probability Density L kurtosis: 0.544
37 Histogram for Budget authority in the United States Probability Density L kurtosis: 0.481
38 Comparative Institutional Friction Web Appendix Log-Log and Semi-Log Plots For each variable we present the cumulative number of observations at or above each value, once on a log-log scale and once where the values are on a logged scale but the frequencies are not (semi-log plot). Distributions that correspond to a Paretian, or power-law, distribution will display a straight line on the log-log plot. Distributions that are exponential will show a straight line on the semi-log presentation. Normal distributions would drop off quickly on the semi-log presentation, and even more quickly on the log-log presentation. We show both the and the tails separately on the same plots ( tails are multiplied by -1 in order to present them on the same scale as the tails). 7
39 Semi log plot for Elections in Denmark β pos = 0.14 R 2 = 0.98 β neg = 0.16 R 2 = 0.96
40 Semi log plot for Interpellations in Denmark β pos = 0 R 2 = 0.86 β neg = 0.02 R 2 = 0.98
41 Semi log plot for Questions in Denmark β pos = 0 R 2 = 0.97 β neg = 0.02 R 2 = 0.88
42 Semi log plot for Laws in Denmark β pos = 0 R 2 = 0.97 β neg = 0.02 R 2 = 0.95
43 Semi log plot for Appropriations in Denmark β pos = 0 R 2 = 0.59 β neg = 0.02 R 2 = 0.92
44 Semi log plot for Spending in Denmark β pos = 0.01 R 2 = 0.67 β neg = 0.04 R 2 = 0.96
45 Semi log plot for Elections in Belgium β pos = 0.07 R 2 = 0.77 β neg = 0.18 R 2 = 0.97
46 Semi log plot for Demonstrations in Belgium β pos = 0 R 2 = 0.97 β neg = 0.01 R 2 = 0.92
47 Semi log plot for Newspapers in Belgium β pos = 0.01 R 2 = 0.84 β neg = 0.03 R 2 = 0.99
48 Semi log plot for TV coverage in Belgium β pos = 0 R 2 = 0.78 β neg = 0.03 R 2 = 0.96
49 Semi log plot for Party platforms in Belgium β pos = 0 R 2 = 0.98 β neg = 0.01 R 2 = 0.92
50 Semi log plot for Interpellations in Belgium β pos = 0 R 2 = 0.97 β neg = 0.02 R 2 = 0.9
51 Semi log plot for Government agreements in Belgium β pos = 0 R 2 = 0.9 β neg = 0.01 R 2 = 0.98
52 Semi log plot for Written questions in Belgium β pos = 0.01 R 2 = 0.88 β neg = 0.03 R 2 = 0.94
53 Semi log plot for Bills in Belgium β pos = 0 R 2 = 0.94 β neg = 0.02 R 2 = 0.89
54 Semi log plot for Laws in Belgium β pos = 0 R 2 = 0.89 β neg = 0.02 R 2 = 0.78
55 Semi log plot for Executive orders in Belgium β pos = 0 R 2 = 0.87 β neg = 0.02 R 2 = 0.93
56 Semi log plot for Budgets in Belgium β pos = 0 R 2 = 0.65 β neg = 0.02 R 2 = 0.95
57 Semi log plot for Presidential elections in the United States β pos = 0.05 R 2 = 0.96 β neg = 0.04 R 2 = 0.97
58 Semi log plot for House elections in the United States β pos = 0.05 R 2 = 0.99 β neg = 0.05 R 2 = 0.98
59 Semi log plot for Senate elections in the United States β pos = 0.05 R 2 = 0.98 β neg = 0.04 R 2 = 0.99
60 Semi log plot for NYT stories in the United States β pos = 0 R 2 = 0.88 β neg = 0.03 R 2 = 0.91
61 Semi log plot for House bills in the United States β pos = 0.01 R 2 = 0.94 β neg = 0.03 R 2 = 0.97
62 Semi log plot for Senate bills in the United States β pos = 0 R 2 = 0.66 β neg = 0.03 R 2 = 0.97
63 Semi log plot for House hearings in the United States β pos = 0 R 2 = 0.93 β neg = 0.02 R 2 = 0.98
64 Semi log plot for Senate hearings in the United States β pos = 0 R 2 = 0.85 β neg = 0.03 R 2 = 0.9
65 Semi log plot for Executive orders in the United States β pos = 0 R 2 = 0.98 β neg = 0.02 R 2 = 0.84
66 Semi log plot for CQ stories in the United States β pos = 0 R 2 = 0.91 β neg = 0.03 R 2 = 0.92
67 Semi log plot for Statutes in the United States β pos = 0 R 2 = 0.93 β neg = 0.03 R 2 = 0.91
68 Semi log plot for Total outlays in the United States β pos = 0 R 2 = 0.64 β neg = 0.03 R 2 = 0.96
69 Semi log plot for Budget authority in the United States β pos = 0 R 2 = 0.86 β neg = 0.02 R 2 = 0.98
