The document discusses key concepts in sampling and measurement for research including sample design techniques, types of sampling, measurement scales, sources of error in measurement, tests of sound measurement, techniques for developing measurement tools, scaling, scaling techniques, and scale construction. It provides an overview of probability and non-probability sampling, nominal to ratio measurement scales, factors that can influence measurement errors, validity and reliability testing, and common methods for scaling responses and constructing scales.
Mba2216 week 11 data analysis part 03 appendixStephen Ong
Multivariate analysis involves simultaneously analyzing multiple variables to understand relationships. This document discusses key concepts in multivariate analysis including:
1. Defining multivariate analysis and when it is appropriate to use.
2. Describing specific techniques like multiple regression, discriminant analysis, logistic regression, MANOVA, canonical correlation analysis, conjoint analysis, factor analysis, cluster analysis, multidimensional scaling, and correspondence analysis.
3. Providing guidelines for selecting the appropriate technique based on the measurement scales and relationship between variables.
It also covers important considerations like measurement error, statistical power, and a structured approach to multivariate model building.
Research Methodology: Questionnaire, Sampling, Data Preparationamitsethi21985
As per PTU's MBA Syllabus, Unit No. 2: Sources Of Data: Primary And Secondary; Data Collection Methods; Questionnaire Designing: Construction, Types And Developing A Good Questionnaire. Sampling Design and Techniques, Scaling Techniques, Meaning, Types, Data Processing Operations, Editing, Coding, Classification, Tabulation. Research Proposal/Synopsis Writing. Practical Framework
SPSS (Statistical Package for the Social Sciences) is software used for data analysis. It can process questionnaires, report data in tables and graphs, and analyze means, chi-squares, regression, and more. Originally its own company, SPSS is now owned by IBM and integrated into their software portfolio. The document provides an overview of using SPSS, including entering data from questionnaires, different question/response formats, and descriptive statistical analysis functions in SPSS like frequencies, cross-tabs, and graphs.
This document provides information about sampling, including definitions, reasons for using sampling, what constitutes a good sample, and different types of sampling methods. It defines sampling as selecting a portion of a larger group to make inferences about the whole group in a way that is accurate, economical, reliable, and fast. A good sample is both valid and precise. It then outlines the key steps in sampling design: identifying the target population and parameters of interest, developing a sampling frame, choosing an appropriate sampling method (probability or non-probability), and determining sample size. The document proceeds to classify and describe different probability sampling methods, including simple random sampling, systematic sampling, stratified random sampling, and cluster sampling.
Formal research methods such as surveys, experiments, and content analysis can be used to understand audiences, measure the effectiveness of media messages, and describe the impact of media. Surveys involve collecting data from a sample using methods like internet, telephone, mail, or interviews. Experiments manipulate variables to determine their effects in controlled conditions. Content analysis objectively converts communications content into quantitative data to analyze themes and issues. Each method has advantages and limitations for different research purposes.
This document discusses concepts related to evaluating test items and assessments, including:
- Item difficulty is measured by the percentage of students who answered the item correctly, with higher percentages indicating an easier item. For standardized tests, items should have around 50% difficulty to maximize score spread.
- Item discrimination compares performance between high- and low-scoring students on an item, with positive discrimination indicating more high-scorers answered correctly. This is important for standardized tests but less so for classroom assessments.
- Classroom assessments can have higher difficulty (easier) items than standardized tests since they focus on mastery rather than differentiation of students.
This presentation of mine focusses on sampling with appropriate pictures and examples. It may be helpful for the faculties as well as fro the student who want to understand the concept of sampling appropraitely. layman language is used in this so that almost everyone can go through it.
The document discusses key concepts in sampling and measurement for research including sample design techniques, types of sampling, measurement scales, sources of error in measurement, tests of sound measurement, techniques for developing measurement tools, scaling, scaling techniques, and scale construction. It provides an overview of probability and non-probability sampling, nominal to ratio measurement scales, factors that can influence measurement errors, validity and reliability testing, and common methods for scaling responses and constructing scales.
