DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.


 Kathleen Boyd
 1 years ago
 Views:
Transcription
1 DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
2 DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize, summarize & describe the data Inferential To determine reliability of the data
3 RELATIONSHIPS SCALES OF MEASURMENT Nominal Scale Only use those statistical procedures that rely on counting  the number (N) in the sample. Ordinal Scale Same as nominal scale Can use statistics that indicate points below which certain percentages of the cases fall.
4 RELATIONSHIPS SCALES OF Interval Scale MEASURMENT Any of the above plus procedures that include adding. Ratio Scale Any statistical procedure is acceptable.
5 MEASUREMENT SUMMARY Measurement Characteristics Scoring Types Examples Nominal Lowest level  used to classify variables into two or more categories. Cases placed in the same category must be equivalent. The categories must be exhaustive  all persons or items must fit into one of the categories. Counting N in sample Labels or # s No relation between # s N of sample Mode Range Football player jerseys 48 not better than 36 Race Gender Must also be mutually exclusive  one person or item can't fit more than one category.
6 MEASUREMENT SUMMARY Measurement Characteristics Scoring Types Examples Ordinal Numbers only used to indicate the rank order of cases of a variable. Cannot measure or evaluate the difference in value between each case. No mathematical or statistical operations (you can't add label 1 to label 2, etc.). Points below which certain % falls. Size of distance between intervals unknown. Order of objects with respect to an attribute. Frequency distribution Median Quartile deviation Spearman rho coefficient of correlation Hardness of metal Personnel evaluations of performance
7 MEASUREMENT SUMMARY Measurement Characteristics Scoring Types Examples Interval Has all of the above characteristics. Added requirement of equal distances or intervals between labels  represent equal distances in the variables of your study. = intervals w/ arbitrary origin No true zero Adding Mean Standard deviation Variance Pearson product moment coefficient of correlation Temperature difference Footcandle levels in lighting IQ s
8 MEASUREMENT SUMMARY Measurement Characteristics Scoring Types Examples Ratio Has all of above features plus an absolute zero point. Enables you to multiple and divide scale numbers to create ratios between labels. Equal intervals Multiply Divide All types Income ranges. Number of years of school. Age in years. Yardstick or architect s scale.
9 FREQUENCY DISTRIBUTIONS The arrangement of the scores from lowest to highest. Implies a general shape to the data because of the shape of the distribution.
10 FREQUENCY DISTRIBUTIONS The easiest way for you to do summary statistics is with a dedicated statistical package. With small data sets, you can do most data manipulation for summary statistics with a spreadsheet.
11 HISTOGRAMS & POLYGONS: GENERAL RULES On horizontal axis, lay out lowest scores to highest  left to right. Lay out frequencies on vertical axis  from 0 up to highest frequency.
12 HISTOGRAMS & POLYGONS: GENERAL RULES Place a point at center of score/frequency intersection. Construct either a histogram or polygon.
13 HISTOGRAMS & POLYGONS: Histogram or polygon. GENERAL RULES
14 MEASURES OF CENTRAL TENDANCY Used to summarize data through a single number that can represent the whole set of scores. Types: mode, median, mode, mean
15 MEASURES OF CENTRAL TENDANCY Mode The value or number that occurs most frequently in the distribution. Two modes are bimodal; three or more are trimodel or multimodal. Very stable and there can be more than one mode. Only appropriate measure for nominal scales.
16 MEASURES OF CENTRAL Median TENDANCY The point in the distribution below which 50% of the scores lie. Scores must be placed in rank order from lowest to highest first. The median can fall between the upper limit and lower limit of a score. Can fall on the border line between scores.
17 MEASURES OF CENTRAL TENDANCY Median (continued) The median is an ordinal statistic because it is based on rank. Can be used on interval and ratio data but the interval characteristic of the data is not used. Only time the median is really useful is when there are extreme scores in the distribution.