70 Log log plot for Elections in Denmark β pos= 1.18 R 2 = 0.82 β neg= 1.1 R 2 = Log of Change
71 Log log plot for Interpellations in Denmark β pos= 1.24 R 2 = 0.93 β neg= 1.23 R 2 = Log of Change
72 Log log plot for Questions in Denmark β pos= 1.27 R 2 = 0.84 β neg= 1.1 R 2 = Log of Change
73 Log log plot for Laws in Denmark β pos= 1.4 R 2 = 0.84 β neg= 1.14 R 2 = Log of Change
74 Log log plot for Appropriations in Denmark β pos= 1.01 R 2 = 0.97 β neg= 1.32 R 2 = Log of Change
75 Log log plot for Spending in Denmark β pos= 1.09 R 2 = 0.95 β neg= 1.31 R 2 = Log of Change
76 Log log plot for Elections in Belgium β pos= 1.36 R 2 = 0.83 β neg= 1.42 R 2 = Log of Change
77 Log log plot for Demonstrations in Belgium β pos= 1.01 R 2 = 0.85 β neg= 0.88 R 2 = Log of Change
78 Log log plot for Newspapers in Belgium β pos= 1.43 R 2 = 0.87 β neg= 1.78 R 2 = Log of Change
79 Log log plot for TV coverage in Belgium β pos= 1.09 R 2 = 0.93 β neg= 1.58 R 2 = Log of Change
80 Log log plot for Party platforms in Belgium β pos= 1.27 R 2 = 0.87 β neg= 0.92 R 2 = Log of Change
81 Log log plot for Interpellations in Belgium β pos= 1.07 R 2 = 0.85 β neg= 1.37 R 2 = Log of Change
82 Log log plot for Government agreements in Belgium β pos= 0.7 R 2 = 0.92 β neg= 0.76 R 2 = Log of Change
83 Log log plot for Written questions in Belgium β pos= 1.27 R 2 = 0.89 β neg= 1.37 R 2 = Log of Change
84 Log log plot for Bills in Belgium β pos= 1.12 R 2 = 0.9 β neg= 1.27 R 2 = Log of Change
85 Log log plot for Laws in Belgium β pos= 1.13 R 2 = 0.92 β neg= 1.54 R 2 = Log of Change
86 Log log plot for Executive orders in Belgium β pos= 1.11 R 2 = 0.91 β neg= 1.46 R 2 = Log of Change
87 Log log plot for Budgets in Belgium β pos= 0.72 R 2 = 0.97 β neg= 0.95 R 2 = Log of Change
88 Log log plot for Presidential elections in the United States β pos= 1.77 R 2 = 0.81 β neg= 1.68 R 2 = Log of Change
89 Log log plot for House elections in the United States β pos= 1.8 R 2 = 0.71 β neg= 1.68 R 2 = Log of Change
90 Log log plot for Senate elections in the United States β pos= 1.76 R 2 = 0.81 β neg= 1.67 R 2 = Log of Change
91 Log log plot for NYT stories in the United States β pos= 1.43 R 2 = 0.9 β neg= 1.6 R 2 = Log of Change
92 Log log plot for House bills in the United States β pos= 1.65 R 2 = 0.89 β neg= 1.86 R 2 = Log of Change
93 Log log plot for Senate bills in the United States β pos= 1.53 R 2 = 0.9 β neg= 1.76 R 2 = Log of Change
94 Log log plot for House hearings in the United States β pos= 1.36 R 2 = 0.91 β neg= 1.45 R 2 = Log of Change
95 Log log plot for Senate hearings in the United States β pos= 1.4 R 2 = 0.91 β neg= 1.84 R 2 = Log of Change
96 Log log plot for Executive orders in the United States β pos= 1.21 R 2 = 0.83 β neg= 1.5 R 2 = Log of Change
97 Log log plot for CQ stories in the United States β pos= 1.31 R 2 = 0.9 β neg= 1.64 R 2 = Log of Change
98 Log log plot for Statutes in the United States β pos= 1.36 R 2 = 0.87 β neg= 1.62 R 2 = Log of Change
99 Log log plot for Total outlays in the United States β pos= 0.91 R 2 = 0.97 β neg= 1.28 R 2 = Log of Change
100 Log log plot for Budget authority in the United States β pos= 1.12 R 2 = 0.92 β neg= 1.36 R 2 = Log of Change
101 Web Appendix add on We added the radio news coverage to the Danish series. In Table 2, the Danish radio series will be listed with the ID number 7. The series entails 23 categories and runs from To Table 5 a and b the following descriptive statistics can be added. mean median var IQR min max N KS-stat KS-p SW-stat SW-p kurtosis L-kurtosis The following graphics can also be added: Histogram for Radio News in Denmark Probability Density L kurtosis: 0.245
102 Log Log Plot for Radio News in Denmark β pos = 1.37 R 2 = 0.9 β neg = 1.73 R 2 = Log of Growth Rate
