This document discusses correlations and how to perform them using SPSS. It defines correlation as finding a relationship between two variables, without implying causation. There are two parts to a correlation analysis: 1) assessing the significance of the correlation, which indicates how consistent the association is between variables, and 2) the coefficient of correlation, which indicates the magnitude and direction of the correlation. The document outlines the assumptions that must be met to perform correlations in SPSS, such as having quantitative variables, no outliers, and normally distributed data. It then provides step-by-step instructions for conducting correlations in SPSS and interpreting the output.
This document discusses statistical analysis techniques such as descriptive analysis, reliability testing, correlation, and regression analysis. It provides details on calculating mean, standard deviation, Cronbach's alpha, Pearson's correlation coefficient, and using regression to analyze relationships between variables and test for mediation. Mediation is tested using Baron and Kenny's four step approach to determine if a third variable mediates the relationship between an independent and dependent variable.
The document discusses various statistical methods for analyzing relationships between variables, including chi-square tests, measures of association like lambda and gamma, and rank correlation. Chi-square tests can be used to test for independence and goodness of fit between nominal or ordinal variables. Measures like lambda and gamma range from 0 to 1 and indicate the strength of association while controlling for errors. Rank correlation assesses relationships between variables when only ordinal data is available by analyzing the agreement between ranks. Cross tabulation allows investigating patterns of bivariate association through distribution analysis.
The document discusses chi-square test and its properties. It defines chi-square as a non-parametric statistical test used for discrete data to test for independence and goodness of fit between observed and expected frequencies. The chi-square test has some key assumptions including independent random samples, nominal or ordinal level data, and no expected cell counts below 5. It is calculated by subtracting expected from observed frequencies, squaring the differences, and dividing by expected counts. The chi-square test can identify if there is a significant association between variables but does not measure the strength of the association.
Correlation is a statistical technique used to determine the degree of relationship between two variables. Correlational research aims to identify and describe relationships but does not imply causation. Positive correlation indicates high scores on one variable are associated with high scores on the other, while negative correlation means high scores on one variable are associated with low scores on the other. Correlational research can be used for explanatory or predictive purposes. More complex techniques like multiple regression allow prediction using combinations of variables. Threats to internal validity like subject characteristics must be controlled.
An introduction to mediation analysis using SPSS software (specifically, Andrew Hayes' PROCESS macro). This was a workshop I gave at the Crossroads 2015 conference at Dalhousie University, March 27, 2015.
This document discusses correlations and how to perform them using SPSS. It defines correlation as finding a relationship between two variables, without implying causation. There are two parts to a correlation analysis: 1) assessing the significance of the correlation, which indicates how consistent the association is between variables, and 2) the coefficient of correlation, which indicates the magnitude and direction of the correlation. The document outlines the assumptions that must be met to perform correlations in SPSS, such as having quantitative variables, no outliers, and normally distributed data. It then provides step-by-step instructions for conducting correlations in SPSS and interpreting the output.
This document discusses statistical analysis techniques such as descriptive analysis, reliability testing, correlation, and regression analysis. It provides details on calculating mean, standard deviation, Cronbach's alpha, Pearson's correlation coefficient, and using regression to analyze relationships between variables and test for mediation. Mediation is tested using Baron and Kenny's four step approach to determine if a third variable mediates the relationship between an independent and dependent variable.
The document discusses various statistical methods for analyzing relationships between variables, including chi-square tests, measures of association like lambda and gamma, and rank correlation. Chi-square tests can be used to test for independence and goodness of fit between nominal or ordinal variables. Measures like lambda and gamma range from 0 to 1 and indicate the strength of association while controlling for errors. Rank correlation assesses relationships between variables when only ordinal data is available by analyzing the agreement between ranks. Cross tabulation allows investigating patterns of bivariate association through distribution analysis.
The document discusses chi-square test and its properties. It defines chi-square as a non-parametric statistical test used for discrete data to test for independence and goodness of fit between observed and expected frequencies. The chi-square test has some key assumptions including independent random samples, nominal or ordinal level data, and no expected cell counts below 5. It is calculated by subtracting expected from observed frequencies, squaring the differences, and dividing by expected counts. The chi-square test can identify if there is a significant association between variables but does not measure the strength of the association.