18 MEASURES OF CENTRAL Mean TENDANCY The arithmetic average  sum of all the scores divided by the N. Most stable measure of central tendency and is more precise than the median or mode. Can be used with interval and ratio scales.
19 MEASURES OF CENTRAL TENDANCY Mean (continued) Can calculate the Mean for a distribution of scores or for a frequency distribution. Best indicator of combined performance whereas the median is the best indicator of typical performance.
20 DISTRIBUTION SHAPES  The mean and median are the same. If a single mode, it falls at the same location as the mean and median. SYMMETRICAL
21 DISTRIBUTION SHAPES  When distributions are skewed the values of central tendency differ. Determine skewness by comparing the mean & median without drawing a histogram or polygon. SKEWED
22 DISTRIBUTION SHAPES  POSITIVE SKEW The mean is always greater than the median & the median is usually greater than the mode. Skew is to the left.
23 DISTRIBUTION SHAPES  NEGATIVE SKEW The mean is always smaller than the median & the median is usually smaller than the mode. Skew is to the right.
24 DISTRIBUTION SHAPES  NORMAL CURVE A symmetrical curve with the same number of scores above & below the mean. Same as symmetrical. Most scores are concentrated around the mean. Approximately 68% of the cases are within +/ 1 SD unit from the mean.
25 VARIABILITY MEASURES Range Difference between the highest and lowest scores. Determine by subtraction. Is an unreliable index of variability because it is derived from only two scores.
26 VARIABILITY MEASURES Quartile deviation Half the difference between the upper and lower quartiles in a distribution. The 75th percentile & the 25th percentile. Provides a measure of onehalf of the range of scores within which lie the middle 50% of the scores. It is an ordinal scale statistic and is used with the median (which means that it is not often used unless there are extreme scores).
27 VARIABILITY MEASURES Variance Based on the mean. Considers the size and location of individual scores. Variance & standard deviation are based on the deviation score which is the difference between a raw score & the mean. The sum of the deviation scores of a distribution are always zero because the scores above the mean are always positive while the scores below the mean are always negative.
28 VARIABILITY MEASURES Standard Deviation SD is the square root of variance Is used to summarize data in the same units as the original data. Most commonly used statistic for variability. It is the square root of the mean of the squared deviation scores.
29 STANDARD SCORES zscores The distance of a score from the mean in standard deviation units. Scores with the same numerical value as the mean will have a zscore of zero. Used to compare one set of scores to another  example two exams and S's performance on the exams. Use of zscores requires use of negative values and fractions. Overcome by using Zscores.
30 Zscores STANDARD SCORES Obtained by multiplying the zscore by 10 and adding 50 to the result. Used to compare scores in different distributions. Allows descriptions in whole numbers. A type of standard score. Does not alter the shape of the original distribution.
31 CORRELATION Used to describe the relationship between pairs of scores. Shows the extent to which a change in one variable is associated with change in another variable.
32 Scattergrams CORRELATION Used to show correlation. One variable on each axis (horizontal and vertical). Plot scattergrams to see both direction & strength of a relationship. Direction shows positive or negative relationship. Scores for independent variable on horizontal axis & dependent variable on vertical axis.
33 Lower left to upper right Positive relationship Low scores on one variable associated with low scores on other High on one high on other. CORRELATION
34 CORRELATION Upper left to lower right Negative relationship. High on one, low on the other variable.
35 CORRELATION Narrow dot band High strength. Straight line shows strong relationship between variables.
36 CORRELATION Scattered dot band Low strength. Relatively weak relationship between variables.
37 CORRELATION Prediction of one variable from another can occur with strong relationships Positive and negative equally important. The higher the correlation between variables in either a positive or negative direction, the more accurate the prediction.
38 CORRELATION COEFFICIENTS Range from to = perfect negative relationship = perfect positive relationship (midpoint) = no relationship at all.
39 CORRELATION COEFFICIENTS Correlation coefficients near unity indicate high degree of relationship. Make accurate prediction about one variable from info about another variable. Desirable to have +/ and above. Again, negative & positive both equally good for prediction.