103 Semi Log Plot for Radio News in Denmark β pos = 0.01 R 2 = 0.89 β neg = 0.03 R 2 = Growth Rate
Last Lecture. Distinguish Populations from Samples. Knowing different Sampling Techniques. Distinguish Parameters from Statistics
Last Lecture Distinguish Populations from Samples Importance of identifying a population and well chosen sample Knowing different Sampling Techniques Distinguish Parameters from Statistics Knowing different
) The cumulative probability distribution shows the probability
Problem Set ECONOMETRICS Prof Òscar Jordà Due Date: Thursday, April 0 STUDENT S NAME: Multiple Choice Questions [0 pts] Please provide your answers to this section below: 3 4 5 6 7 8 9 0 ) The cumulative
Stat 101 Exam 1 Important Formulas and Concepts 1
1 Chapter 1 1.1 Definitions Stat 101 Exam 1 Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2. Categorical/Qualitative
Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:
Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the
Ø Set of mutually exclusive categories. Ø Classify or categorize subject. Ø No meaningful order to categorization.
Statistical Tools in Evaluation HPS 41 Dr. Joe G. Schmalfeldt Types of Scores Continuous Scores scores with a potentially infinite number of values. Discrete Scores scores limited to a specific number
QUANTITATIVE DATA. UNIVARIATE DATA data for one variable
QUANTITATIVE DATA Recall that quantitative (numeric) data values are numbers where data take numerical values for which it is sensible to find averages, such as height, hourly pay, and pulse rates. UNIVARIATE
Describing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
(quantitative or categorical variables) Numerical descriptions of center, variability, position (quantitative variables)
3. Descriptive Statistics Describing data with tables and graphs (quantitative or categorical variables) Numerical descriptions of center, variability, position (quantitative variables) Bivariate descriptions
Tabulation means putting data into tables. A table is a matrix of data in rows and columns, with the rows and the columns having titles.
1 Tabulation means putting data into tables. A table is a matrix of data in rows and columns, with the rows and the columns having titles. 2 converting the set of numbers into the form of a grouped frequency
Essential Academic Skills Subtest III: Mathematics (003)
Essential Academic Skills Subtest III: Mathematics (003) NES, the NES logo, Pearson, the Pearson logo, and National Evaluation Series are trademarks in the U.S. and/or other countries of Pearson Education,
Chapter 6. The Standard Deviation as a Ruler and the Normal Model 1 /67
Chapter 6 The Standard Deviation as a Ruler and the Normal Model 1 /67 Homework Read Chpt 6 Complete Reading Notes Do P129 1, 3, 5, 7, 15, 17, 23, 27, 29, 31, 37, 39, 43 2 /67 Objective Students calculate
SPSS LAB FILE 1
SPSS LAB FILE www.mcdtu.wordpress.com 1 www.mcdtu.wordpress.com 2 www.mcdtu.wordpress.com 3 OBJECTIVE 1: Transporation of Data Set to SPSS Editor INPUTS: Files: group1.xlsx, group1.txt PROCEDURE FOLLOWED:
Weighting Missing Data Coding and Data Preparation Wrap-up Preview of Next Time. Data Management
Data Management Department of Political Science and Government Aarhus University November 24, 2014 Data Management Weighting Handling missing data Categorizing missing data types Imputation Summary measures
Describing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
Descriptive Statistics
Descriptive Statistics CHAPTER OUTLINE 6-1 Numerical Summaries of Data 6- Stem-and-Leaf Diagrams 6-3 Frequency Distributions and Histograms 6-4 Box Plots 6-5 Time Sequence Plots 6-6 Probability Plots Chapter
Identify the scale of measurement most appropriate for each of the following variables. (Use A = nominal, B = ordinal, C = interval, D = ratio.