Correlation is a statistical technique used to determine the degree of relationship between two variables. Correlational research aims to identify and describe relationships but does not imply causation. Positive correlation indicates high scores on one variable are associated with high scores on the other, while negative correlation means high scores on one variable are associated with low scores on the other. Correlational research can be used for explanatory or predictive purposes. More complex techniques like multiple regression allow prediction using combinations of variables. Threats to internal validity like subject characteristics must be controlled.
An introduction to mediation analysis using SPSS software (specifically, Andrew Hayes' PROCESS macro). This was a workshop I gave at the Crossroads 2015 conference at Dalhousie University, March 27, 2015.
Generalized Linear Models for Between-Subjects Designssmackinnon
This document provides an overview of generalized linear models (GLiM) for analyzing between-subjects designs. It discusses key assumptions of between-subjects ANOVA such as normality and homogeneity of variance. It then explains how GLiM in SPSS can be used as an alternative approach that describes the distribution of the outcome variable, specifies a link function, and uses maximum likelihood estimation rather than ordinary least squares. The document walks through an example comparing models with different distributions and link functions, and demonstrates interpreting output including parameter estimates, tests of effects, and estimated marginal means.
Correlational research describes the linear relationship between two or more variables without attributing cause and effect. The correlation coefficient is used to measure the strength of this relationship on a scale from -1 to 1. Positive correlations indicate variables increase or decrease together, while negative correlations mean they change in opposite directions. Scatterplots visually depict the correlation by showing how paired values of different variables relate on a graph. The Pearson's r formula is commonly used to calculate correlation coefficients from sample data.
This document provides highlights and key concepts for an exam on structural equation modeling (SEM). It defines terms like path coefficients, direct/indirect/total effects, identification, and discusses techniques for assessing model fit. Identification issues are more likely for models with large numbers of coefficients, reciprocal effects, or many similar concepts. The document also outlines steps in SEM like model specification, identification, estimation, and respecification.
Regression analysis is a statistical technique used to model relationships between variables. It allows one to predict the average value of a dependent variable based on the value of one or more independent variables. The key ideas are that the dependent variable is influenced by the independent variables in a linear or curvilinear fashion, and regression provides an equation to estimate the dependent variable given values of the independent variables. Common applications of linear regression include forecasting, determining relationships between variables, and estimating how changes in one variable impact another.
This document discusses Chi Square and related procedures for analyzing categorical data. It explains that Chi Square can be used for goodness of fit tests to check if a sample follows a particular distribution, and for tests of association to check if two categorical variables are related. It provides examples of how to conduct and interpret Chi Square goodness of fit and association tests using SPSS. Other related procedures discussed include Fisher's Exact Test for small sample sizes and McNamer's Test for analyzing changes in paired categorical data.
QUANTITATIVE DATA ANALYSIS HOW TO DO A T-TEST ON MS-EXCEL AND SPSSICFAI Business School
This document provides instructions for performing a t-test in Microsoft Excel and SPSS. It explains that a t-test is used to test the null hypothesis that the means of two populations are equal. It then outlines the 7 step process to run a t-test in Excel, including selecting the data ranges, hypothesized mean difference, and output range. For SPSS, it lists the 4 step process of selecting the grouping and test variables, defining the groups, and running the independent samples t-test.
1. Researchers should consult multiple fit statistics when evaluating the fit of a confirmatory factor analysis model as no single statistic is ideal.
2. Different fit statistics were developed with different rationales and assess model fit in various ways.
3. Sample size impacts the chi-square statistic, with larger samples increasing the likelihood of rejection.
Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
A gentle introduction to growth curves using SPSSsmackinnon
A brief introduction on how to conduct growth curve statistical analyses using SPSS software, including some sample syntax. Originally presented at IWK Statistics Seminar Series at the IWK Health Center, Halifax, NS, May 1, 2013.