Mba2216 week 11 data analysis part 03 appendixStephen Ong
Multivariate analysis involves simultaneously analyzing multiple variables to understand relationships. This document discusses key concepts in multivariate analysis including:
1. Defining multivariate analysis and when it is appropriate to use.
2. Describing specific techniques like multiple regression, discriminant analysis, logistic regression, MANOVA, canonical correlation analysis, conjoint analysis, factor analysis, cluster analysis, multidimensional scaling, and correspondence analysis.
3. Providing guidelines for selecting the appropriate technique based on the measurement scales and relationship between variables.
It also covers important considerations like measurement error, statistical power, and a structured approach to multivariate model building.
Research Methodology: Questionnaire, Sampling, Data Preparationamitsethi21985
As per PTU's MBA Syllabus, Unit No. 2: Sources Of Data: Primary And Secondary; Data Collection Methods; Questionnaire Designing: Construction, Types And Developing A Good Questionnaire. Sampling Design and Techniques, Scaling Techniques, Meaning, Types, Data Processing Operations, Editing, Coding, Classification, Tabulation. Research Proposal/Synopsis Writing. Practical Framework
SPSS (Statistical Package for the Social Sciences) is software used for data analysis. It can process questionnaires, report data in tables and graphs, and analyze means, chi-squares, regression, and more. Originally its own company, SPSS is now owned by IBM and integrated into their software portfolio. The document provides an overview of using SPSS, including entering data from questionnaires, different question/response formats, and descriptive statistical analysis functions in SPSS like frequencies, cross-tabs, and graphs.
This document provides information about sampling, including definitions, reasons for using sampling, what constitutes a good sample, and different types of sampling methods. It defines sampling as selecting a portion of a larger group to make inferences about the whole group in a way that is accurate, economical, reliable, and fast. A good sample is both valid and precise. It then outlines the key steps in sampling design: identifying the target population and parameters of interest, developing a sampling frame, choosing an appropriate sampling method (probability or non-probability), and determining sample size. The document proceeds to classify and describe different probability sampling methods, including simple random sampling, systematic sampling, stratified random sampling, and cluster sampling.
Formal research methods such as surveys, experiments, and content analysis can be used to understand audiences, measure the effectiveness of media messages, and describe the impact of media. Surveys involve collecting data from a sample using methods like internet, telephone, mail, or interviews. Experiments manipulate variables to determine their effects in controlled conditions. Content analysis objectively converts communications content into quantitative data to analyze themes and issues. Each method has advantages and limitations for different research purposes.
This document discusses concepts related to evaluating test items and assessments, including:
- Item difficulty is measured by the percentage of students who answered the item correctly, with higher percentages indicating an easier item. For standardized tests, items should have around 50% difficulty to maximize score spread.
- Item discrimination compares performance between high- and low-scoring students on an item, with positive discrimination indicating more high-scorers answered correctly. This is important for standardized tests but less so for classroom assessments.
- Classroom assessments can have higher difficulty (easier) items than standardized tests since they focus on mastery rather than differentiation of students.
This presentation of mine focusses on sampling with appropriate pictures and examples. It may be helpful for the faculties as well as fro the student who want to understand the concept of sampling appropraitely. layman language is used in this so that almost everyone can go through it.
This document provides an overview of starting SPSS, including installing the software, opening SPSS, the main SPSS windows, entering and saving data, and conducting statistical analysis. It discusses the SPSS data editor interface, defining variables, entering data by copying from Excel or directly into SPSS, and saving SPSS files. It also briefly mentions bibliographic citations for SPSS.
This chapter introduces foundational statistical concepts. It discusses why managers need statistics to properly present information, draw conclusions from samples, and improve processes. It also covers the difference between descriptive and inferential statistics, important definitions like population and parameter, why data is needed, sources of data, survey design, sampling methods, and types of survey errors.