Answers to Items from Problem Set 1 Item 1 Identify the scale of measurement most appropriate for each of the following variables. (Use A = nominal, B = ordinal, C = interval, D = ratio.) a. response latency
What are the mean, median, and mode for the data set below? Step 1
Unit 11 Review Analyzing Data Name Per The mean is the average of the values. The median is the middle value(s) when the values are listed in order. The mode is the most common value(s). What are the mean,
unadjusted model for baseline cholesterol 22:31 Monday, April 19,
unadjusted model for baseline cholesterol 22:31 Monday, April 19, 2004 1 Class Level Information Class Levels Values TRETGRP 3 3 4 5 SEX 2 0 1 Number of observations 916 unadjusted model for baseline cholesterol
ParSCORE Marking System. Instructor User Guide
ParSCORE Marking System Instructor User Guide 2015 Summary Scanning Services is located in Learning and Technology Center in SE12-103N. Hours of Operation: Monday to Friday: 8:00am to 4:00pm Closed weekends
Preliminary Statistics course. Lecture 1: Descriptive Statistics
Preliminary Statistics course Lecture 1: Descriptive Statistics Rory Macqueen (rm43@soas.ac.uk), September 2015 Organisational Sessions: 16-21 Sep. 10.00-13.00, V111 22-23 Sep. 15.00-18.00, V111 24 Sep.
Elementary Statistics
Elementary Statistics Q: What is data? Q: What does the data look like? Q: What conclusions can we draw from the data? Q: Where is the middle of the data? Q: Why is the spread of the data important? Q:
What is statistics? Statistics is the science of: Collecting information. Organizing and summarizing the information collected
What is statistics? Statistics is the science of: Collecting information Organizing and summarizing the information collected Analyzing the information collected in order to draw conclusions Two types
Introduction to Statistics
Introduction to Statistics Data and Statistics Data consists of information coming from observations, counts, measurements, or responses. Statistics is the science of collecting, organizing, analyzing,
Ø Set of mutually exclusive categories. Ø Classify or categorize subject. Ø No meaningful order to categorization.
Statistical Tools in Evaluation HPS 41 Fall 213 Dr. Joe G. Schmalfeldt Types of Scores Continuous Scores scores with a potentially infinite number of values. Discrete Scores scores limited to a specific
BNG 495 Capstone Design. Descriptive Statistics
BNG 495 Capstone Design Descriptive Statistics Overview The overall goal of this short course in statistics is to provide an introduction to descriptive and inferential statistical methods, with a focus
Types of Information. Topic 2 - Descriptive Statistics. Examples. Sample and Sample Size. Background Reading. Variables classified as STAT 511
Topic 2 - Descriptive Statistics STAT 511 Professor Bruce Craig Types of Information Variables classified as Categorical (qualitative) - variable classifies individual into one of several groups or categories
a table or a graph or an equation.
Topic (8) POPULATION DISTRIBUTIONS 8-1 So far: Topic (8) POPULATION DISTRIBUTIONS We ve seen some ways to summarize a set of data, including numerical summaries. We ve heard a little about how to sample
Summarizing Measured Data
Summarizing Measured Data 12-1 Overview Basic Probability and Statistics Concepts: CDF, PDF, PMF, Mean, Variance, CoV, Normal Distribution Summarizing Data by a Single Number: Mean, Median, and Mode, Arithmetic,
Chapter 6 The Standard Deviation as a Ruler and the Normal Model
Chapter 6 The Standard Deviation as a Ruler and the Normal Model Overview Key Concepts Understand how adding (subtracting) a constant or multiplying (dividing) by a constant changes the center and/or spread
Lecture 2. Quantitative variables. There are three main graphical methods for describing, summarizing, and detecting patterns in quantitative data:
Lecture 2 Quantitative variables There are three main graphical methods for describing, summarizing, and detecting patterns in quantitative data: Stemplot (stem-and-leaf plot) Histogram Dot plot Stemplots
Math 120 Introduction to Statistics Mr. Toner s Lecture Notes 3.1 Measures of Central Tendency
Math 1 Introduction to Statistics Mr. Toner s Lecture Notes 3.1 Measures of Central Tendency The word average: is very ambiguous and can actually refer to the mean, median, mode or midrange. Notation:
2.0 Lesson Plan. Answer Questions. Summary Statistics. Histograms. The Normal Distribution. Using the Standard Normal Table
2.0 Lesson Plan Answer Questions 1 Summary Statistics Histograms The Normal Distribution Using the Standard Normal Table 2. Summary Statistics Given a collection of data, one needs to find representations
STT 315 This lecture is based on Chapter 2 of the textbook.