40 PEARSONS R (PRODUCT MOMENT CORRELATION) Used with either interval or ratio scales. Defined as the mean of zscore products of two variables. Most common method for correlation. Same statistical family as mean.
41 PEARSONS R (PRODUCT MOMENT CORRELATION) Assumes a linear relationship between the two variables. (Straight line fit between scores of the two variables). If curvilinear, must use other methods.
42 SPEARMAN RHO Used with rank order data; ordinal scales. Part of the same statistical family as median. Ranges from to (same as Pearsons R).
43 SOURCES OF INFO See your bibliography for the class!
Chapter 3: Central Tendency
Chapter 3: Central Tendency Central Tendency In general terms, central tendency is a statistical measure that determines a single value that accurately describes the center of the distribution and represents
More informationChapter 15 Multiple Choice Questions (The answers are provided after the last question.)
Chapter 15 Multiple Choice Questions (The answers are provided after the last question.) 1. What is the median of the following set of scores? 18, 6, 12, 10, 14? a. 10 b. 14 c. 18 d. 12 2. Approximately
More informationHomework 3. Part 1. Name: Score: / null
Name: Score: / Homework 3 Part 1 null 1 For the following sample of scores, the standard deviation is. Scores: 7, 2, 4, 6, 4, 7, 3, 7 Answer Key: 2 2 For any set of data, the sum of the deviation scores
More informationResearch Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement
Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.
More informationSession 1.6 Measures of Central Tendency
Session 1.6 Measures of Central Tendency Measures of location (Indices of central tendency) These indices locate the center of the frequency distribution curve. The mode, median, and mean are three indices
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationFrequency Distributions
Displaying Data Frequency Distributions After collecting data, the first task for a researcher is to organize and summarize the data to get a general overview of the results. Remember, this is the goal
More informationDescribe what is meant by a placebo Contrast the doubleblind procedure with the singleblind procedure Review the structure for organizing a memo
Readings: Ha and Ha Textbook  Chapters 1 8 Appendix D & E (online) Plous  Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability
More informationLesson 4 Part 1. Relationships between. two numerical variables. Correlation Coefficient. Relationship between two
Lesson Part Relationships between two numerical variables Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear between two numerical variables Relationship
More information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x  x) B. x 3 x C. 3x  x D. x  3x 2) Write the following as an algebraic expression
More informationContent DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS
Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction
More informationDescriptive Statistics and Measurement Scales
Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample
More informationMeans, standard deviations and. and standard errors
CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard
More informationStatistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
More informationData Analysis: Describing Data  Descriptive Statistics
WHAT IT IS Return to Table of ontents Descriptive statistics include the numbers, tables, charts, and graphs used to describe, organize, summarize, and present raw data. Descriptive statistics are most
More informationChapter 3 Descriptive Statistics: Numerical Measures. Learning objectives
Chapter 3 Descriptive Statistics: Numerical Measures Slide 1 Learning objectives 1. Single variable Part I (Basic) 1.1. How to calculate and use the measures of location 1.. How to calculate and use the
More informationMCQ S OF MEASURES OF CENTRAL TENDENCY
MCQ S OF MEASURES OF CENTRAL TENDENCY MCQ No 3.1 Any measure indicating the centre of a set of data, arranged in an increasing or decreasing order of magnitude, is called a measure of: (a) Skewness (b)
More informationF. Farrokhyar, MPhil, PhD, PDoc
Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How
More informationModule 3: Correlation and Covariance
Using Statistical Data to Make Decisions Module 3: Correlation and Covariance Tom Ilvento Dr. Mugdim Pašiƒ University of Delaware Sarajevo Graduate School of Business O ften our interest in data analysis
More informationLEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016
UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe
More informationThe correlation coefficient
The correlation coefficient Clinical Biostatistics The correlation coefficient Martin Bland Correlation coefficients are used to measure the of the relationship or association between two quantitative
More information( ) ( ) Central Tendency. Central Tendency
1 Central Tendency CENTRAL TENDENCY: A statistical measure that identifies a single score that is most typical or representative of the entire group Usually, a value that reflects the middle of the distribution
More informationSTATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI
STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members
More informationInferential Statistics
Inferential Statistics Sampling and the normal distribution Zscores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are
More informationnot to be republished NCERT Measures of Central Tendency
You have learnt in previous chapter that organising and presenting data makes them comprehensible. It facilitates data processing. A number of statistical techniques are used to analyse the data. In this
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationCorrelation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2
Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables
More informationCorrelation key concepts:
CORRELATION Correlation key concepts: Types of correlation Methods of studying correlation a) Scatter diagram b) Karl pearson s coefficient of correlation c) Spearman s Rank correlation coefficient d)
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationGCSE HIGHER Statistics Key Facts
GCSE HIGHER Statistics Key Facts Collecting Data When writing questions for questionnaires, always ensure that: 1. the question is worded so that it will allow the recipient to give you the information
More informationReport of for Chapter 2 pretest
Report of for Chapter 2 pretest Exam: Chapter 2 pretest Category: Organizing and Graphing Data 1. "For our study of driving habits, we recorded the speed of every fifth vehicle on Drury Lane. Nearly every
More informationModule 2 Project Maths Development Team Draft (Version 2)
5 Week Modular Course in Statistics & Probability Strand 1 Module 2 Analysing Data Numerically Measures of Central Tendency Mean Median Mode Measures of Spread Range Standard Deviation InterQuartile Range
More informationA frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes
A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes together with the number of data values from the set that
More informationSTATISTICS FOR PSYCH MATH REVIEW GUIDE
STATISTICS FOR PSYCH MATH REVIEW GUIDE ORDER OF OPERATIONS Although remembering the order of operations as BEDMAS may seem simple, it is definitely worth reviewing in a new context such as statistics formulae.
More information11/20/2014. Correlational research is used to describe the relationship between two or more naturally occurring variables.
Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection
More informationDescriptive Statistics
Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web
More informationDr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101
Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam Software Profiling Seminar, 2013 Statistics 101 Descriptive Statistics Population Object Object Object Sample numerical description Object
More informationDescriptive Statistics. Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion
Descriptive Statistics Purpose of descriptive statistics Frequency distributions Measures of central tendency Measures of dispersion Statistics as a Tool for LIS Research Importance of statistics in research
More informationQuantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality
Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality 1 To help choose which type of quantitative data analysis to use either before
More informationWe will use the following data sets to illustrate measures of center. DATA SET 1 The following are test scores from a class of 20 students:
MODE The mode of the sample is the value of the variable having the greatest frequency. Example: Obtain the mode for Data Set 1 77 For a grouped frequency distribution, the modal class is the class having
More informationVariables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test.
The Analysis of Research Data The design of any project will determine what sort of statistical tests you should perform on your data and how successful the data analysis will be. For example if you decide
More informationMEASURES OF VARIATION
NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are
More informationDescriptive Statistics. Frequency Distributions and Their Graphs 2.1. Frequency Distributions. Chapter 2
Chapter Descriptive Statistics.1 Frequency Distributions and Their Graphs Frequency Distributions A frequency distribution is a table that shows classes or intervals of data with a count of the number
More informationCentral Tendency. n Measures of Central Tendency: n Mean. n Median. n Mode
Central Tendency Central Tendency n A single summary score that best describes the central location of an entire distribution of scores. n Measures of Central Tendency: n Mean n The sum of all scores divided
More informationGCSE Statistics Revision notes
GCSE Statistics Revision notes Collecting data Sample This is when data is collected from part of the population. There are different methods for sampling Random sampling, Stratified sampling, Systematic
More informationCHAPTER 3 COMMONLY USED STATISTICAL TERMS
CHAPTER 3 COMMONLY USED STATISTICAL TERMS There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of
More informationNumerical Measures of Central Tendency