The document discusses correlation, regression, and hypothesis testing involving two variables. It defines correlation and the correlation coefficient r, which measures the strength of a linear relationship between two variables. Regression analyzes the relationship between variables to determine if it is positive/negative and linear/nonlinear. Hypothesis tests using r evaluate whether a linear correlation exists between two variables in a population. Confidence intervals and predictions can be made from significant relationships.
The chi-square test is used to compare observed data to expected data. It determines if differences between the observed and expected numbers are due to chance or something more significant. The chi-square test has several key steps: stating the null and alternative hypotheses, choosing a significance level, finding the critical value, calculating the test statistic by summing the squared differences between observed and expected values divided by the expected value, and making a conclusion by comparing the test statistic to the critical value. The chi-square test has assumptions of adequate sample sizes and independence of data. It is useful for testing goodness of fit, independence of attributes, and homogeneity.
What is a Wilcoxon Sign-Ranked Test (pair t non para)?Ken Plummer
The Wilcoxon Signed-Ranked Test is a non-parametric statistical hypothesis test used to compare two related samples, such as the same set of observations measured under two different conditions, to assess whether their population mean ranks differ. It can be used as an alternative to the paired Student's t-test when the assumption of normality is not met or the data is only on an ordinal scale. Like the paired t-test, it tests whether the mean or median of the differences between paired observations is significantly different from zero.
The document provides an overview of the chi-squared test and examples of its applications. It introduces the chi-squared test as a method to assess how well observed data fits expected theoretical results. Several examples are given demonstrating chi-squared tests of goodness of fit for binomial, Poisson, normal and contingency table distributions. Practice questions are also provided involving a range of chi-squared test applications.
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
This document discusses various statistical concepts for summarizing and analyzing quantitative data, including:
- Descriptive statistics like mean, median, mode, range, and standard deviation to summarize central tendency and variability.
- Different measurement scales for data like nominal, ordinal, interval, and ratio scales.
- Graphical representations of data like histograms, bar graphs, and scatterplots.
- Correlational research which investigates relationships between two variables using the Pearson correlation coefficient.
The document discusses the Chi-square (χ2) test, which is a non-parametric test used to test hypotheses about distributions of frequencies across categories of data. It can be used to test for comparing variance and to test for independence between two variables. The summary provides steps for applying the Chi-square test, including calculating expected frequencies, observed vs expected values, the Chi-square statistic, and comparing it to critical values. An example application to test the effectiveness of vaccination in preventing smallpox is shown.
Correlational research designs examine relationships between two or more variables without manipulating any variables. They are used to describe and measure the degree of association between variables or sets of scores. There are two main types of correlational designs: explanatory/explanation designs which examine associations between variables, and prediction designs which identify predictor variables that can anticipate outcomes. Key aspects of correlational research include scatterplots, correlation coefficients, significance testing, and multiple variable techniques like partial correlation and multiple regression.
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test for independence and goodness of fit. Karl Pearson introduced the chi-square test to compare observed and expected frequencies across categories. The test calculates a chi-square statistic and compares it to a critical value to determine if the null hypothesis that the distributions are the same can be rejected. Examples demonstrated how to calculate expected frequencies, the chi-square statistic, degrees of freedom, and compare to critical values to test independence between variables and goodness of fit to theoretical distributions.
This document summarizes a study that used canonical correlation analysis to detect potential bias in faculty promotion scores at American University of Nigeria. The study aimed to test if canonical correlation could identify bias scoring, determine the influence of individual assessors' scores, and discriminate between promotable and non-promotable candidates. The results showed that canonical correlation could detect bias and influence with over 90% confidence and correctly classified candidates into promotable and non-promotable groups, rejecting the null hypotheses. Thus, canonical correlation was found to be an effective statistical tool for unbiased promotion scoring and decision making at the university.
This document discusses various types and methods of measuring correlation between two variables. It describes correlation as a statistical tool to measure the degree of relationship between variables. Some key methods covered include scatter diagrams, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient. Positive and negative correlation examples are provided. The document also differentiates between simple, multiple, partial, and total correlation, as well as linear and non-linear correlation.