This course introduces students to statistical techniques for business decision making. Students will learn to analyze and present business data using appropriate software and statistical tools. Topics covered include descriptive statistics, probability, sampling, hypothesis testing, regression analysis, and comparing means of two and three groups. Assessments include a midterm, project, and final exam. Statistics are used to organize and analyze information to make it more easily understood, allowing judgments about the world. Descriptive statistics describe characteristics of data sets, while inferential statistics allow inferences about populations from data samples.
The document provides details about conducting an item analysis of a test. It discusses the key steps in item analysis which include: 1) arranging student answer sheets in order of performance and dividing them into high and low groups, 2) calculating the difficulty level and discrimination power of each item, and 3) using the results to select items to keep, modify, or eliminate from the test. The item analysis helps evaluate the quality of individual test items and identify areas for improving the test and future item writing.
The document outlines 31 learning outcomes for data analysis. It discusses key concepts in data analysis including editing raw data, coding qualitative responses, constructing data files, and descriptive statistical analysis. The goal is for students to understand processes for preparing and summarizing data including editing, coding, and descriptive statistics.
This document discusses quantitative data analysis techniques including univariate analysis of single variables, bivariate analysis of two variables, and multivariate analysis of more than two variables. It describes developing code categories, constructing a codebook, entering data, presenting univariate data, making subgroup comparisons, and constructing bivariate tables to analyze relationships between variables.
You begin every statistical analysis by identifying the source of the data.
Among the important sources of data are published sources, experiments,
and surveys.
This document defines key concepts related to population and sampling in research methods. It discusses the differences between populations and samples, and the importance of carefully defining both. It also covers probability and non-probability sampling techniques like simple random sampling, stratified sampling, cluster sampling, and quota sampling. The advantages and disadvantages of different sampling methods are presented.
This document discusses various multivariate analysis techniques. It provides an overview of multidimensional scaling (MDS) which maps distances between observations in a high dimensional space to a lower dimensional space. It also discusses data envelopment analysis (DEA) which uses linear programming to evaluate the efficiency of decision making units relative to a efficient frontier. Finally, it notes some conditions and considerations for implementing DEA, such as having homogenous decision making units and a sufficient sample size.
EXAMINING DISTRACTORS AND EFFECTIVENESS
Distractors are the multiple choice response options that are not the correct answer. They are plausible but incorrect options that are often developed based upon students’ common misconceptions or miscalculations. Item analysis software typically indicates the percentage of students who selected each option, distractors and key.
educ 11
This document summarizes a discussion between Susan Athey and Guido Imbens on the relationship between machine learning and causal inference. It notes that while machine learning excels at prediction problems using large datasets, it has weaknesses when it comes to causal questions. Econometrics and statistics literature focuses more on formal theories of causality. The document proposes combining the strengths of both fields by developing machine learning methods that can estimate causal effects, accounting for issues like endogeneity and treatment effect heterogeneity. It outlines some open problems and directions for future research at the intersection of these fields.
This document discusses item analysis, which is a statistical technique used to evaluate test items. It has two types: qualitative, which involves expert review; and quantitative, which uses numerical indicators. Item analysis is important as it helps select appropriate items, determine item difficulty levels, provide discrimination between more and less capable examinees, and inform test improvements. Item difficulty is calculated as the percentage answering correctly, and should typically be between 25-75%. Item discrimination indicates how well items differentiate high and low scoring groups, and is calculated using an index formula. Both difficulty and discrimination are considered when selecting quality test items.
This document discusses different sampling techniques used in market research. It describes probability-based sampling methods like random sampling, stratified sampling, and cluster sampling. It also describes non-probability sampling techniques like quota sampling and convenience sampling. For each method, it outlines their advantages and disadvantages. It provides details on how to plan a sample survey, including defining objectives and population, selecting a sampling method, determining sample size, and collecting data.