Numerical Measures of Central Tendency Often, it is useful to have special numbers which summarize characteristics of a data set These numbers are called descriptive statistics or summary statistics. A
More informationChapter 3: Data Description Numerical Methods
Chapter 3: Data Description Numerical Methods Learning Objectives Upon successful completion of Chapter 3, you will be able to: Summarize data using measures of central tendency, such as the mean, median,
More informationIntroduction; Descriptive & Univariate Statistics
Introduction; Descriptive & Univariate Statistics I. KEY COCEPTS A. Population. Definitions:. The entire set of members in a group. EXAMPLES: All U.S. citizens; all otre Dame Students. 2. All values of
More informationFREQUENCY AND PERCENTILES
FREQUENCY DISTRIBUTIONS AND PERCENTILES New Statistical Notation Frequency (f): the number of times a score occurs N: sample size Simple Frequency Distributions Raw Scores The scores that we have directly
More informationUnivariate Descriptive Statistics
Univariate Descriptive Statistics Displays: pie charts, bar graphs, box plots, histograms, density estimates, dot plots, stemleaf plots, tables, lists. Example: sea urchin sizes Boxplot Histogram Urchin
More informationNumerical Summarization of Data OPRE 6301
Numerical Summarization of Data OPRE 6301 Motivation... In the previous session, we used graphical techniques to describe data. For example: While this histogram provides useful insight, other interesting
More informationExercise 1.12 (Pg. 2223)
Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.
More informationFrequency distributions, central tendency & variability. Displaying data
Frequency distributions, central tendency & variability Displaying data Software SPSS Excel/Numbers/Google sheets Social Science Statistics website (socscistatistics.com) Creating and SPSS file Open the
More informationApplied Statistics Handbook
Applied Statistics Handbook Phil Crewson Version 1. Applied Statistics Handbook Copyright 006, AcaStat Software. All rights Reserved. http://www.acastat.com Protected under U.S. Copyright and international
More informationCOMPARISON MEASURES OF CENTRAL TENDENCY & VARIABILITY EXERCISE 8/5/2013. MEASURE OF CENTRAL TENDENCY: MODE (Mo) MEASURE OF CENTRAL TENDENCY: MODE (Mo)
COMPARISON MEASURES OF CENTRAL TENDENCY & VARIABILITY Prepared by: Jess Roel Q. Pesole CENTRAL TENDENCY what is average or typical in a distribution Commonly Measures: 1. Mode. Median 3. Mean quantified
More informationMIDTERM EXAMINATION Spring 2009 STA301 Statistics and Probability (Session  2)
MIDTERM EXAMINATION Spring 2009 STA301 Statistics and Probability (Session  2) Question No: 1 Median can be found only when: Data is Discrete Data is Attributed Data is continuous Data is continuous
More informationHypothesis Testing  Relationships
 Relationships Session 3 AHX43 (28) 1 Lecture Outline Correlational Research. The Correlation Coefficient. An example. Considerations. One and Twotailed Tests. Errors. Power. for Relationships AHX43
More informationWhy do we measure central tendency? Basic Concepts in Statistical Analysis
Why do we measure central tendency? Basic Concepts in Statistical Analysis Chapter 4 Too many numbers Simplification of data Descriptive purposes What is central tendency? Measure of central tendency A
More informationDESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS
DESCRIPTIVE STATISTICS AND EXPLORATORY DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi  110 012 seema@iasri.res.in 1. Descriptive Statistics Statistics
More informationSampling, frequency distribution, graphs, measures of central tendency, measures of dispersion
Statistics Basics Sampling, frequency distribution, graphs, measures of central tendency, measures of dispersion Part 1: Sampling, Frequency Distributions, and Graphs The method of collecting, organizing,
More informationResearch Methods 1 Handouts, Graham Hole,COGS  version 1.0, September 2000: Page 1:
Research Methods 1 Handouts, Graham Hole,COGS  version 1.0, September 000: Page 1: DESCRIPTIVE STATISTICS  FREQUENCY DISTRIBUTIONS AND AVERAGES: Inferential and Descriptive Statistics: There are four
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More information9 Descriptive and Multivariate Statistics
9 Descriptive and Multivariate Statistics Jamie Price Donald W. Chamberlayne * S tatistics is the science of collecting and organizing data and then drawing conclusions based on data. There are essentially
More informationDescribing Data. Carolyn J. Anderson EdPsych 580 Fall Describing Data p. 1/42
Describing Data Carolyn J. Anderson EdPsych 580 Fall 2005 Describing Data p. 1/42 Describing Data Numerical Descriptions Single Variable Relationship Graphical displays Single variable. Relationships in
More informationThe right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median
CONDENSED LESSON 2.1 Box Plots In this lesson you will create and interpret box plots for sets of data use the interquartile range (IQR) to identify potential outliers and graph them on a modified box
More informationAlgebra I. Copyright 2014 Fuel Education LLC. All rights reserved.
