The document provides information on bivariate analysis and cross-tabulation. It discusses how cross-tabulation allows examination of relationships between two variables and calculation of percentages to compare groups. Chi-square is introduced as a test of hypotheses about relationships between nominal or ordinal variables, requiring calculation of expected frequencies. Examples are provided to demonstrate cross-tabulation tables and chi-square calculations.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Application of Univariate, Bi-variate and Multivariate analysis Pooja k shettySundar B N
This document discusses different types of statistical analysis used to analyze data. Univariate analysis examines one variable at a time through methods like frequency distributions, histograms, and pie charts. Bivariate analysis considers the relationship between two variables, such as income and weight. Multivariate analysis studies three or more variables simultaneously, with applications in fields like social science, climatology, and medicine.
This document provides an overview of univariate analysis. It defines key terms like variables, scales of measurement, and types of univariate analysis. It describes descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It also discusses inferential univariate analysis and appropriate statistical tests for different variable types and research questions, including z-tests, t-tests, and chi-square tests. Examples are provided to illustrate calculating and interpreting these statistics.
- Univariate analysis refers to analyzing one variable at a time using statistical measures like proportions, percentages, means, medians, and modes to describe data.
- These measures provide a "snapshot" of a variable through tools like frequency tables and charts to understand patterns and the distribution of cases.
- Measures of central tendency like the mean, median and mode indicate typical or average values, while measures of dispersion like the standard deviation and range indicate how spread out or varied the data are around central values.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
Application of Univariate, Bi-variate and Multivariate analysis Pooja k shettySundar B N
This document discusses different types of statistical analysis used to analyze data. Univariate analysis examines one variable at a time through methods like frequency distributions, histograms, and pie charts. Bivariate analysis considers the relationship between two variables, such as income and weight. Multivariate analysis studies three or more variables simultaneously, with applications in fields like social science, climatology, and medicine.
This document provides an overview of univariate analysis. It defines key terms like variables, scales of measurement, and types of univariate analysis. It describes descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). It also discusses inferential univariate analysis and appropriate statistical tests for different variable types and research questions, including z-tests, t-tests, and chi-square tests. Examples are provided to illustrate calculating and interpreting these statistics.
- Univariate analysis refers to analyzing one variable at a time using statistical measures like proportions, percentages, means, medians, and modes to describe data.
- These measures provide a "snapshot" of a variable through tools like frequency tables and charts to understand patterns and the distribution of cases.
- Measures of central tendency like the mean, median and mode indicate typical or average values, while measures of dispersion like the standard deviation and range indicate how spread out or varied the data are around central values.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
This document provides an overview of statistics concepts including descriptive and inferential statistics. Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode), dispersion (range, standard deviation), and frequency/percentage. Inferential statistics allow inferences to be made about a population based on a sample through hypothesis testing and other statistical techniques. The document discusses preparing data in Excel and using formulas and functions to calculate descriptive statistics. It also introduces the concepts of normal distribution, kurtosis, and skewness in describing data distributions.
This document provides an overview of various quantitative data analysis techniques including parametric and non-parametric statistics, descriptive statistics, contingency analysis, t-tests, ANOVA, correlation, and regression. It discusses assumptions and processes for each technique and how to interpret results. Computer software like SPSS and SAS can be used to analyze large, complex datasets.
This document discusses descriptive statistics and how to calculate them. It covers preparing data for analysis through coding and tabulation. It then defines four types of descriptive statistics: measures of central tendency like mean, median, and mode; measures of variability like range and standard deviation; measures of relative position like percentiles and z-scores; and measures of relationships like correlation coefficients. It provides formulas for calculating common descriptive statistics like the mean, standard deviation, and Pearson correlation.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
This document discusses point and interval estimation. It defines an estimator as a function used to infer an unknown population parameter based on sample data. Point estimation provides a single value, while interval estimation provides a range of values with a certain confidence level, such as 95%. Common point estimators include the sample mean and proportion. Interval estimators account for variability in samples and provide more information than point estimators. The document provides examples of how to construct confidence intervals using point estimates, confidence levels, and standard errors or deviations.
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Introduction to Probability and Probability DistributionsJezhabeth Villegas
This document provides an overview of teaching basic probability and probability distributions to tertiary level teachers. It introduces key concepts such as random experiments, sample spaces, events, assigning probabilities, conditional probability, independent events, and random variables. Examples are provided for each concept to illustrate the definitions and computations. The goal is to explain the necessary probability foundations for teachers to understand sampling distributions and assessing the reliability of statistical estimates from samples.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
The t-test is used to determine if there are significant differences between the means of two groups. An independent-samples t-test was conducted to compare the affective commitment, continuance commitment, and normative commitment of male and female employees. The t-test results showed a significant difference in affective commitment between males (M=3.49720) and females (M=3.38016), but no significant differences in continuance commitment or normative commitment between the two groups.