This document provides information about getting fully solved assignments from an assignment help service. It lists the contact email and phone number and specifies the programs and subjects they can provide assignments for, including research methodology, management subjects for various semesters, and more. It also provides an example of a research methodology assignment question that is answered in detail.
This document defines key concepts related to population and sampling in research methods. It discusses the difference between populations and samples, and describes different sampling techniques including probability sampling methods like simple random sampling, stratified sampling, cluster sampling, and non-probability sampling techniques like convenience sampling, purposive sampling, snowball sampling, and quota sampling. The advantages and disadvantages of different sampling methods are also outlined.
This document provides an overview of basic statistical concepts. It discusses that statistics involves collecting, organizing, analyzing, and interpreting quantitative data. There are two main divisions of statistics: descriptive statistics, which are used to summarize and describe basic features of data, and inferential statistics, which are used to make inferences about populations based on samples. The document also covers topics such as populations and samples, levels of measurement, data collection methods, sampling techniques, and ways to present statistical data through tables, graphs, and other visual formats.
This document discusses item analysis, which examines student responses to test questions. There are two types: quantitative, which uses statistics like difficulty and discrimination indices, and qualitative, which involves expert review. Difficulty index measures the proportion of students answering correctly, ranging from very difficult to very easy. Discrimination index measures an item's ability to distinguish high-scoring from low-scoring students. Qualitative analysis involves experts proofreading tests for issues like ambiguity before administration.
The document discusses item analysis which is used to evaluate the quality and performance of test items. It addresses several key aspects of item analysis including calculating the index of difficulty and discrimination for each item, examining distractors, and using the results to identify issues and determine if items should be retained, modified, or discarded. The purpose is to select the best items for the final test form and identify areas for improvement.
The document discusses how to add a t-test button to the Excel main menu bar to perform t-tests. It provides steps to add the Analysis ToolPak add-in, which will make the t-test options available. It then outlines the steps to run independent and paired t-tests, including selecting the data, choosing the appropriate t-test, and interpreting the output values such as the means, correlation, and t-value.
Level of Measurement, Frequency Distribution,Stem & Leaf Qasim Raza
This document discusses multivariate data analysis and techniques. It begins by defining qualitative and quantitative data, and the different levels of measurement - nominal, ordinal, interval, and ratio. It then discusses frequency distributions, stem and leaf plots, and demonstrates their use in SPSS. Finally, it defines multivariate data analysis as involving two or more variables, and provides examples of multivariate techniques such as multiple regression, discriminant analysis, MANOVA, and their appropriate uses depending on the level of measurement of the variables.
This document discusses key concepts related to sampling theory and measurement in research studies. It defines important sampling terms like population, sampling criteria, sampling methods, sampling error and bias. It also covers levels of measurement, reliability, validity and various measurement strategies like physiological measures, observations, interviews, questionnaires and scales. Finally, it provides an overview of statistical analysis techniques including descriptive statistics, inferential statistics, the normal curve and common tests like t-tests, ANOVA, and regression analysis.
This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.
This document provides an overview of starting SPSS, including installing the software, opening SPSS, the main SPSS windows, entering and saving data, and conducting statistical analysis. It discusses the SPSS data editor interface, defining variables, entering data by copying from Excel or directly into SPSS, and saving SPSS files. It also briefly mentions bibliographic citations for SPSS.
This chapter introduces foundational statistical concepts. It discusses why managers need statistics to properly present information, draw conclusions from samples, and improve processes. It also covers the difference between descriptive and inferential statistics, important definitions like population and parameter, why data is needed, sources of data, survey design, sampling methods, and types of survey errors.
This course introduces students to statistical techniques for business decision making. Students will learn to analyze and present business data using appropriate software and statistical tools. Topics covered include descriptive statistics, probability, sampling, hypothesis testing, regression analysis, and comparing means of two and three groups. Assessments include a midterm, project, and final exam. Statistics are used to organize and analyze information to make it more easily understood, allowing judgments about the world. Descriptive statistics describe characteristics of data sets, while inferential statistics allow inferences about populations from data samples.