Algebra I COURSE DESCRIPTION: The purpose of this course is to allow the student to gain mastery in working with and evaluating mathematical expressions, equations, graphs, and other topics, with an emphasis
More informationIntroduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
More informationIntroduction to Statistics for Psychology. Quantitative Methods for Human Sciences
Introduction to Statistics for Psychology and Quantitative Methods for Human Sciences Jonathan Marchini Course Information There is website devoted to the course at http://www.stats.ox.ac.uk/ marchini/phs.html
More informationSpearman s correlation
Spearman s correlation Introduction Before learning about Spearman s correllation it is important to understand Pearson s correlation which is a statistical measure of the strength of a linear relationship
More informationNominal Scaling. Measures of Central Tendency, Spread, and Shape. Interval Scaling. Ordinal Scaling
Nominal Scaling Measures of, Spread, and Shape Dr. J. Kyle Roberts Southern Methodist University Simmons School of Education and Human Development Department of Teaching and Learning The lowest level of
More informationChapter 5: The normal approximation for data
Chapter 5: The normal approximation for data Context................................................................... 2 Normal curve 3 Normal curve.............................................................
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measures of Dispersion While measures of central tendency indicate what value of a variable is (in one sense or other) average or central or typical in a set of data, measures of
More informationData. ECON 251 Research Methods. 1. Data and Descriptive Statistics (Review) CrossSectional and TimeSeries Data. Population vs.
ECO 51 Research Methods 1. Data and Descriptive Statistics (Review) Data A variable  a characteristic of population or sample that is of interest for us. Data  the actual values of variables Quantitative
More informationThe Normal Distribution
The Normal Distribution Cal State Northridge Ψ320 Andrew Ainsworth PhD The standard deviation Benefits: Uses measure of central tendency (i.e. mean) Uses all of the data points Has a special relationship
More informationFoundation of Quantitative Data Analysis
Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10  October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1
More informationCorrelational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
More informationALGEBRA I A PLUS COURSE OUTLINE
ALGEBRA I A PLUS COURSE OUTLINE OVERVIEW: 1. Operations with Real Numbers 2. Equation Solving 3. Word Problems 4. Inequalities 5. Graphs of Functions 6. Linear Functions 7. Scatterplots and Lines of Best
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More information13.2 Measures of Central Tendency
13.2 Measures of Central Tendency Measures of Central Tendency For a given set of numbers, it may be desirable to have a single number to serve as a kind of representative value around which all the numbers
More informationDescriptive Statistics. Understanding Data: Categorical Variables. Descriptive Statistics. Dataset: Shellfish Contamination
Descriptive Statistics Understanding Data: Dataset: Shellfish Contamination Location Year Species Species2 Method Metals Cadmium (mg kg  ) Chromium (mg kg  ) Copper (mg kg  ) Lead (mg kg  ) Mercury
More informationStatistics GCSE Higher Revision Sheet
Statistics GCSE Higher Revision Sheet This document attempts to sum up the contents of the Higher Tier Statistics GCSE. There is one exam, two hours long. A calculator is allowed. It is worth 75% of the
More informationThe Big 50 Revision Guidelines for S1
The Big 50 Revision Guidelines for S1 If you can understand all of these you ll do very well 1. Know what is meant by a statistical model and the Modelling cycle of continuous refinement 2. Understand
More information4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: "What do the data look like?"
Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest, the actual distribution of the variables, and the number of cases. Different analyses
More informationSTAT 155 Introductory Statistics. Lecture 5: Density Curves and Normal Distributions (I)
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STAT 155 Introductory Statistics Lecture 5: Density Curves and Normal Distributions (I) 9/12/06 Lecture 5 1 A problem about Standard Deviation A variable
More informationMeasurement & Data Analysis. On the importance of math & measurement. Steps Involved in Doing Scientific Research. Measurement
Measurement & Data Analysis Overview of Measurement. Variability & Measurement Error.. Descriptive vs. Inferential Statistics. Descriptive Statistics. Distributions. Standardized Scores. Graphing Data.
More informationALGEBRA 1/ALGEBRA 1 HONORS
ALGEBRA 1/ALGEBRA 1 HONORS CREDIT HOURS: 1.0 COURSE LENGTH: 2 Semesters COURSE DESCRIPTION The purpose of this course is to allow the student to gain mastery in working with and evaluating mathematical
More information32 Measures of Central Tendency and Dispersion
32 Measures of Central Tendency and Dispersion In this section we discuss two important aspects of data which are its center and its spread. The mean, median, and the mode are measures of central tendency
More informationDescriptive statistics parameters: Measures of centrality
Descriptive statistics parameters: Measures of centrality Contents Definitions... 3 Classification of descriptive statistics parameters... 4 More about central tendency estimators... 5 Relationship between
More informationChapter 7 What to do when you have the data
Chapter 7 What to do when you have the data We saw in the previous chapters how to collect data. We will spend the rest of this course looking at how to analyse the data that we have collected. Stem and
More informationStatistical Foundations: Measures of Location and Central Tendency and Summation and Expectation
Statistical Foundations: and Central Tendency and and Lecture 4 September 5, 2006 Psychology 790 Lecture #49/05/2006 Slide 1 of 26 Today s Lecture Today s Lecture Where this Fits central tendency/location
More informationChapter 2 Statistical Foundations: Descriptive Statistics
Chapter 2 Statistical Foundations: Descriptive Statistics 20 Chapter 2 Statistical Foundations: Descriptive Statistics Presented in this chapter is a discussion of the types of data and the use of frequency
More informationDomain Essential Question Common Core Standards Resources
Middle School Math 20162017 Domain Essential Question Common Core Standards First Ratios and Proportional Relationships How can you use mathematics to describe change and model real world solutions? How
More informationCHAPTER 3 CENTRAL TENDENCY ANALYSES
CHAPTER 3 CENTRAL TENDENCY ANALYSES The next concept in the sequential statistical steps approach is calculating measures of central tendency. Measures of central tendency represent some of the most simple
More informationChapter 3 Central Tendency
Chapter 3 Central Tendency PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Seventh Edition by Frederick J Gravetter and Larry B. Wallnau Learning Outcomes 1 2 3 4 5 6 Understand
More informationBiostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY
Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to
More informationData Mining Part 2. Data Understanding and Preparation 2.1 Data Understanding Spring 2010
Data Mining Part 2. and Preparation 2.1 Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Outline Introduction Measuring the Central Tendency Measuring the Dispersion of Data Graphic Displays References
More informationChapter 14: Analyzing Relationships Between Variables
Chapter Outlines for: Frey, L., Botan, C., & Kreps, G. (1999). Investigating communication: An introduction to research methods. (2nd ed.) Boston: Allyn & Bacon. Chapter 14: Analyzing Relationships Between
More information103 Measures of Central Tendency and Variation
103 Measures of Central Tendency and Variation So far, we have discussed some graphical methods of data description. Now, we will investigate how statements of central tendency and variation can be used.
More informationUsing Excel for inferential statistics
FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied
More information