- MAP testing will take place this week, with detailed information available in announcements
- Next week, students will begin working on their end-of-year projects
- This document provides information about bivariate data, scatter plots, and lines of best fit for a statistics and probability lesson
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This document provides an overview of sampling techniques. It defines key sampling terms like population, sample, sampling frame, and discusses the need for sampling due to constraints of time and money for a full census. The document outlines different sampling methods like simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and snowball sampling. The document emphasizes the importance of representativeness, adequacy and independence for a good sample. It concludes by noting sources of error in sampling like sampling errors and non-sampling errors.
This document provides an overview of inferential statistics. It defines inferential statistics as using samples to draw conclusions about populations and make predictions. It discusses key concepts like hypothesis testing, null and alternative hypotheses, type I and type II errors, significance levels, power, and effect size. Common inferential tests like t-tests, ANOVA, and meta-analyses are also introduced. The document emphasizes that inferential statistics allow researchers to generalize from samples to populations and test hypotheses about relationships between variables.
This document discusses descriptive statistics and measures of central tendency. It defines raw data and descriptive measures. It describes organizing data through ordered arrays and grouped data using frequency distributions. Methods for determining the number of class intervals and class width are provided. Common measures of central tendency - the mean, median and mode - are defined. The mean is the sum of all values divided by the total number. The median is the middle value of ordered data. Examples are given to demonstrate calculating these statistics.
This document discusses descriptive statistics used in research. It defines descriptive statistics as procedures used to organize, interpret, and communicate numeric data. Key aspects covered include frequency distributions, measures of central tendency (mode, median, mean), measures of variability, bivariate descriptive statistics using contingency tables and correlation, and describing risk to facilitate evidence-based decision making. The overall purpose of descriptive statistics is to synthesize and summarize quantitative data for analysis in research.
Univariate, bivariate analysis, hypothesis testing, chi squarekongara
This document provides an introduction to data analysis. It discusses various topics related to measurement and types of data, including univariate and bivariate analysis. For univariate analysis, it describes descriptive statistics such as mean, median, mode, variance, and standard deviation. It also discusses data distributions and different measurement scales. For bivariate analysis, it introduces cross-tabulation and chi-square tests to examine relationships between two variables. Cross-tabulation allows looking at associations between variables through frequencies and percentages in tables, while chi-square can be used to test hypotheses about relationships and determine statistical significance.
This document discusses bivariate analysis of twin data to examine the genetic and environmental contributions to the covariance between two traits. It introduces bivariate twin models that partition the covariance into additive genetic (A), shared environmental (C), and unique environmental (E) components. The expected covariance matrices for MZ and DZ twins are presented. The goal of bivariate analysis is to understand what factors make sets of variables correlate through examining within-twin and cross-twin covariances. Examples of applications to real twin datasets are given.
This document provides an overview of statistics concepts including descriptive and inferential statistics. Descriptive statistics are used to summarize and describe data through measures of central tendency (mean, median, mode), dispersion (range, standard deviation), and frequency/percentage. Inferential statistics allow inferences to be made about a population based on a sample through hypothesis testing and other statistical techniques. The document discusses preparing data in Excel and using formulas and functions to calculate descriptive statistics. It also introduces the concepts of normal distribution, kurtosis, and skewness in describing data distributions.
This document provides an overview of various quantitative data analysis techniques including parametric and non-parametric statistics, descriptive statistics, contingency analysis, t-tests, ANOVA, correlation, and regression. It discusses assumptions and processes for each technique and how to interpret results. Computer software like SPSS and SAS can be used to analyze large, complex datasets.
This document discusses descriptive statistics and how to calculate them. It covers preparing data for analysis through coding and tabulation. It then defines four types of descriptive statistics: measures of central tendency like mean, median, and mode; measures of variability like range and standard deviation; measures of relative position like percentiles and z-scores; and measures of relationships like correlation coefficients. It provides formulas for calculating common descriptive statistics like the mean, standard deviation, and Pearson correlation.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
This document discusses point and interval estimation. It defines an estimator as a function used to infer an unknown population parameter based on sample data. Point estimation provides a single value, while interval estimation provides a range of values with a certain confidence level, such as 95%. Common point estimators include the sample mean and proportion. Interval estimators account for variability in samples and provide more information than point estimators. The document provides examples of how to construct confidence intervals using point estimates, confidence levels, and standard errors or deviations.
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Introduction to Probability and Probability DistributionsJezhabeth Villegas
This document provides an overview of teaching basic probability and probability distributions to tertiary level teachers. It introduces key concepts such as random experiments, sample spaces, events, assigning probabilities, conditional probability, independent events, and random variables. Examples are provided for each concept to illustrate the definitions and computations. The goal is to explain the necessary probability foundations for teachers to understand sampling distributions and assessing the reliability of statistical estimates from samples.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
The t-test is used to determine if there are significant differences between the means of two groups. An independent-samples t-test was conducted to compare the affective commitment, continuance commitment, and normative commitment of male and female employees. The t-test results showed a significant difference in affective commitment between males (M=3.49720) and females (M=3.38016), but no significant differences in continuance commitment or normative commitment between the two groups.