STT 315 This lecture is based on Chapter 2 of the textbook. Acknowledgement: Author is thankful to Dr. Ashok Sinha, Dr. Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit some of their
NEW YORK ALGEBRA TABLE OF CONTENTS
NEW YORK ALGEBRA TABLE OF CONTENTS CHAPTER 1 NUMBER SENSE & OPERATIONS TOPIC A: Number Theory: Properties of Real Numbers {A.N.1} PART 1: Closure...1 PART 2: Commutative Property...2 PART 3: Associative
Chapter 1 - Lecture 3 Measures of Location
Chapter 1 - Lecture 3 of Location August 31st, 2009 Chapter 1 - Lecture 3 of Location General Types of measures Median Skewness Chapter 1 - Lecture 3 of Location Outline General Types of measures What
MODULE 7 UNIVARIATE EDA - QUANTITATIVE
MODULE 7 UNIVARIATE EDA - QUANTITATIVE Contents 7.1 Interpreting Shape........................................ 46 7.2 Interpreting Outliers....................................... 47 7.3 Comparing the Median
Perhaps the most important measure of location is the mean (average). Sample mean: where n = sample size. Arrange the values from smallest to largest:
1 Chapter 3 - Descriptive stats: Numerical measures 3.1 Measures of Location Mean Perhaps the most important measure of location is the mean (average). Sample mean: where n = sample size Example: The number
Exam: 4 hour multiple choice. Agenda. Course Introduction to Statistics. Lecture 1: Introduction to Statistics. Per Bruun Brockhoff
Course 02402 Lecture 1: Per Bruun Brockhoff DTU Informatics Building 305 - room 110 Danish Technical University 2800 Lyngby Denmark e-mail: pbb@imm.dtu.dk Agenda 1 2 3 4 Per Bruun Brockhoff (pbb@imm.dtu.dk),
Practical Statistics for the Analytical Scientist Table of Contents
Practical Statistics for the Analytical Scientist Table of Contents Chapter 1 Introduction - Choosing the Correct Statistics 1.1 Introduction 1.2 Choosing the Right Statistical Procedures 1.2.1 Planning
SUMMARIZING MEASURED DATA. Gaia Maselli
SUMMARIZING MEASURED DATA Gaia Maselli maselli@di.uniroma1.it Computer Network Performance 2 Overview Basic concepts Summarizing measured data Summarizing data by a single number Summarizing variability
F78SC2 Notes 2 RJRC. If the interest rate is 5%, we substitute x = 0.05 in the formula. This gives
F78SC2 Notes 2 RJRC Algebra It is useful to use letters to represent numbers. We can use the rules of arithmetic to manipulate the formula and just substitute in the numbers at the end. Example: 100 invested
Survey on Population Mean
MATH 203 Survey on Population Mean Dr. Neal, Spring 2009 The first part of this project is on the analysis of a population mean. You will obtain data on a specific measurement X by performing a random
Statistics Toolbox 6. Apply statistical algorithms and probability models
Statistics Toolbox 6 Apply statistical algorithms and probability models Statistics Toolbox provides engineers, scientists, researchers, financial analysts, and statisticians with a comprehensive set of
Z score indicates how far a raw score deviates from the sample mean in SD units. score Mean % Lower Bound
1 EDUR 8131 Chat 3 Notes 2 Normal Distribution and Standard Scores Questions Standard Scores: Z score Z = (X M) / SD Z = deviation score divided by standard deviation Z score indicates how far a raw score
Section 3. Measures of Variation
Section 3 Measures of Variation Range Range = (maximum value) (minimum value) It is very sensitive to extreme values; therefore not as useful as other measures of variation. Sample Standard Deviation The
Chapter 2: Tools for Exploring Univariate Data
Stats 11 (Fall 2004) Lecture Note Introduction to Statistical Methods for Business and Economics Instructor: Hongquan Xu Chapter 2: Tools for Exploring Univariate Data Section 2.1: Introduction What is
Mathematics Grade 7 Transition Alignment Guide (TAG) Tool