The document provides details about conducting an item analysis of a test. It discusses the key steps in item analysis which include: 1) arranging student answer sheets in order of performance and dividing them into high and low groups, 2) calculating the difficulty level and discrimination power of each item, and 3) using the results to select items to keep, modify, or eliminate from the test. The item analysis helps evaluate the quality of individual test items and identify areas for improving the test and future item writing.
The document outlines 31 learning outcomes for data analysis. It discusses key concepts in data analysis including editing raw data, coding qualitative responses, constructing data files, and descriptive statistical analysis. The goal is for students to understand processes for preparing and summarizing data including editing, coding, and descriptive statistics.
This document discusses quantitative data analysis techniques including univariate analysis of single variables, bivariate analysis of two variables, and multivariate analysis of more than two variables. It describes developing code categories, constructing a codebook, entering data, presenting univariate data, making subgroup comparisons, and constructing bivariate tables to analyze relationships between variables.
You begin every statistical analysis by identifying the source of the data.
Among the important sources of data are published sources, experiments,
and surveys.
This document defines key concepts related to population and sampling in research methods. It discusses the differences between populations and samples, and the importance of carefully defining both. It also covers probability and non-probability sampling techniques like simple random sampling, stratified sampling, cluster sampling, and quota sampling. The advantages and disadvantages of different sampling methods are presented.
This document discusses various multivariate analysis techniques. It provides an overview of multidimensional scaling (MDS) which maps distances between observations in a high dimensional space to a lower dimensional space. It also discusses data envelopment analysis (DEA) which uses linear programming to evaluate the efficiency of decision making units relative to a efficient frontier. Finally, it notes some conditions and considerations for implementing DEA, such as having homogenous decision making units and a sufficient sample size.
EXAMINING DISTRACTORS AND EFFECTIVENESS
Distractors are the multiple choice response options that are not the correct answer. They are plausible but incorrect options that are often developed based upon students’ common misconceptions or miscalculations. Item analysis software typically indicates the percentage of students who selected each option, distractors and key.
educ 11
This document summarizes a discussion between Susan Athey and Guido Imbens on the relationship between machine learning and causal inference. It notes that while machine learning excels at prediction problems using large datasets, it has weaknesses when it comes to causal questions. Econometrics and statistics literature focuses more on formal theories of causality. The document proposes combining the strengths of both fields by developing machine learning methods that can estimate causal effects, accounting for issues like endogeneity and treatment effect heterogeneity. It outlines some open problems and directions for future research at the intersection of these fields.
This document discusses item analysis, which is a statistical technique used to evaluate test items. It has two types: qualitative, which involves expert review; and quantitative, which uses numerical indicators. Item analysis is important as it helps select appropriate items, determine item difficulty levels, provide discrimination between more and less capable examinees, and inform test improvements. Item difficulty is calculated as the percentage answering correctly, and should typically be between 25-75%. Item discrimination indicates how well items differentiate high and low scoring groups, and is calculated using an index formula. Both difficulty and discrimination are considered when selecting quality test items.
This document discusses different sampling techniques used in market research. It describes probability-based sampling methods like random sampling, stratified sampling, and cluster sampling. It also describes non-probability sampling techniques like quota sampling and convenience sampling. For each method, it outlines their advantages and disadvantages. It provides details on how to plan a sample survey, including defining objectives and population, selecting a sampling method, determining sample size, and collecting data.
This document provides information about getting fully solved assignments from an assignment help service. It lists the contact email and phone number and specifies the programs and subjects they can provide assignments for, including research methodology, management subjects for various semesters, and more. It also provides an example of a research methodology assignment question that is answered in detail.
