Regression presentation
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A researcher conducted a study to investigate the relationship between anxiety, motivation, and writing performance. Multiple regression analysis was used to address: 1) how well anxiety and motivation predict writing performance, 2) which is the best predictor. Anxiety and motivation scores from 50 learners were collected via questionnaires and correlated with writing performance scores from essays. The regression model explained 15% of variance in writing performance, with anxiety making the largest unique contribution as the best predictor. Motivation's contribution was not statistically significant.
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One-Way ANOVA
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one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
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Example
A professor at a local University believes there is a relationship between head size and
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The data are shown in the following table.
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Step 2: State the Alternative hypothesis.
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Step 4: State the test statistic.
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variance within samples
F
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STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...
Researchers should take several steps to make statistical results meaningful:
1. Perform a power analysis to determine adequate sample size and ensure power is above .50, ideally .80. Power is the probability of detecting real effects.
2. Never set the alpha level lower than .05 and try to set it higher to .10 if acceptable.
3. Report effect sizes and confidence intervals to provide context around statistical significance. Effect sizes indicate the magnitude of differences between groups.
Fundamental of Statistics and Types of Correlations
This document provides an overview of key concepts in statistics including parametric vs non-parametric statistics, descriptive vs inferential statistics, types of errors, significance levels, correlation, and different correlation coefficients. Parametric statistics rely on assumptions of the normal distribution while non-parametric do not. Descriptive statistics describe data and inferential statistics draw conclusions. Type I and II errors occur when the null hypothesis is incorrectly rejected or not rejected. Significance levels like 0.05 are used to determine statistical significance. Correlation measures the relationship between variables from -1 to 1. Different coefficients like Pearson, Spearman, and Kendall's Tau are used depending on the scale of measurement and data distribution.
Overview of Multivariate Statistical Methods
This is an overview of advanced multivariate statistical methods which have become very relevant in many domains over the last few decades. These methods are powerful and can exploit the massive datasets implemented today in meaningful ways. Typically analytics platforms do not deploy these statistical methods, in favor of straightforward metrics and machine learning, and thus they are often overlooked. Additional references are available as documented.
Week 5 Lecture 14 The Chi Square TestQuite often, patterns of .docx
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 .
Recep maz msb 701 quantitative analysis for managers
Regression analysis is a statistical method used to understand the relationship between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. The summary examines a regression model with the total number of white people ages 18-64 as the dependent variable and the number of white people below the poverty level ages 18-64 as the independent variable. The regression outputs, including the R-squared, adjusted R-squared, significance F, and p-values are interpreted to evaluate the model and relationship between the variables. The analysis finds there is no significant relationship between the two variables in the model.
Recep maz msb 701 quantitative analysis for managers
Regression analysis is a statistical method used to understand the relationship between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. The summary examines a regression model with the total number of white people ages 18-64 as the dependent variable and the number of white people below the poverty level ages 18-64 as the independent variable. The regression outputs, including the R-squared, adjusted R-squared, significance F, and p-values are interpreted to evaluate the model and relationship between the variables. The analysis finds there is no significant relationship between the two variables in the model.
CH3.pdf
- The document discusses the multiple linear regression model, including defining dependent and independent variables, the motivation for using multiple regression, and providing examples.
- It describes how OLS estimation works by minimizing the sum of squared residuals to estimate the intercept and slope parameters. It also discusses how to interpret coefficients from multiple regression by "partialing out" the effects of other independent variables.
- The key assumptions for the multiple regression model are outlined, including that it is linear in parameters, has a random sample with no perfect collinearity, has a zero conditional mean, and is homoscedastic. Violations of these assumptions can cause issues like omitted variable bias.
Correlation research
Correlation research examines the relationships between two or more non-manipulated variables without changing any variables. It can be used to predict scores on one variable based on scores of another predictor variable. Common techniques include explanatory design to look for associations between variables and prediction design to identify predictors of outcomes. Tools to analyze correlations include scatter plots, correlation coefficients, and regression analysis.
Week 5 Lecture 14 The Chi Square Test Quite often, pat.docx
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).
Data processing
The document discusses various statistical techniques used to analyze relationships between variables, including correlation, t-tests, analysis of variance, chi-square tests, and more. It provides examples of how to apply these techniques and interpret their results. For instance, it explains how a t-test for dependent means could be used to compare the science achievement of an experimental group that received computer-aided instruction versus a control group that received traditional teaching.
Section 1 Data File DescriptionThe fictional data represents a te.docx
This document describes using dummy predictor variables in multiple regression analysis. It provides an example using hypothetical data on faculty salaries. Key points:
- Dummy variables allow inclusion of categorical predictors like gender or political party in regression by coding them numerically.
- For k categories, k-1 dummy variables are needed. This example uses gender (coded 0,1) and college (coded 1,2,3) as predictors.
- Regression and ANOVA provide equivalent information about differences in mean salaries for gender and across colleges. Dummy variable regression tests are equivalent to ANOVA comparisons.
- The document screens the salary data for violations of regression assumptions like normality before running analyses.
Commonly Used Statistics in Medical Research Part I
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
this activity is designed for you to explore the continuum of an a.docx
this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
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His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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Regression presentation
- 1. Regression SPSS Report 4 By Maryam Bolouri & Tahereh Soleimani
- 2. The main types of research questions that multiple regression can be used to address are: 1. how well a set of variables is able to predict a particular outcome? 2. which variable in a set of variables is the best predictor of an outcome? 3. whether a particular predictor variable is still able to predict an outcome when the effects of another variable are controlled
- 3. To recap: It explores the relationship between one continuous dependent variable and a number of independent variables or predictors (usually continuous). Multiple regression is based on correlation. It allows a more sophisticated exploration of the interrelationship among a set of variables. Multiple regression will provide you with information about the model as a whole (all subscales) and the relative contribution of each of the variables that make up the model (individual subscales).