Transition Alignment Guide (TAG) Tool As districts build their mathematics curriculum for 2013-14, it is important to remember the implementation schedule for new mathematics TEKS. In 2012, the Teas State
Percentile: Formula: To find the percentile rank of a score, x, out of a set of n scores, where x is included:
AP Statistics Chapter 2 Notes 2.1 Describing Location in a Distribution Percentile: The pth percentile of a distribution is the value with p percent of the observations (If your test score places you in
Dr. Maddah ENMG 617 EM Statistics 10/15/12. Nonparametric Statistics (2) (Goodness of fit tests)
Dr. Maddah ENMG 617 EM Statistics 10/15/12 Nonparametric Statistics (2) (Goodness of fit tests) Introduction Probability models used in decision making (Operations Research) and other fields require fitting
STATISTICS. 1. Measures of Central Tendency
STATISTICS 1. Measures o Central Tendency Mode, median and mean For a sample o discrete data, the mode is the observation, x with the highest requency,. 1 N F For grouped data in a cumulative requency
Statistics 100 Exam 2 March 8, 2017
STAT 100 EXAM 2 Spring 2017 (This page is worth 1 point. Graded on writing your name and net id clearly and circling section.) PRINT NAME (Last name) (First name) net ID CIRCLE SECTION please! L1 (MWF
Test 2C AP Statistics Name:
Test 2C AP Statistics Name: Part 1: Multiple Choice. Circle the letter corresponding to the best answer. 1. Which of these variables is least likely to have a Normal distribution? (a) Annual income for
Gov 2002: 3. Randomization Inference
Gov 2002: 3. Randomization Inference Matthew Blackwell September 10, 2015 Where are we? Where are we going? Last week: This week: What can we identify using randomization? Estimators were justified via
Entering and recoding variables
Entering and recoding variables To enter: You create a New data file Define the variables on Variable View Enter the values on Data View To create the dichotomies: Transform -> Recode into Different Variable
Chapte The McGraw-Hill Companies, Inc. All rights reserved.
er15 Chapte Chi-Square Tests d Chi-Square Tests for -Fit Uniform Goodness- Poisson Goodness- Goodness- ECDF Tests (Optional) Contingency Tables A contingency table is a cross-tabulation of n paired observations
ECLT 5810 Data Preprocessing. Prof. Wai Lam
ECLT 5810 Data Preprocessing Prof. Wai Lam Why Data Preprocessing? Data in the real world is imperfect incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate
TOPIC: Descriptive Statistics Single Variable
TOPIC: Descriptive Statistics Single Variable I. Numerical data summary measurements A. Measures of Location. Measures of central tendency Mean; Median; Mode. Quantiles - measures of noncentral tendency
Chart types and when to use them
APPENDIX A Chart types and when to use them Pie chart Figure illustration of pie chart 2.3 % 4.5 % Browser Usage for April 2012 18.3 % 38.3 % Internet Explorer Firefox Chrome Safari Opera 35.8 % Pie chart
Describing Distributions with Numbers
Describing Distributions with Numbers Using graphs, we could determine the center, spread, and shape of the distribution of a quantitative variable. We can also use numbers (called summary statistics)
Curriculum Summary 8 th Grade Algebra I
Curriculum Summary 8 th Grade Algebra I Students should know and be able to demonstrate mastery in the following skills by the end of Eighth Grade: The Number System Extend the properties of exponents
The Model Building Process Part I: Checking Model Assumptions Best Practice (Version 1.1)
The Model Building Process Part I: Checking Model Assumptions Best Practice (Version 1.1) Authored by: Sarah Burke, PhD Version 1: 31 July 2017 Version 1.1: 24 October 2017 The goal of the STAT T&E COE
Chapter 3: Displaying and summarizing quantitative data p52 The pattern of variation of a variable is called its distribution.