This document defines key concepts related to population and sampling in research methods. It discusses the difference between populations and samples, and describes different sampling techniques including probability sampling methods like simple random sampling, stratified sampling, cluster sampling, and non-probability sampling techniques like convenience sampling, purposive sampling, snowball sampling, and quota sampling. The advantages and disadvantages of different sampling methods are also outlined.
This document provides an overview of basic statistical concepts. It discusses that statistics involves collecting, organizing, analyzing, and interpreting quantitative data. There are two main divisions of statistics: descriptive statistics, which are used to summarize and describe basic features of data, and inferential statistics, which are used to make inferences about populations based on samples. The document also covers topics such as populations and samples, levels of measurement, data collection methods, sampling techniques, and ways to present statistical data through tables, graphs, and other visual formats.
This document discusses item analysis, which examines student responses to test questions. There are two types: quantitative, which uses statistics like difficulty and discrimination indices, and qualitative, which involves expert review. Difficulty index measures the proportion of students answering correctly, ranging from very difficult to very easy. Discrimination index measures an item's ability to distinguish high-scoring from low-scoring students. Qualitative analysis involves experts proofreading tests for issues like ambiguity before administration.
The document discusses item analysis which is used to evaluate the quality and performance of test items. It addresses several key aspects of item analysis including calculating the index of difficulty and discrimination for each item, examining distractors, and using the results to identify issues and determine if items should be retained, modified, or discarded. The purpose is to select the best items for the final test form and identify areas for improvement.
The document discusses how to add a t-test button to the Excel main menu bar to perform t-tests. It provides steps to add the Analysis ToolPak add-in, which will make the t-test options available. It then outlines the steps to run independent and paired t-tests, including selecting the data, choosing the appropriate t-test, and interpreting the output values such as the means, correlation, and t-value.
Level of Measurement, Frequency Distribution,Stem & Leaf Qasim Raza
This document discusses multivariate data analysis and techniques. It begins by defining qualitative and quantitative data, and the different levels of measurement - nominal, ordinal, interval, and ratio. It then discusses frequency distributions, stem and leaf plots, and demonstrates their use in SPSS. Finally, it defines multivariate data analysis as involving two or more variables, and provides examples of multivariate techniques such as multiple regression, discriminant analysis, MANOVA, and their appropriate uses depending on the level of measurement of the variables.
This document discusses key concepts related to sampling theory and measurement in research studies. It defines important sampling terms like population, sampling criteria, sampling methods, sampling error and bias. It also covers levels of measurement, reliability, validity and various measurement strategies like physiological measures, observations, interviews, questionnaires and scales. Finally, it provides an overview of statistical analysis techniques including descriptive statistics, inferential statistics, the normal curve and common tests like t-tests, ANOVA, and regression analysis.
This document provides an overview of different types of statistical tests used for data analysis and interpretation. It discusses scales of measurement, parametric vs nonparametric tests, formulating hypotheses, types of statistical errors, establishing decision rules, and choosing the appropriate statistical test based on the number and types of variables. Key statistical tests covered include t-tests, ANOVA, chi-square tests, and correlations. Examples are provided to illustrate how to interpret and report the results of these common statistical analyses.
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 an overview of a session on basics of writing research papers. It discusses qualitative and quantitative data analysis, including descriptive statistics, scales of data measurement, and statistical tests like chi-square, correlation, regression, t-test, and ANOVA. The key takeaways are understanding different data types, right tests for data combinations, setting hypotheses, and writing interpretations. Examples of analyzing literature reviews, consumer perceptions, and relationships between variables are also presented.
T test, independant sample, paired sample and anovaQasim Raza
The document discusses various statistical analyses that can be performed in SPSS, including t-tests, ANOVA, and post-hoc tests. It provides details on one-sample t-tests, independent t-tests, paired t-tests, one-way ANOVA tests, and evaluating assumptions like normality. Examples are given on how to conduct these tests in SPSS and how to interpret the output. Guidance is provided on follow-up post-hoc tests that can be used after ANOVA to examine differences between specific groups.