- MAP testing will take place this week, with detailed information available in announcements
- Next week, students will begin working on their end-of-year projects
- This document provides information about bivariate data, scatter plots, and lines of best fit for a statistics and probability lesson
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
This document provides an overview of topics related to research and statistics, including research problems, variables, hypotheses, data collection, presentation, and analysis using SPSS. It discusses key concepts such as descriptive versus inferential statistics, point and interval estimates, and confidence intervals for means and proportions. The document serves as an introduction to research methodology and statistical analysis concepts.
This document provides an overview of sampling techniques. It defines key sampling terms like population, sample, sampling frame, and discusses the need for sampling due to constraints of time and money for a full census. The document outlines different sampling methods like simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and snowball sampling. The document emphasizes the importance of representativeness, adequacy and independence for a good sample. It concludes by noting sources of error in sampling like sampling errors and non-sampling errors.
This document provides an overview of inferential statistics. It defines inferential statistics as using samples to draw conclusions about populations and make predictions. It discusses key concepts like hypothesis testing, null and alternative hypotheses, type I and type II errors, significance levels, power, and effect size. Common inferential tests like t-tests, ANOVA, and meta-analyses are also introduced. The document emphasizes that inferential statistics allow researchers to generalize from samples to populations and test hypotheses about relationships between variables.
This document discusses descriptive statistics and measures of central tendency. It defines raw data and descriptive measures. It describes organizing data through ordered arrays and grouped data using frequency distributions. Methods for determining the number of class intervals and class width are provided. Common measures of central tendency - the mean, median and mode - are defined. The mean is the sum of all values divided by the total number. The median is the middle value of ordered data. Examples are given to demonstrate calculating these statistics.
This document discusses descriptive statistics used in research. It defines descriptive statistics as procedures used to organize, interpret, and communicate numeric data. Key aspects covered include frequency distributions, measures of central tendency (mode, median, mean), measures of variability, bivariate descriptive statistics using contingency tables and correlation, and describing risk to facilitate evidence-based decision making. The overall purpose of descriptive statistics is to synthesize and summarize quantitative data for analysis in research.
Univariate, bivariate analysis, hypothesis testing, chi squarekongara
This document provides an introduction to data analysis. It discusses various topics related to measurement and types of data, including univariate and bivariate analysis. For univariate analysis, it describes descriptive statistics such as mean, median, mode, variance, and standard deviation. It also discusses data distributions and different measurement scales. For bivariate analysis, it introduces cross-tabulation and chi-square tests to examine relationships between two variables. Cross-tabulation allows looking at associations between variables through frequencies and percentages in tables, while chi-square can be used to test hypotheses about relationships and determine statistical significance.
This document discusses bivariate analysis of twin data to examine the genetic and environmental contributions to the covariance between two traits. It introduces bivariate twin models that partition the covariance into additive genetic (A), shared environmental (C), and unique environmental (E) components. The expected covariance matrices for MZ and DZ twins are presented. The goal of bivariate analysis is to understand what factors make sets of variables correlate through examining within-twin and cross-twin covariances. Examples of applications to real twin datasets are given.
The document outlines the steps for conducting frequency distribution, cross-tabulation, and hypothesis testing analyses. It discusses measuring location, variability, and shape in frequency distributions. Cross-tabulation involves analyzing two or more variables simultaneously through tables. Hypothesis testing follows general steps including formulating hypotheses, selecting a test, determining significance levels, collecting data, and making conclusions.
This chapter outline covers frequency distributions, measures of location, variability and shape associated with frequency distributions. It introduces hypothesis testing and provides a general procedure for hypothesis testing. It discusses cross-tabulations for two and three variables and associated statistics like chi-square. It covers hypothesis testing related to differences and parametric and non-parametric tests for one sample, two independent samples and paired samples. It concludes with a focus on internet and computer applications.
This document discusses the key concepts and assumptions of multiple linear regression analysis. It begins by defining the multiple regression model as examining the linear relationship between a dependent variable (Y) and two or more independent variables (X1, X2,...Xk). It then outlines the assumptions of the regression model, including linearity, independence of errors, normality of errors, and equal variance. The document also discusses how to evaluate the significance of the overall model and individual variables using F-tests, t-tests, and confidence intervals. It concludes by discussing how to evaluate the regression assumptions by examining the residuals.
This document discusses factors that influence the selection of data analysis strategies and provides a classification of statistical techniques. It notes that the previous research steps, known data characteristics, statistical technique properties, and researcher background all impact strategy selection. Statistical techniques can be univariate, analyzing single variables, or multivariate, analyzing relationships between multiple variables simultaneously. Multivariate techniques are further classified as dependence techniques, with identifiable dependent and independent variables, or interdependence techniques examining whole variable sets. The document provides examples of common univariate and multivariate techniques.
This thesis is dedicated to all men and women who have contributed to the
knowledge of astronomy and astrology with the noble aim of enlightening us
for a better life on this planet and for the progress and upliftment of civilization from the dawn of time to eternity.
Astrology deals with the observation and correlation of energy exchanging between the
planets of the solar system, the stars in the constellations and life here on Earth. There are astrological markings uncovered which have been found to date back as far as 3000 B.C. The Chaldeans (later known as the Babylonians) kept records which date back to 700 B.C.