This document discusses various statistical analyses that can be used to analyze the relationship between variables:
1. Bivariate correlation measures the strength and direction of association between two variables using Pearson's correlation coefficient (r). A significant correlation is when p<0.05.
2. Linear regression analysis uses linear equations to model relationships between a dependent variable and one or more independent variables. It identifies outliers that do not follow patterns.
3. Multiple regression extends linear regression to multiple independent variables, allowing analysis of their collective influence on a dependent variable. It provides measures like R and R-squared of the model's accuracy and fit.
Generalized Linear Models for Between-Subjects Designssmackinnon
This document provides an overview of generalized linear models (GLiM) for analyzing between-subjects designs. It discusses key assumptions of between-subjects ANOVA such as normality and homogeneity of variance. It then explains how GLiM in SPSS can be used as an alternative approach that describes the distribution of the outcome variable, specifies a link function, and uses maximum likelihood estimation rather than ordinary least squares. The document walks through an example comparing models with different distributions and link functions, and demonstrates interpreting output including parameter estimates, tests of effects, and estimated marginal means.
Correlational research describes the linear relationship between two or more variables without attributing cause and effect. The correlation coefficient is used to measure the strength of this relationship on a scale from -1 to 1. Positive correlations indicate variables increase or decrease together, while negative correlations mean they change in opposite directions. Scatterplots visually depict the correlation by showing how paired values of different variables relate on a graph. The Pearson's r formula is commonly used to calculate correlation coefficients from sample data.
This document provides highlights and key concepts for an exam on structural equation modeling (SEM). It defines terms like path coefficients, direct/indirect/total effects, identification, and discusses techniques for assessing model fit. Identification issues are more likely for models with large numbers of coefficients, reciprocal effects, or many similar concepts. The document also outlines steps in SEM like model specification, identification, estimation, and respecification.
Regression analysis is a statistical technique used to model relationships between variables. It allows one to predict the average value of a dependent variable based on the value of one or more independent variables. The key ideas are that the dependent variable is influenced by the independent variables in a linear or curvilinear fashion, and regression provides an equation to estimate the dependent variable given values of the independent variables. Common applications of linear regression include forecasting, determining relationships between variables, and estimating how changes in one variable impact another.
This document discusses Chi Square and related procedures for analyzing categorical data. It explains that Chi Square can be used for goodness of fit tests to check if a sample follows a particular distribution, and for tests of association to check if two categorical variables are related. It provides examples of how to conduct and interpret Chi Square goodness of fit and association tests using SPSS. Other related procedures discussed include Fisher's Exact Test for small sample sizes and McNamer's Test for analyzing changes in paired categorical data.
QUANTITATIVE DATA ANALYSIS HOW TO DO A T-TEST ON MS-EXCEL AND SPSSICFAI Business School
This document provides instructions for performing a t-test in Microsoft Excel and SPSS. It explains that a t-test is used to test the null hypothesis that the means of two populations are equal. It then outlines the 7 step process to run a t-test in Excel, including selecting the data ranges, hypothesized mean difference, and output range. For SPSS, it lists the 4 step process of selecting the grouping and test variables, defining the groups, and running the independent samples t-test.
1. Researchers should consult multiple fit statistics when evaluating the fit of a confirmatory factor analysis model as no single statistic is ideal.
2. Different fit statistics were developed with different rationales and assess model fit in various ways.
3. Sample size impacts the chi-square statistic, with larger samples increasing the likelihood of rejection.
Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
A gentle introduction to growth curves using SPSSsmackinnon
A brief introduction on how to conduct growth curve statistical analyses using SPSS software, including some sample syntax. Originally presented at IWK Statistics Seminar Series at the IWK Health Center, Halifax, NS, May 1, 2013.
The document discusses correlation, regression, and hypothesis testing involving two variables. It defines correlation and the correlation coefficient r, which measures the strength of a linear relationship between two variables. Regression analyzes the relationship between variables to determine if it is positive/negative and linear/nonlinear. Hypothesis tests using r evaluate whether a linear correlation exists between two variables in a population. Confidence intervals and predictions can be made from significant relationships.