T test, Student’s t Test, Key Takeaways, Uses of t-test / Application , Type of t-test, Type of t-test Cont.., One-tailed or two-tailed t-test, Which t-test to Use, t-test Formula, The t-score, Understanding P-values, Degrees of Freedom, How is the t-distribution table used, Example, Example Cont.., Different t-test Formulae, Different t-test Formulae Cont.., Reference.
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
This document provides an overview of using SPSS to conduct descriptive and inferential statistical analyses. It discusses entering data into SPSS, conducting descriptive analyses, and using SPSS to perform inferential tests including chi-squared tests, correlations, and t-tests. An example research study is described that aims to determine if a particular teaching method leads to higher achievement and satisfaction for visual learners. The document outlines how SPSS can be used to establish causality, describe the sample, test for independence between variables, measure correlations, and test for significant differences in means between groups.
(Individuals With Disabilities Act Transformation Over the Years)DSilvaGraf83
(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
ANOVA
ANOVA
• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
One-Way ANOVA Excel Output
Treatme
(Individuals With Disabilities Act Transformation Over the Years)DMoseStaton39
(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
ANOVA
ANOVA
• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
One-Way ANOVA Excel Output
Treatme
This document discusses key concepts related to sampling and measurement in research. It covers topics such as population and sampling criteria when selecting a sample. It also discusses levels of measurement, reliability, validity, and different measurement strategies like interviews, questionnaires, and scales. Finally, it provides an overview of statistical analysis, including descriptive statistics, levels of measurement, and common statistical tests. The overall purpose is to introduce fundamental concepts for designing research studies and analyzing quantitative data.
t test for statistics 1st sem mba sylabusSoujanyaLk1
The document discusses the t-test, a statistical analysis developed by William Gosset under the pseudonym "Student" in 1908. The t-test can be used to determine if there are differences between the means of two groups and requires data on the mean difference, standard deviations, and sample sizes of each group. Different types of t-tests include paired t-tests for within-subjects designs, independent t-tests for between-subjects designs, and one-sample t-tests for comparing a sample to a standard value.
The document provides information about conducting an independent samples t-test using SPSS. It explains that an independent t-test assesses whether the means of two independent groups differ significantly. The document then walks through an example, showing how to enter data into SPSS and interpret the output, which includes descriptive statistics, tests of assumptions, and the significance test results. It emphasizes reporting the t-value, degrees of freedom, and p-value to indicate whether the group means differ significantly.
This document discusses methods for testing differences between groups, including t-tests, z-tests, and ANOVA. It provides examples of how to test for significant differences between percentages and means of two independent groups. Key points covered include determining if a difference is statistically significant, using ANOVA to compare means of three or more groups, and properly reporting difference test results to clients.
U3 IP.sav
MKTG420_U3IP.doc
Unit 3 Individual Project 1
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American Intercontinental University
MACROBUTTON DoFieldClick Type your Paper Title
Project Type: MKTG420 Unit 3 Individual Project
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Abstract
This is a single paragraph, no indentation is required. The next page will be an abstract; “a brief, comprehensive summary of the contents of the article; it allows the readers to survey the contents of an article quickly” (Publication Manual, 2010). The length of this abstract should be 35-50 words (2-3 sentences). NOTE: the abstract must be on page 2 and the body of the paper will begin on page 3.
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Introduction
Remember to always indent the first line of a paragraph (use the tab key). The introduction should be short (2-3 sentences). The margins, font size, spacing, and font type (italics or plain) are set in APA format. While you may change the names of the headings and subheadings, do not change the font.