- 4. Homework 5: Interested to investigate the relationship between anxiety, motivation and writing performance, a researcher conducted a study with 50 learners. Anxiety and motivation with 20 questions were measured on separate questionnaires on a 5- point Likert scale. The index for writing (out of 25) was the average of two raters of the essay written under timed conditions. Predictors: anxiety and motivation Dependent V: writing performance Relationship btw them (predictive power)
- 5. Research question and research hypothesis: How well the anxiety and motivation levels can predict writing performance? How much variance in writing performance can be explained by scores of anxiety and motivation scales? Which variables is the best predictor of writing performance? H0: there is no significant relationship with predictive power between anxiety, motivation and the dependent variable of the study (writing performance).
- 6. Step one: checking the assumptions sample size It is recommended that ‘for social science research, about 15 participants per predictor are needed for a reliable equation’. In this study there are 2 independent variables so the required sample must be larger that 30 which in this study is 50 and quite acceptable.
- 7. Step one: checking the assumptions Multicollinearity Multicollinearity exists when the independent variables are highly correlated (r=.9 and above). Singularity occurs when one independent variable is actually a combination of other independent variables. The correlation btw independent variable must be smaller that 0.7. The correlation btw independent variables and dependent one must be larger than 0.3
- 8. Correlation and coefficient tables: Correlations Writing performance anxiety motivation Pearson Correlation writingperformance 1.000 .305 -.168 anxiety .305 1.000 .227 motivation -.168 .227 1.000 Sig. (1-tailed) writingperformance . .016 .121 anxiety .016 . .057 motivation .121 .057 . N writingperformance 50 50 50 anxiety 50 50 50 motivation 50 50 50
- 9. Step one: checking the assumptions outliers, normality, linearity, homoscedastisity Multiple regression is very sensitive to outliers (very high or very low scores). Tabachnick and Fidell (2007, p. 128) define outliers as those with standardised residual values above about 3.3 (or less than –3.3). Residuals are the differences between the obtained and the predicted dependent variable (DV) scores. The residuals scatterplots allow you to check: • normality: the residuals should be normally distributed about the predicted DV scores • linearity: the residuals should have a straight-line relationship with predicted DV scores • homoscedasticity: the variance of the residuals about predicted DV scores should be the same for all predicted scores.
- 10. Step one: checking the assumptions scatter plot and normal probability plot There was no outlier.
- 11. Step two: evaluating the model how much of the variance in the dependent variable is explained by the model and in this study R square is 0.152 or explains 15per cent of the variance in writing performance. This is not a respectable result. Adjusted R square statistic ‘corrects’ this value to provide a better estimate of the true population value. As the sample of the study is fairly small it is better to include Adjusted one in the interpretation stage of the study. To assess the statistical significance of the result, it is necessary to look in the table labelled ANOVA. This tests the null hypothesis that multiple R in the population equals 0. The model in this example reaches statistical significance (Sig. = .021; this really means p<.05).
- 12. Model summary table and ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 146.967 2 73.483 4.225 .021a Residual 817.513 47 17.394 Total 964.480 49 a. Predictors: (Constant), motivation, anxiety b. Dependent Variable: writing performance Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .390a .152 .116 4.17060 a. Predictors: (Constant), motivation, anxiety b. Dependent Variable: writing performance
- 13. Step three: evaluating each of the independent variables we are interested in comparing the contribution of each independent variable; therefore we will use the beta values. In this case the largest beta coefficient is 0.36, which is for Anxiety. This means that this variable makes the strongest unique contribution to explaining the dependent variable, when the variance explained by all other variables in the model is controlled for. The Beta value for Motivation was slightly lower (–0.25), indicating that it made less of a unique contribution. In this case, just anxiety made a unique, and statistically significant, contribution to the prediction of writing performance, yet it turned out that motivation didn’t make a respectable contribution.
- 14. Step three: evaluating each of the independent variables In this example, Anxiety has a part correlation coefficient of 0.35. If we square this (multiply it by itself) we get .12, indicating that Anxiety uniquely explains 12 per cent of the variance in writing performance. For the motivation the value is –.24, which squared gives us .05, indicating a unique contribution of 5 per cent to the explanation of variance in writing performance. All in all these 2 predictors 12+5=17% of variance in dependant V can be explained. The total R square explained 15% of variance in scores. It is because of the violation of the required assumptions.
- 15. Coefficient table Coefficientsa Model Unstandardized Coefficients Standardiz ed Coefficient s t Sig. 95% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero- order Partial Part Toleran ce VIF (Constant) 19.158 2.326 8.236 .000 14.478 23.837 anxiety .056 .021 .362 2.623 .012 .013 .098 .305 .357 .352 .949 1.054 motivation -.058 .032 -.250 -1.815 .076 -.122 .006 -.168 -.256 -.244 .949 1.054 . Dependent Variable: writingperformance
- 16. How to report and present the outcome Our model, which includes anxiety and motivation, explains 15 percent of the variance in writing performance(Question 1). Of these two variables, anxiety makes the largest unique contribution (beta = .36), and motivation contribution was not statistically significant (Question 2).
- 17. Thank you You are all great.