The chi-square test is used to compare observed data to expected data. It determines if differences between the observed and expected numbers are due to chance or something more significant. The chi-square test has several key steps: stating the null and alternative hypotheses, choosing a significance level, finding the critical value, calculating the test statistic by summing the squared differences between observed and expected values divided by the expected value, and making a conclusion by comparing the test statistic to the critical value. The chi-square test has assumptions of adequate sample sizes and independence of data. It is useful for testing goodness of fit, independence of attributes, and homogeneity.
What is a Wilcoxon Sign-Ranked Test (pair t non para)?Ken Plummer
The Wilcoxon Signed-Ranked Test is a non-parametric statistical hypothesis test used to compare two related samples, such as the same set of observations measured under two different conditions, to assess whether their population mean ranks differ. It can be used as an alternative to the paired Student's t-test when the assumption of normality is not met or the data is only on an ordinal scale. Like the paired t-test, it tests whether the mean or median of the differences between paired observations is significantly different from zero.
The document provides an overview of the chi-squared test and examples of its applications. It introduces the chi-squared test as a method to assess how well observed data fits expected theoretical results. Several examples are given demonstrating chi-squared tests of goodness of fit for binomial, Poisson, normal and contingency table distributions. Practice questions are also provided involving a range of chi-squared test applications.
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
This document discusses various statistical concepts for summarizing and analyzing quantitative data, including:
- Descriptive statistics like mean, median, mode, range, and standard deviation to summarize central tendency and variability.
- Different measurement scales for data like nominal, ordinal, interval, and ratio scales.
- Graphical representations of data like histograms, bar graphs, and scatterplots.
- Correlational research which investigates relationships between two variables using the Pearson correlation coefficient.
The document discusses the Chi-square (χ2) test, which is a non-parametric test used to test hypotheses about distributions of frequencies across categories of data. It can be used to test for comparing variance and to test for independence between two variables. The summary provides steps for applying the Chi-square test, including calculating expected frequencies, observed vs expected values, the Chi-square statistic, and comparing it to critical values. An example application to test the effectiveness of vaccination in preventing smallpox is shown.
Correlational research designs examine relationships between two or more variables without manipulating any variables. They are used to describe and measure the degree of association between variables or sets of scores. There are two main types of correlational designs: explanatory/explanation designs which examine associations between variables, and prediction designs which identify predictor variables that can anticipate outcomes. Key aspects of correlational research include scatterplots, correlation coefficients, significance testing, and multiple variable techniques like partial correlation and multiple regression.
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test for independence and goodness of fit. Karl Pearson introduced the chi-square test to compare observed and expected frequencies across categories. The test calculates a chi-square statistic and compares it to a critical value to determine if the null hypothesis that the distributions are the same can be rejected. Examples demonstrated how to calculate expected frequencies, the chi-square statistic, degrees of freedom, and compare to critical values to test independence between variables and goodness of fit to theoretical distributions.
This document summarizes a study that used canonical correlation analysis to detect potential bias in faculty promotion scores at American University of Nigeria. The study aimed to test if canonical correlation could identify bias scoring, determine the influence of individual assessors' scores, and discriminate between promotable and non-promotable candidates. The results showed that canonical correlation could detect bias and influence with over 90% confidence and correctly classified candidates into promotable and non-promotable groups, rejecting the null hypotheses. Thus, canonical correlation was found to be an effective statistical tool for unbiased promotion scoring and decision making at the university.
This document discusses various types and methods of measuring correlation between two variables. It describes correlation as a statistical tool to measure the degree of relationship between variables. Some key methods covered include scatter diagrams, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient. Positive and negative correlation examples are provided. The document also differentiates between simple, multiple, partial, and total correlation, as well as linear and non-linear correlation.
This document discusses various statistical analyses that can be used to analyze the relationship between variables:
1. Bivariate correlation measures the strength and direction of association between two variables using Pearson's correlation coefficient (r). A significant correlation is when p<0.05.
2. Linear regression analysis uses linear equations to model relationships between a dependent variable and one or more independent variables. It identifies outliers that do not follow patterns.