Wise men of this period were known as astrologer priests and they were highly esteemed in
the community. Their knowledge was based on observations of the positions of stars and
planets which they used to cast horoscopes and natal charts for kings and rulers. The priests
of the Egyptian Pharaohs were instructed in astrology by the Babylonians for it was a part of
their religion. It is imperative for us to have a glimpse of the dawn of time and the original role that stars played in human lives just to understand the importance of fixed stars and constellations in astrology. Long before there were horoscopes, aspects, houses or signs, dedicated priest astrologers of virtually every civilization, observed and measured sky patterns. The first
picture book of man was the sky and man used to spend hours of the night observing the ever
moving heavenly spectacle. Mysterious risings, settings, and circling of the heavens were
weighed against mundane phenomena of earth, sea and mankind. The ancient wisdom recorded by earliest scribes, known only to these most learned priests of the earliest civilizations has come into our hands.
LINKS TO RASHIS, GRAHA, MAHADASHAS - VEDIC ASTROLOGY BY BARBARA PIJAN LAMAanthony writer
The document lists various useful links related to Vedic astrology concepts on the website of Barbara Pijan Lama, including pages on rashis (zodiac signs), grahas (planets), mahadashas (major planetary periods), and vimshottari dasha (120 year planetary cycle) interpretations. There are over 20 individual links provided to pages analyzing different astrological topics.
ppt on data collection , processing , analysis of data & report writingIVRI
This document provides information on data collection methods and statistical analysis. It discusses various types of data collection including observation, interviews, questionnaires, surveys, and case studies. It also covers primary and secondary sources of data. The document outlines steps for processing and analyzing data such as editing, coding, tabulation, and classification. It describes various statistical tools for analysis including measures of central tendency, dispersion, t-tests, and chi-square tests. Guidelines are provided for writing reports to communicate the results of a research study.
Questionnaire construction is presented by Prakash Aryal. Questionnaires can be used for primary research and involve asking respondents questions either in person or through mail/online surveys. Key steps in constructing a questionnaire include determining the type of survey, developing questions, organizing the question sequence and layout, and pilot testing. Questions should avoid ambiguity, bias, and double meanings. Both open-ended and closed-ended questions can be used, with closed-ended questions being easier to analyze but potentially limiting responses. The order and format of questions is also important to make the questionnaire smooth, logical and easy for respondents to follow.
The document provides an overview of factor analysis, including:
- Factor analysis is a statistical technique used to reduce a large number of variables into a smaller number of underlying factors or components according to patterns of correlation between variables.
- The two main types are exploratory factor analysis, which is used when the underlying factors are unknown, and confirmatory factor analysis, which is used to test hypotheses about a predetermined factor structure.
- Key steps in factor analysis include determining the appropriateness of the data, extracting factors using various criteria, rotating factors to improve interpretation, and interpreting the results including factor loadings and communalities.
This document provides an overview of multivariate analysis techniques, including dependency techniques like multiple regression, discriminant analysis, and MANOVA, as well as interdependency techniques like factor analysis, cluster analysis, and multidimensional scaling. It describes the uses and processes for each technique, such as using multiple regression to predict values, discriminate analysis to classify groups, and factor analysis to reduce variables. The document is signed off with warm wishes from the owner of Power Group.
This is an exclusive presentation on data collection for researchers in National Institutes Labor of Administration & Training (NILAT), Ministry of production, government of Pakistan
Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
This document describes the features and capabilities of a customized retail audit methodology and mobile survey platform. It allows conducting retail audits by scanning product barcodes, taking photos and videos, and answering predefined survey questions to create a dataset for analysis. The platform offers standard and advanced question types, custom scripting, complex grids, barcode scanning, add-on modules, and enhanced analysis and customizable reporting capabilities.
Multidimensional scaling (MDS) is a technique used to analyze proximities or distances between pairs of objects. The goal of MDS is to place objects in a dimensional space such that the distances between objects in that space correspond as closely as possible to the proximities in the original data. There are metric and nonmetric approaches to MDS depending on the level of measurement of the proximities. MDS can be used to visualize perceptions of similarity between objects and allows for further analysis of the dimensional configuration.
Multidimensional scaling (MDS) is a technique used to visualize the similarity or dissimilarity of observations in a geometric space. It can be applied to image measurement, market segmentation, new product development, advertising effectiveness, pricing analysis, channel decisions, and attitude scale construction. MDS involves collecting similarity judgments or preference rankings between items and representing them as points in a multidimensional space. The stress value indicates how well the points fit in the space, with lower values indicating a better fit. The spatial map can be interpreted to understand competition between brands and identify opportunities.
This document provides an overview and categorization of various marketing research techniques. It separates the techniques into mature techniques that have been used for some time, such as correlation analysis and regression analysis, and modern techniques that are newer, such as decision trees, dynamic programming, and technological forecasting. For several of the techniques, a brief explanation of the approach is given. The overall purpose is to familiarize management with the key research tools used by researchers.