Chapter 3: Displaying and summarizing quantitative data p52 The pattern of variation of a variable is called its distribution. 1 Histograms p53 Spoiled ballots are a real threat to democracy. Below are
SPATIAL VOTING (MULTIPLE DIMENSIONS)
SPATIAL VOTING (MULTIPLE DIMENSIONS) 1 Assumptions Alternatives are points in an n-dimensional space. Examples for 2D: Social Issues and Economic Issues Domestic Spending and Foreign Spending Single-peaked
ntopic Organic Traffic Study
ntopic Organic Traffic Study 1 Abstract The objective of this study is to determine whether content optimization solely driven by ntopic recommendations impacts organic search traffic from Google. The
CS 147: Computer Systems Performance Analysis
CS 147: Computer Systems Performance Analysis Summarizing Variability and Determining Distributions CS 147: Computer Systems Performance Analysis Summarizing Variability and Determining Distributions 1
Solutions exercises of Chapter 7
Solutions exercises of Chapter 7 Exercise 1 a. These are paired samples: each pair of half plates will have about the same level of corrosion, so the result of polishing by the two brands of polish are
MIDTERM EXAMINATION (Spring 2011) STA301- Statistics and Probability
STA301- Statistics and Probability Solved MCQS From Midterm Papers March 19,2012 MC100401285 Moaaz.pk@gmail.com Mc100401285@gmail.com PSMD01 MIDTERM EXAMINATION (Spring 2011) STA301- Statistics and Probability
Meelis Kull Autumn Meelis Kull - Autumn MTAT Data Mining - Lecture 03
Meelis Kull meelis.kull@ut.ee Autumn 2017 1 Demo: Data science mini-project CRISP-DM: cross-industrial standard process for data mining Data understanding: Types of data Data understanding: First look
Area Formulas. Linear
Math Vocabulary and Formulas Approximate Area Arithmetic Sequences Average Rate of Change Axis of Symmetry Base Behavior of the Graph Bell Curve Bi-annually(with Compound Interest) Binomials Boundary Lines
CIVL 7012/8012. Collection and Analysis of Information
CIVL 7012/8012 Collection and Analysis of Information Uncertainty in Engineering Statistics deals with the collection and analysis of data to solve real-world problems. Uncertainty is inherent in all real
Advanced Quantitative Research Methodology Lecture Notes: January Ecological 28, 2012 Inference1 / 38
Advanced Quantitative Research Methodology Lecture Notes: Ecological Inference 1 Gary King http://gking.harvard.edu January 28, 2012 1 c Copyright 2008 Gary King, All Rights Reserved. Gary King http://gking.harvard.edu
Supporting Information. Controlling the Airwaves: Incumbency Advantage and. Community Radio in Brazil
Supporting Information Controlling the Airwaves: Incumbency Advantage and Community Radio in Brazil Taylor C. Boas F. Daniel Hidalgo May 7, 2011 1 1 Descriptive Statistics Descriptive statistics for the
Topic 3: Introduction to Statistics. Algebra 1. Collecting Data. Table of Contents. Categorical or Quantitative? What is the Study of Statistics?!
Topic 3: Introduction to Statistics Collecting Data We collect data through observation, surveys and experiments. We can collect two different types of data: Categorical Quantitative Algebra 1 Table of
Chapter 3 Examining Data
Chapter 3 Examining Data This chapter discusses methods of displaying quantitative data with the objective of understanding the distribution of the data. Example During childhood and adolescence, bone
Math 140 Introductory Statistics
Math 140 Introductory Statistics Professor Silvia Fernández Chapter 2 Based on the book Statistics in Action by A. Watkins, R. Scheaffer, and G. Cobb. Visualizing Distributions Recall the definition: The
Math 140 Introductory Statistics
Visualizing Distributions Math 140 Introductory Statistics Professor Silvia Fernández Chapter Based on the book Statistics in Action by A. Watkins, R. Scheaffer, and G. Cobb. Recall the definition: The
Created by T. Madas. Candidates may use any calculator allowed by the regulations of this examination.
IYGB GCE Mathematics MMS Advanced Level Practice Paper Q Difficulty Rating: 3.400/0.6993 Time: 3 hours Candidates may use any calculator allowed by the regulations of this examination. Information for
1 Measures of the Center of a Distribution
1 Measures of the Center of a Distribution Qualitative descriptions of the shape of a distribution are important and useful. But we will often desire the precision of numerical summaries as well. Two aspects
Mathematics Standards for High School Financial Algebra A and Financial Algebra B
Mathematics Standards for High School Financial Algebra A and Financial Algebra B Financial Algebra A and B are two semester courses that may be taken in either order or one taken without the other; both
Analysis of 2x2 Cross-Over Designs using T-Tests
Chapter 234 Analysis of 2x2 Cross-Over Designs using T-Tests Introduction This procedure analyzes data from a two-treatment, two-period (2x2) cross-over design. The response is assumed to be a continuous
Final Exam STAT On a Pareto chart, the frequency should be represented on the A) X-axis B) regression C) Y-axis D) none of the above
King Abdul Aziz University Faculty of Sciences Statistics Department Final Exam STAT 0 First Term 49-430 A 40 Name No ID: Section: You have 40 questions in 9 pages. You have 90 minutes to solve the exam.