Part 1: Research background on the scales
Introduce the concept and be sure to indent the first line of the paragraph. Provide background on each of the 4 scales (assurance, empathy, reliability and responsiveness), not limited to a simple definition but as a measurement that aids marketers. Discuss how the questions in the survey are transformed into "scales" (also called "factors"). In other studies using SERVQUAL, how many and what types of respondents were included? Part 1 of the Individual Project should be 1 page in length. Be sure to cite your resources.
Part 1: Concept of Scales/Factors
Introduce the concept and be sure to indent the first line of the paragraph.
Part 1: SERVQUAL Samples
Introduce the concept and be sure to indent the first line of the paragraph.
Part 2: (Full-Text Research) Service Quality and Segmentation
Introduce the concept and be sure to indent the first line of the paragraph. Connect information from at least 3 articles. Do not write and overview or critique of the articles. Synthesize and connect the information contained to develop a solid understanding of how service quality and segmentation are related. Part 2 of the Individual Project should be 2 pages in length and should be predominately from at least three articles in AIU's full-text databases. Be sure to cite your resources.
Part 3: Null/Hypo 1, ANOVA, Decision
Attached is a small set of data that has been collected from brand loyal customers of Store 1 and Store 2. Write out a Null hypothesis and an alternate hypothesis for each of the 4 aspects of service quality that are include in the analysis (assurance, empathy, reliability and responsiveness) to see if there is a difference between stores. Run 4 ANOVAs to test the Null hypotheses. State the decision for each of the tests.
Part 3: Null/Hypo 2, ANOVA, Decision
Write out a Null hypothesis and an alternate hypothesis ...
This document provides an overview of analysis of variance (ANOVA) and experimental design. It discusses parametric and nonparametric test procedures, with parametric procedures making stronger assumptions about the data like normal distributions. One-way and two-way ANOVA are introduced for analyzing experiments with one or two factors. Key concepts in ANOVA are partitioning total variation into variation due to treatments and random error, and using F-ratios to test if treatment means are significantly different. Examples show how to set up ANOVA in StatGraphics and evaluate the significance of main effects and interactions.
This document provides an overview of analysis of variance (ANOVA) and experimental design. It discusses parametric and nonparametric test procedures, with parametric procedures making stronger assumptions about the data like normal distributions. One-way and two-way ANOVA are introduced for analyzing experiments with one or two factors. Key concepts in ANOVA are partitioning total variation into variation due to treatments and random error, and using F-ratios to test if treatment means are significantly different. Examples are provided to illustrate hypothesis testing and interpreting ANOVA results from StatGraphics software.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. Statistical Tests for Differences
Choose the correct statistic test to compare
means
Area of Applica
tion
Level Sca
ling
Subgroups
Test Example
Hypotheses
about
frequency
Nominal All Chi-square
Do customer industry types
differ by company size ?
Hypotheses
about means
Metric
(Interval
or
ratio)
One
One Sample
T-test
Is the purchase frequency
different from 1.5?
Two
Independent
Samples T-test
Is the purchase frequency
greater for email promotion
responders than that for non-responders?
Three or more
One-way
ANOVA
Is the purchase frequency
different by company size?
4. One Sample T-test
You can choose your own file
by uploading it to the cloud.
Is the overall rating significantly
different than 4?
5. One Sample T-test
3 STEPS! Easy to apply!
Then manually enter the
test value and choose th
e sided test.
First select the test variable
in your data file
Now click on ‘Run’!
6. One Sample t-test
Conclusion: Rating is not different
from 4 on average.
14. ANOVA
P value 0.32 (>0.05)
Conclusion: Rating is indifferent
across ethnicity.
P Value: Exact Probability of getting
a computed test statistic that is due
to chance. The smaller the p value,
the smaller the probability that the
observed result occurred by chance
16. Cross-tab (Chi-square test)
• Cross-tab is a frequency table of two
or three variables
• Used to examine association between
two or 3 variables (usually 2)
• H0: there is a relation between variable X
and variable Y
• Variables take a limited number of
values, for example:
Consumers: gender, ethnicity
Business: industry, company size