This document discusses a research project on the soft drinks market in Bijapur, India. The objectives are to understand consumer tastes, find opportunities for new entrants, and survey existing soft drink outlets. The methodology involves market research and analysis. The benefits are helping companies understand local tastes, analyze risks, and successfully launch new products. It also provides background on ice cream and soft drinks, including their history and the major types of retailers.
SPSS is a statistical software package used for interactive or programmed data analysis. It can perform complex data analysis and statistics with simple commands. Originally called the Statistical Package for the Social Sciences when it was first created in 1968, SPSS is now owned by IBM. The default window in SPSS contains a data editor with two sheets - the data view sheet displays raw data while the variable view sheet defines metadata for each variable. SPSS allows users to easily enter, clean, manage and analyze data to derive useful information for making informed decisions.
This document provides information about various statistical concepts including variables, probability, distributions, hypothesis testing, and Python libraries for statistical analysis. It defines different types of variables, such as continuous, discrete, categorical, and their examples. It also explains concepts like population, sample, central tendency, dispersion, probability, distributions, hypothesis testing, t-test, z-test, ANOVA. Finally, it mentions commonly used Python libraries like SciPy for conducting statistical tests and analysis.
This document discusses correlation and how it measures the strength and direction of the linear relationship between two variables. It provides examples of scatterplots that show the dispersion of data points around the line of averages. A society with little dispersion would provide more certainty about the rewards of improving education levels. The correlation coefficient r quantifies the correlation between two variables, ranging from -1 to 1, with values closer to those extremes indicating a strong correlation. Excel's CORREL function can calculate correlation between variables in a data set.
QUESTION 1Question 1 Describe the purpose of ecumenical servic.docxmakdul
This document contains a summary of a research article that examines the relationship between patient satisfaction scores and inpatient admission volumes at teaching and non-teaching hospitals. The study found a statistically significant positive correlation between patient satisfaction and admissions at teaching hospitals, but a non-significant negative correlation at non-teaching hospitals. When combined, teaching and non-teaching hospitals showed a statistically significant negative correlation. The findings suggest patient satisfaction may impact admissions more at teaching hospitals. The conclusion provides recommendations for healthcare organizations to strategically focus on patient satisfaction to strengthen performance.
The document discusses different statistical methods for organizing and summarizing data, including frequency tables, stem-and-leaf plots, histograms, and scatter plots. It provides examples of each method and explains how to interpret the results, such as looking for relationships between variables in scatter plots. Key terms defined include correlation, variables, and linear regression lines.
Week 5 Lecture 14 The Chi Square Test Quite often, pat.docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are
generally the result of counting how many things fit into a particular category. Whenever we
make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes
in these visual patterns will be our first clues that things have changed, and the first clue that we
need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving
counts (how many fit into this category, how many into that, etc.) is the chi-square. It is
extremely easy to calculate and has many more uses than we will cover. Examining patterns
involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of
these uses have a common trait: they involve counts per group. In fact, the chi-square is the only
statistic we will look at that we use when we have counts per multiple groups (Tanner &
Youssef-Morgan, 2013).
Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches
some pattern we are interested in. Example: Are the employees in our example company
distributed equal across the grades? Or, a more reasonable expectation for a company might be
are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by
generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all
of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we
determine the p-value of getting a result as large or larger to determine if we reject or not reject
our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx
Statistics window rather than the Data Analysis where we found the t and ANOVA test
functions. The most important for us are:
• CHISQ.TEST (actual range, expected range) – returns the p-value for the test
• CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value
or probability value used.
• CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual
range, expected range) will provide us with the p-value of the calculated chi square value (but
does not give us the actual calculated chi square value for the test). We can compare this value
against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting
the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated
value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df).
This document provides an overview of graphing principles and their application in economics. It begins with an introduction explaining the importance of understanding graphs. It then presents a series of slides explaining key graphing concepts like the Cartesian plane, linear relationships between variables, calculating slope, and using the equation y=mx+b to represent relationships. Examples are given for positive, negative, and no relationships between real-world variables. Both linear and non-linear relationships are covered. The goal is to establish the foundational graphing skills needed for understanding economics.
Week 5 Lecture 14 The Chi Square TestQuite often, patterns of .docxcockekeshia
Week 5 Lecture 14
The Chi Square Test
Quite often, patterns of responses or measures give us a lot of information. Patterns are generally the result of counting how many things fit into a particular category. Whenever we make a histogram, bar, or pie chart we are looking at the pattern of the data. Frequently, changes in these visual patterns will be our first clues that things have changed, and the first clue that we need to initiate a research study (Lind, Marchel, & Wathen, 2008).
One of the most useful test in examining patterns and relationships in data involving counts (how many fit into this category, how many into that, etc.) is the chi-square. It is extremely easy to calculate and has many more uses than we will cover. Examining patterns involves two uses of the Chi-square - the goodness of fit and the contingency table. Both of these uses have a common trait: they involve counts per group. In fact, the chi-square is the only statistic we will look at that we use when we have counts per multiple groups (Tanner & Youssef-Morgan, 2013). Chi Square Goodness of Fit Test
The goodness of fit test checks to see if the data distribution (counts per group) matches some pattern we are interested in. Example: Are the employees in our example company distributed equal across the grades? Or, a more reasonable expectation for a company might be are the employees distributed in a pyramid fashion – most on the bottom and few at the top?