Stat 427/527: Advanced Data Analysis I
Stat 427/527: Advanced Data Analysis I Review of Chapters 1-4 Sep, 2017 1 / 18 Concepts you need to know/interpret Numerical summaries: measures of center (mean, median, mode) measures of spread (sample
STAT 200 Chapter 1 Looking at Data - Distributions
STAT 200 Chapter 1 Looking at Data - Distributions What is Statistics? Statistics is a science that involves the design of studies, data collection, summarizing and analyzing the data, interpreting the
Chapter 1:Descriptive statistics
Slide 1.1 Chapter 1:Descriptive statistics Descriptive statistics summarises a mass of information. We may use graphical and/or numerical methods Examples of the former are the bar chart and XY chart,
Bivariate statistics: correlation
Research Methods for Political Science Bivariate statistics: correlation Dr. Thomas Chadefaux Assistant Professor in Political Science Thomas.chadefaux@tcd.ie 1 Bivariate relationships: interval-ratio
Time: 1 hour 30 minutes
Paper Reference(s) 6684/0 Edexcel GCE Statistics S Silver Level S Time: hour 30 minutes Materials required for examination papers Mathematical Formulae (Green) Items included with question Nil Candidates
Further Mathematics 2018 CORE: Data analysis Chapter 2 Summarising numerical data
Chapter 2: Summarising numerical data Further Mathematics 2018 CORE: Data analysis Chapter 2 Summarising numerical data Extract from Study Design Key knowledge Types of data: categorical (nominal and ordinal)
Chapter 3. Measuring data
Chapter 3 Measuring data 1 Measuring data versus presenting data We present data to help us draw meaning from it But pictures of data are subjective They re also not susceptible to rigorous inference Measuring
PS 203 Spring 2002 Homework One - Answer Key
PS 203 Spring 2002 Homework One - Answer Key 1. If you have a home or office computer, download and install WinBUGS. If you don t have your own computer, try running WinBUGS in the Department lab. 2. The
Averages How difficult is QM1? What is the average mark? Week 1b, Lecture 2
Averages How difficult is QM1? What is the average mark? Week 1b, Lecture 2 Topics: 1. Mean 2. Mode 3. Median 4. Order Statistics 5. Minimum, Maximum, Range 6. Percentiles, Quartiles, Interquartile Range
Handout 1: Predicting GPA from SAT
Handout 1: Predicting GPA from SAT appsrv01.srv.cquest.utoronto.ca> appsrv01.srv.cquest.utoronto.ca> ls Desktop grades.data grades.sas oldstuff sasuser.800 appsrv01.srv.cquest.utoronto.ca> cat grades.data
Lecture 06. DSUR CH 05 Exploring Assumptions of parametric statistics Hypothesis Testing Power
Lecture 06 DSUR CH 05 Exploring Assumptions of parametric statistics Hypothesis Testing Power Introduction Assumptions When broken then we are not able to make inference or accurate descriptions about
How do you write and evaluate algebraic expressions? How can algebraic expressions and equations represent actual situations?
Topic: Expressions, Equations and Functions Days: 13 Key Learning: Expressions, equations, tables and graphs are used to model realworld situations. Unit Essential Question(s): How do you write and evaluate
ADMS2320.com. We Make Stats Easy. Chapter 4. ADMS2320.com Tutorials Past Tests. Tutorial Length 1 Hour 45 Minutes
We Make Stats Easy. Chapter 4 Tutorial Length 1 Hour 45 Minutes Tutorials Past Tests Chapter 4 Page 1 Chapter 4 Note The following topics will be covered in this chapter: Measures of central location Measures
Common Core State Standards with California Additions 1 Standards Map. Algebra I
Common Core State s with California Additions 1 s Map Algebra I *Indicates a modeling standard linking mathematics to everyday life, work, and decision-making N-RN 1. N-RN 2. Publisher Language 2 Primary
STATISTICS 479 Exam II (100 points)
Name STATISTICS 79 Exam II (1 points) 1. A SAS data set was created using the following input statement: Answer parts(a) to (e) below. input State $ City $ Pop199 Income Housing Electric; (a) () Give the
SIMPLE TWO VARIABLE REGRESSION
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 SIMPLE TWO VARIABLE REGRESSION I. AGENDA: A. Causal inference and non-experimental research B. Least squares principle C. Regression
P8130: Biostatistical Methods I
P8130: Biostatistical Methods I Lecture 2: Descriptive Statistics Cody Chiuzan, PhD Department of Biostatistics Mailman School of Public Health (MSPH) Lecture 1: Recap Intro to Biostatistics Types of Data
Contents. 13. Graphs of Trigonometric Functions 2 Example Example
Contents 13. Graphs of Trigonometric Functions 2 Example 13.19............................... 2 Example 13.22............................... 5 1 Peterson, Technical Mathematics, 3rd edition 2 Example 13.19
MATH 117 Statistical Methods for Management I Chapter Three
Jubail University College MATH 117 Statistical Methods for Management I Chapter Three This chapter covers the following topics: I. Measures of Center Tendency. 1. Mean for Ungrouped Data (Raw Data) 2.