3. Multiple regression extends linear regression to multiple independent variables, allowing analysis of their collective influence on a dependent variable. It provides measures like R and R-squared of the model's accuracy and fit.
This document discusses linear regression analysis. It defines simple and multiple linear regression, and explains that regression examines the relationship between independent and dependent variables. The document provides the equations for linear regression analysis, and discusses calculating the slope, intercept, standard error of the estimate, and coefficient of determination. It explains that regression analysis is widely used for prediction and forecasting in areas like advertising and product sales.
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
This document discusses different types and methods of measuring correlation between variables. It covers:
- Types of correlation including simple, multiple, partial, and total correlation.
- Methods for studying correlation such as scatter diagrams, Karl Pearson's coefficient of correlation, and Spearman's rank correlation coefficient.
- Karl Pearson's coefficient measures the strength and direction of the linear relationship between two quantitative variables. Spearman's rank correlation coefficient is used when variables are qualitative or ranked.
- Positive and negative correlation examples are provided like the relationship between temperature and water consumption.
This document discusses linear functions and mathematical modeling. It defines linear functions as having a constant rate of change and being represented by the equation y=mx+b. The document shows how to determine if a dataset represents a linear function by calculating the rate of change. It also discusses using linear models to make predictions by extrapolating or interpolating data points. Guidelines for evaluating the reliability of linear trendlines for prediction are provided.
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Chapter 10: Correlation and Regression
10.1: Correlation
1. The document discusses linear correlation and regression between plasma amphetamine levels and amphetamine-induced psychosis scores using data from 10 patients.
2. A positive correlation was found between the two variables, and a linear regression equation was established to predict psychosis scores from amphetamine levels.
3. However, further statistical tests were needed to determine if the correlation and regression model could be generalized to the overall patient population.
Correlation _ Regression Analysis statistics.pptxkrunal soni
This document discusses correlation and related statistical concepts. Correlation measures the strength and direction of association between two quantitative variables. A correlation of 0 means no association, 1 means perfect positive association, and -1 means perfect negative association. Correlation is independent of measurement units and scaling of variables. Hypothesis testing is used to make inferences about the population correlation based on a sample correlation. The null hypothesis is that the population correlation is 0, and alternative hypotheses specify a non-zero correlation. The test statistic used is Student's t distribution. The null is rejected if the calculated t exceeds the critical value or if the p-value is less than the significance level.
Multiple regression analysis allows modeling of relationships between a dependent variable and multiple independent variables. The model takes the form of Y = β0 + β1X1 + β2X2 + ... + βkXk + ε, where Y is the dependent variable, the X's are independent variables, the β's are coefficients, and ε is the error term. Regression coefficients are estimated to predict Y values and are interpreted as the expected change in Y from a one-unit change in the corresponding X, holding other X's constant. The overall model, individual coefficients, and goodness of fit can be evaluated statistically. Nonlinear relationships may require transforming variables before applying regression.
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 .
Correlation and Regression Analysis using SPSS and Microsoft ExcelSetia Pramana
This document discusses correlation and linear regression analysis. It covers correlation coefficients, linear relationships between variables, assumptions of linear regression, and using SPSS and Excel to conduct correlation and regression analyses. Pearson and Spearman correlation coefficients are introduced as measures of the linear association between two continuous variables. Simple and multiple linear regression models are explained as tools to predict an outcome variable from one or more predictor variables.
Regression analysis is a statistical technique for investigating relationships between variables. Simple linear regression defines a relationship between two variables (X and Y) using a best-fit straight line. Multiple regression extends this to model relationships between a dependent variable Y and multiple independent variables (X1, X2, etc.). Regression coefficients are estimated to define the regression equation, and R-squared and the standard error can be used to assess the goodness of fit of the regression model to the data. Regression analysis has applications in pharmaceutical experimentation such as analyzing standard curves for drug analysis.