The Chi Square test compares the actual versus a proposed distribution of counts by generating a measure for each cell or count: (actual – expected)2/actual. Summing these for all of the cells or groups provides us with the Chi Square Statistic. As with our other tests, we determine the p-value of getting a result as large or larger to determine if we reject or not reject our null hypothesis. An example will show the approach using Excel.
Regardless of the Chi Square test, the chi square related functions are found in the fx Statistics window rather than the Data Analysis where we found the t and ANOVA test functions. The most important for us are:
· CHISQ.TEST (actual range, expected range) – returns the p-value for the test
· CHISQ.INV.RT(p-value, df) – returns the actual Chi Square value for the p-value or probability value used.
· CHISQ.DIST.RT(X, df) – returns the p-value for a given value.
When we have a table of actual and expected results, using the =CHISQ.TEST(actual range, expected range) will provide us with the p-value of the calculated chi square value (but does not give us the actual calculated chi square value for the test). We can compare this value against our alpha criteria (generally 0.05) to make our decision about rejecting or not rejecting the null hypothesis.
If, after finding the p-value for our chi square test, we want to determine the calculated value of the chi square statistic, we can use the =CHISQ.INV.RT(probability, df) function, the value for probability is .
This document provides an overview of graphing principles and their application in economics. It begins with an introduction explaining the importance of understanding graphs. It then presents a series of slides explaining key graphing concepts like the Cartesian plane, linear relationships between variables, measuring slope, and using the equation y=mx+b to represent linear relationships algebraically. Examples are given for positive, negative, and no relationships between real-world variables like income and life expectancy. Non-linear relationships are also briefly discussed. The document emphasizes that understanding graphs is critical for success in economics courses.
The document discusses correlation, which is a statistic that measures the strength and direction of the relationship between two variables. It provides an example of calculating the correlation between height and self-esteem using made-up data from 20 individuals. The correlation is found to be 0.73, indicating a strong positive relationship. The significance of the correlation is then tested to determine if it is likely due to chance.
This document summarizes quantitative data analysis techniques for summarizing data from samples and generalizing to populations. It discusses variables, simple and effect statistics, statistical models, and precision of estimates. Key points covered include describing data distribution through plots and statistics, common effect statistics for different variable types and models, ensuring model fit, and interpreting precision, significance, and probability to generalize from samples.
This document discusses quantitative and qualitative data analysis techniques. It covers:
- Displays for numerical (frequency charts, histograms) and categorical data (bar charts, pie charts, contingency tables).
- Measures for numerical data including mean, median, mode, range, variance, standard deviation, and quartiles.
- Scatter plots to examine relationships between two quantitative variables and measures of association like covariance and correlation coefficient.
- Contingency tables to study relationships between two categorical variables and examine dependency/independency.
- An example analyzing Titanic passenger data using contingency tables to examine the "first-class passengers first" policy.
The document discusses crosstabulation, which shows the relationship between two or more categorical variables through frequency tables. It explains how to compute crosstabs in SPSS by selecting variables for the row and column and choosing relevant statistics like chi-square. The chi-square test determines if the frequencies differ from what is expected by chance alone. It assumes categories are independent and expected counts are at least 5 in each cell.
This document summarizes quantitative data analysis techniques. It discusses how to summarize data using simple statistics like means and standard deviations. It also covers effect statistics that summarize relationships between variables, such as slopes from regression. Statistical tests like t-tests and ANOVA are used to generalize sample results to populations and assess statistical significance. Precision is expressed using confidence intervals rather than just p-values. More complex models can also be reduced to these foundational analyses.
Data analysis test for association BY Prof Sachin Udepurkarsachinudepurkar
1) The document discusses analyzing relationships between variables through bivariate analysis. Bivariate analysis examines the relationship between two variables and can determine direction, strength, and statistical significance.
2) It provides examples of using scatter plots and calculating covariance to visually represent and quantify relationships between variables. Covariance measures how much two variables change together.
3) Calculating the correlation coefficient further standardizes and quantifies relationships, resulting in a number between -1 and 1 that indicates the strength and direction of a relationship. Strong positive or negative correlations near 1 or -1 show clear relationships between variables.
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
This section discusses analyzing categorical data:
- It introduces categorical variables and how to construct frequency tables and graphs like bar graphs and pie charts to display categorical variable distributions.
- It explains how to construct and interpret two-way tables to analyze relationships between two categorical variables, and how to examine marginal and conditional distributions.
- It emphasizes organizing statistical problems using a four step approach of stating the question, planning an approach, doing calculations/graphs, and concluding.
marketing research & applications on SPSSANSHU TIWARI
The document discusses various statistical techniques used in marketing research to analyze survey data, including frequency distributions, measures of central tendency and variability, hypothesis testing, and cross-tabulation. Frequency distributions are used to determine the mean, mode, median and answer questions about single variables. Hypothesis testing involves forming hypotheses, selecting a test, determining significance levels, collecting data, and making statistical decisions. Cross-tabulation examines relationships between two or more variables using techniques like chi-square tests. Both parametric and non-parametric tests are used depending on variable scales.
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
2. So far the statistical methods we
have used only permit us to:
• Look at the frequency in which certain
numbers or categories occur.
• Look at measures of central tendency such
as means, modes, and medians for one
variable.
• Look at measures of dispersion such as
standard deviation and z scores for one
interval or ratio level variable.
3. Bivariate analysis allows us to:
• Look at associations/relationships among
two variables.
• Look at measures of the strength of the
relationship between two variables.
• Test hypotheses about relationships
between two nominal or ordinal level
variables.
4. For example, what does this table tell us about
opinions on welfare by gender?
Support cutting
welfare benefits Male Female
for immigrants
Yes 15 5
No 10 20
Total 25 25
5. Are frequencies sufficient to
allow us to make comparisons
about groups?
What other information do we
need?
6. Is this table more helpful?
Benefits for Males Female
Immigrants
Yes 15 (60%) 5 (20%)
No 10 (40%) 20 (80%)
Total 25 (100%) 25 (100%)
7. How would you write a sentence
or two to describe what is in this
table?
8. Rules for cross-tabulation
• Calculate either column or row percents.
• Calculations are the number of frequencies
in a cell of a table divided by the total
number of frequencies in that column or
row, for example 20/25 = 80.0%
• All percentages in a column or row should
total 100%.
9. Let’s look at another example –
social work degrees by gender
Social Work Male Female
Degree
BA 20 (33.3%) 20 ( %)
MSW 30 ( ) 70 (70.0%)
Ph.D. 10 (16.7%) 10 (10.0%)
60 (100.0%) 100 (100.0%
10. Questions:
What group had the largest percentage of
Ph.Ds?
What are the ways in which you could
find the missing numbers?
Is it obvious why you would use
percentages to make comparisons among
two or more groups?
11. In the following table, were people with drug,
alcohol, or a combination of both most likely
to be referred for individual treatment?
Services Alcohol Drugs Both
Individual 10 (25%) 30 (60%) 5 (50%)
Treatment
Group 10 (25%) 10 (20%) 2 (20%)
Treatment
AA 20 (50%) 10 (20%) 3 (30%)
Total 40 (100%) 50 (100%) 10 (100%)
12. Use the same table to answer the
following question:
How much more likely are
people with alcohol problems
alone to be referred to AA than
people with drug problems or a
combination of drug and alcohol
problems?
13. We use cross-tabulation when:
• We want to look at relationships among two
or three variables.
• We want a descriptive statistical measure to
tell us whether differences among groups
are large enough to indicate some sort of
relationship among variables.
14. Cross-tabs are not sufficient to:
• Tell us the strength or actually size of the relationships
among two or three variables.
• Test a hypothesis about the relationship between two or
three variables.
• Tell us the direction of the relationship among two or more
variables.
• Look at relationships between one nominal or ordinal
variable and one ratio or interval variable unless the range
of possible values for the ratio or interval variable is small.
What do you think a table with a large number of ratio
values would look like?
15. We can use cross-tabs to visually
assess whether independent and
dependent variables might be
related. In addition, we also use
cross-tabs to find out if
demographic variables such as
gender and ethnicity are related
to the second variable.
16. For example, gender may
determine if someone votes
Democratic or Republican or if
income is high, medium, or low.
Ethnicity might be related to
where someone lives or attitudes
about whether undocumented
workers should receive driver’s
licenses.
17. Because we use tables in these ways, we can
set up some decision rules about how to use
tables.
• Independent variables should be column variables.
• If you are not looking at independent and
dependent variable relationships, use the variable
that can logically be said to influence the other as
your column variable.
• Using this rule, always calculate column
percentages rather than row percentages.
• Use the column percentages to interpret your
results.
18. For example,
• If we were looking at the relationship between
gender and income, gender would be the column
variable and income would be the row variable.
Logically gender can determine income. Income
does not determine your gender.
• If we were looking at the relationship between
ethnicity and location of a person’s home,
ethnicity would be the column variable.
• However, if we were looking at the relationship
between gender and ethnicity, one does not
influence the other. Either variable could be the
column variable.
19. SPSS will allow you to choose a
column variable and row variable
and whether or not your table
will include column or row
percents.
20. You must use an additional statistic, chi-
square, if you want to:
• Test a hypothesis about two variables.
• Look at the strength of the relationship between an
independent and dependent variable.
• Determine whether the relationship between the
two variables is large enough to rule out random
chance or sampling error as reasons that there
appears to be a relationship between the two
variables.
21. Chi-square is simply an extension of a
cross-tabulation that gives you more
information about the relationship.
However, it provides no information
about the direction of the relationship
(positive or negative) between the two
variables.
22. Let’s use the following table to
test a hypothesis:
Education
Income High Low Total
High (Above 40 50
$40,000)
Low ($39,999 50
or less)
Total 50 50 100
23. I have not filled in all of the information
because we need to talk about two concepts
before we start calculations:
• Degrees of Freedom: In any table, there are
a limited number of choices for the values
in each cell.