This document provides an introduction to key concepts in statistics including measures of central tendency, variation, distributions, and linear regression. It defines the mean, median, and mode as measures of central tendency. Measures of variation described include range, variance, and standard deviation. Common distributions like the normal distribution are explained and its key properties outlined. Hypothesis testing and p-values are also introduced. Finally, the concepts of covariance, correlation, and simple linear regression models are summarized.
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Ash ece 353 week 5 discussions 1 cognitive development and learning neweyavagal
1) The document discusses cognitive development and learning in early childhood education. It describes how school provides opportunities for children to develop cognitive skills through new concepts, exploration, and experimentation.
2) It also discusses a case study of a school psychologist evaluating a child, Michael, to determine appropriate educational placement. Intelligence testing is discussed as well as controversies in assessing intelligence.
3) The teacher is encouraged to discuss strategies used in a lesson to enhance cognition, how those strategies influence development, and how cognitive abilities could be reinforced after the lesson. Additional factors like environment, genetics, and biology that could affect a child's testing performance are also to be considered without diagnosing.
This document provides guidelines for the Week 5 Final Paper assignment in an MSU ECE 345 course. The final project requires students to create a Learning and Development Handbook for Infant and Toddler Teachers that discusses the program's educational philosophy, learning activities, and teaching strategies. It must address specific bullet points including the educational philosophy, theories of child development, stages of development, teaching strategies, activity plans, family engagement, and resources. The handbook aims to demonstrate an understanding of infant and toddler learning and development.
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This document provides instructions for an assignment to create an informational brochure or handout for a hypothetical transition meeting. The brochure is intended to educate other transition team members, such as explaining the role of parents in promoting their child's independence. It should include a definition of special education transition services, the role of the chosen team member, the steps in the transition process and how the member contributes, and questions commonly asked of the member. Sources must be cited and formatting should follow APA style. The overall goal is to justify collaborative roles and examine the transition planning process.
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Ash bus 308 week 4 quiz
1. ASH BUS 308 Week 4 Quiz (3 Set) NEW
Check this A+ tutorial guideline at
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week-4-quiz-recent
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Question 1. The t Stat value is used to determine the statistical
significance of each of the variables listed in a regression
analysis.
Question 2. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 3. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 4. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 5. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 6. Which statement does not belong?
Question 7. Pearson Correlation Coefficient is a mathematical
value that shows the strength of the linear (straight line)
relationship between two variables.
Question 8. A regression analysis uses two distinct types of
data. The first are variables that are at least nominal level.
Question 9. The ANOVA table provides the Significance of F to
use to see if we reject or fail to reject the null hypothesis of no
significance. The Significance of F is also known as the P-value.
Question 10. When performing a regression analysis using the
2. Regression option in Data Analysis, the input for the Y range is
the independent variable (can generally control) and the input
X range is for the dependent variables.
BUS 308 Week 4 Quiz Set 2
Question 1. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 2. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 3. Pearson Correlation Coefficient is a mathematical
value that shows the strength of the linear (straight line)
relationship between two variables.
Question 4. A Pearson correlation of +1.00 is considered a
“perfect positive correlation”. This means….
Question 5. Spearman’s rank order correlation (rho) can be
performed on ordinal or any ranked data.
Question 6. The t Stat value is used to determine the statistical
significance of each of the variables listed in a regression
analysis.
Question 7. Pearson’s Correlation requires at least interval
level data.
Question 8. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 9. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 10. When looking at a regression statistics table,
Multiple R displays the percent of variation in common
between the dependent and all of the independent variables.
BUS 308 Week 4 Quiz Set 3
3. Question 1. Pearson’s Correlation requires at least interval
level data.
Question 2. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 3. When plotting variables on a scatter diagram, the
variables plotted on the Y-axis is the horizontal axis and the X-
axis is the vertical axis.
Question 4. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 5. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 6. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 7. A Pearson correlation of +1.00 is considered a
“perfect positive correlation”. This means….
Question 8. When looking at a regression statistics table,
Multiple R displays the percent of variation in common
between the dependent and all of the independent variables.
Question 9. Which statement does not belong?
Question 10. The t Stat value is used to determine the
statistical significance of each of the variables listed in a
regression analysis.