• Marginals: Total frequencies in columns
and rows.
24. Let’s look at the number of choices
we have in the previous table:
Education
Income High Low Total
High (Above 40 50
$40,000)
Low ($39,999 50
or less)
Total 50 50 100
25. So the table becomes:
Education
Income High Low Total
High (Above 40 10 50
$40,000)
Low ($39,999 10 40 50
or less)
Total 50 50 100
26. The rules for determining degrees of freedom
in cross-tabulations or contingency tables:
• In any two by two tables (two columns, two
rows, excluding marginals) DF = 1.
• For all other tables, calculate DF as:
(c -1 ) * (r-1) where c = columns and r =
rows.
( So for a table with 3 columns and 4 rows,
DF = ____. )
27. Importance of Degrees of Freedom
• You will see degrees of freedom on your SPSS
print out.
• Most types of inferential statistics use DF in
calculations.
• In chi-square, we need to know DF if we are
calculating chi-square by hand. You must use the
value of the chi-square and DF to determine if the
chi-square value is large enough to be statistically
significant (consult chi-square table in most
statistics books).
28. Steps in testing a hypothesis:
• State the research hypothesis
• State the null hypothesis
• Choose a level of statistical significance
(alpha level)
• Select and compute the test statistic
• Make a decision regarding whether to
accept or reject the null hypothesis.
29. Calculating Chi-Square
• Formula is [0 - E]2
E
Where 0 is the observed value in a cell
E is the expected value in the same
cell we would see if there was no
association
30. First steps
Alternative hypothesis is: There is a relationship
between income level and education for
respondents in a survey of BA students.
Null hypothesis is: There is no relationship between
income level and education for respondents in a
survey of BA students
Confidence level set at .05
31. Rules for determining whether the chi-square
statistic and probability are large enough to verify a
relationship.
• For hand calculations, use the degree(s) of
freedom and the confidence level you set to check
the Chi-square table found in most statistics
books. For the chi-square to be statistically
significant, it must be the same size or larger than
the number in the table.
• On an SPSS print out, the p. or significance value
must be the same size or smaller than your
significance level.
32. The formula for expected values are
E = R*C
Education
Income High Low Total
High (Above 25 25 50
$40,000)
Low ($39,999 25 25 50
or less)
Total 50 50 100
33. Go back to our first table
Education
Income High Low Total
High (Above 40 10 50
$40,000)
Low ($39,999 10 40 50
or less)
Total 50 50 100
34. Chi-square calculation is
Expected
Values Chi-square
Cell 1 50 * 50/100 25 (40-25)2/25 9
Cell 2 50*50/100 25 (10-25)2/25 9
Cell 3 50 * 50/100 25 (10-25)2/25 9
Cell 4 50*50/100 25 (40-25)2/25 9
36
At .05, 1 = df, chi-square must be larger
than 3.84 to be statistically significant
35. Let’s calculate another chi-square- service
receipt by location of residence
Service Urban Rural Total
Yes 20 40 60
No 30 10 40
Total 50 50 100
36. For this table,
• DF = 1
• Alternative hypothesis:
Receiving service is associated with
location of residence.
Null hypothesis:
There is no association between receiving
service and location of residence.
37. Calculations for chi-square are
Expected
Values Chi-square
Cell 1 50 * 60/100 30 (20-30)2/30 3.33
Cell 2 50*40/100 20 (30-20)2/20 5.00
Cell 3 50*60/100 30 (40-30)2/30 3.33
Cell 4 50*40/100 20 (10-20)2/20 5.00
16.67
At 1 DF at .01 chi-square must be greater than 6.64. Do
we accept or reject the null hypothesis?
38. Running chi-square in SPSS
• Select descriptive statistics
• Select cross-tabulation
• Highlight your independent variable and click on the arrow.
• Highlight your dependent variable and click on the arrow.
• Select Cells
• Choose column percents
• Click continue
• Select statistics
• Select chi-square
• Click continue
• Click ok
39. SPSS print out
Chi-Square Tests
Asymp. Sig.
Value df (2-sided)
Pearson Chi-Square 2.569 a 5 .766
Likelihood Ratio 2.590 5 .763
Linear-by-Linear
.087 1 .768
Association
N of Valid Cases 336
a. 2 cells (16.7%) have expected count less than 5. The
minimum expected count is 1.57.
40. Recode
• To run ratio or interval level variables into SPSS
you need to recode or change the variable into a
categorical or nominal or ordinal variable.
You first need to decide how you will set up
categories and assign a number to them.
For example if your ratio variables for Age are: 25,
37, 42, 50, and 64, you might decide on two
categories: 1 = under 50
2 = 50 and over
41. Recode Instructions
• Go to Transform menu
• Go to Recode
• Select different variable
• Type in new variable name
• Click continue
• Enter range of ratio numbers for first category (25 to 49)
• Enter number for first category (1) in right hand screen.
• Click Add
• Enter range of ratio numbers (50 to 54) for category two
• Enter number for second category (2)
• Click Add
• Click Continue
• Click Change
• Click o.k.