The document discusses statistical analysis and statistical software packages like SPSS. It explains concepts like hypothesis, level of significance, statistical tests like ANOVA and t-tests. It provides examples of hypothesis statements, how to conduct one-way and two-way ANOVA, and the steps involved in statistical analysis and research proposals. Factor analysis is introduced as a statistical method to describe variability among observed correlated variables in terms of fewer unobserved factors.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
The document provides an overview of analysis of variance (ANOVA). It defines ANOVA and discusses its key concepts, including how it was developed by Ronald Fisher. It also covers one-way and two-way ANOVA, describing their techniques and providing examples. The uses, advantages and limitations of ANOVA are outlined.
This document provides an overview of analysis of variance (ANOVA). It introduces ANOVA and its key concepts, including its development by Ronald Fisher. It defines ANOVA and distinguishes between one-way and two-way ANOVA. It outlines the assumptions, techniques, and examples of how to perform one-way and two-way ANOVA. It also discusses the uses, advantages, and limitations of ANOVA for analyzing differences between multiple means and factors.
ANOVA is a statistical technique used to compare the means of three or more groups. It can test if population means are equal or if some are different. The document outlines the steps in ANOVA including describing data, stating hypotheses, calculating test statistics, and making conclusions. It also discusses one-way and two-way ANOVA designs, comparing means between multiple groups while controlling for Type I error, and the calculations involved including sums of squares, degrees of freedom, and F-ratios.
This document provides an overview of experimental design and analysis of variance (ANOVA). It defines key terms like independent and dependent variables, experimental units, treatments, and blocks. It explains different types of experimental designs like completely randomized designs, randomized block designs, and factorial experiments. It also covers ANOVA computations and assumptions for one-way and randomized block ANOVA models. Multiple comparison procedures like Tukey's HSD are introduced to identify differences between specific treatment means. Examples are provided to demonstrate applications of one-way and randomized block ANOVA.
Statistics for Anaesthesiologists covers basic to intermediate level statistics for researchers especially commonly used study designs or tests in Anaesthesiology research.
The document discusses analysis of variance (ANOVA) which is used to compare the means of three or more groups. It explains that ANOVA avoids the problems of multiple t-tests by providing an omnibus test of differences between groups. The key steps of ANOVA are outlined, including partitioning variation between and within groups to calculate an F-ratio. A large F value indicates more difference between groups than expected by chance alone.
This document discusses analysis of variance (ANOVA), a statistical technique used to compare differences between group means. It provides examples of when ANOVA would be used, such as comparing sales across store locations or production levels across work shifts. The key assumptions of ANOVA are introduced, including normal distributions and equal variances between groups. Finally, the document outlines the basic steps in an ANOVA, including partitioning total variation into between- and within-group variations to test for statistically significant differences between means.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
The document provides an overview of analysis of variance (ANOVA). It defines ANOVA and discusses its key concepts, including how it was developed by Ronald Fisher. It also covers one-way and two-way ANOVA, describing their techniques and providing examples. The uses, advantages and limitations of ANOVA are outlined.
This document provides an overview of analysis of variance (ANOVA). It introduces ANOVA and its key concepts, including its development by Ronald Fisher. It defines ANOVA and distinguishes between one-way and two-way ANOVA. It outlines the assumptions, techniques, and examples of how to perform one-way and two-way ANOVA. It also discusses the uses, advantages, and limitations of ANOVA for analyzing differences between multiple means and factors.
ANOVA is a statistical technique used to compare the means of three or more groups. It can test if population means are equal or if some are different. The document outlines the steps in ANOVA including describing data, stating hypotheses, calculating test statistics, and making conclusions. It also discusses one-way and two-way ANOVA designs, comparing means between multiple groups while controlling for Type I error, and the calculations involved including sums of squares, degrees of freedom, and F-ratios.
This document provides an overview of experimental design and analysis of variance (ANOVA). It defines key terms like independent and dependent variables, experimental units, treatments, and blocks. It explains different types of experimental designs like completely randomized designs, randomized block designs, and factorial experiments. It also covers ANOVA computations and assumptions for one-way and randomized block ANOVA models. Multiple comparison procedures like Tukey's HSD are introduced to identify differences between specific treatment means. Examples are provided to demonstrate applications of one-way and randomized block ANOVA.
Statistics for Anaesthesiologists covers basic to intermediate level statistics for researchers especially commonly used study designs or tests in Anaesthesiology research.
The document discusses analysis of variance (ANOVA) which is used to compare the means of three or more groups. It explains that ANOVA avoids the problems of multiple t-tests by providing an omnibus test of differences between groups. The key steps of ANOVA are outlined, including partitioning variation between and within groups to calculate an F-ratio. A large F value indicates more difference between groups than expected by chance alone.
This document discusses analysis of variance (ANOVA), a statistical technique used to compare differences between group means. It provides examples of when ANOVA would be used, such as comparing sales across store locations or production levels across work shifts. The key assumptions of ANOVA are introduced, including normal distributions and equal variances between groups. Finally, the document outlines the basic steps in an ANOVA, including partitioning total variation into between- and within-group variations to test for statistically significant differences between means.
The document discusses various parametric statistical tests including t-tests, ANOVA, ANCOVA, and MANOVA. It provides definitions and assumptions for parametric tests and explains how they can be used to analyze quantitative data that follows a normal distribution. Specific parametric tests covered in detail include the independent samples t-test, paired t-test, one-way ANOVA, two-way ANOVA, and ANCOVA. Examples are provided to illustrate how each test is conducted and how results are interpreted.
The document provides an overview of analysis of variance (ANOVA), including what it is, how it works, key terminology, and the steps to conduct one-way and two-way ANOVA tests. ANOVA is a statistical technique used to test if there are significant differences between the means of two or more groups. It compares the variation within groups to the variation between groups to determine if observed differences are due to chance. The document outlines the null and alternative hypotheses, calculations for sums of squares, degrees of freedom, F-statistics, and how to interpret the results against critical values from the F-distribution table.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
The document provides information on statistical techniques for comparing means between groups, including t-tests, analysis of variance (ANOVA), and their assumptions and applications. T-tests are used to compare two groups, while ANOVA allows comparison of three or more groups and controls for increased Type I error rates. Steps for conducting t-tests, ANOVA, and post-hoc tests using SPSS are outlined along with examples and interpretations.
The document discusses statistical methods for comparing means between groups, including t-tests and analysis of variance (ANOVA). It provides information on different types of t-tests (one sample, independent samples, and paired samples t-tests), assumptions of t-tests, and how to perform t-tests in SPSS. It also covers one-way ANOVA, including its assumptions, components of variation, properties of the F-test, and how to run a one-way ANOVA in SPSS. Examples are provided for each statistical test.
(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
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
This document discusses analysis of variance (ANOVA), which is a statistical technique used to compare three or more population means. It begins by introducing the assumptions of ANOVA, including that populations are normally distributed and have equal variances. It then explains how ANOVA decomposes total variation into between-sample and within-sample variations. The ratio of between-sample to within-sample variation, known as the F-ratio, is used to test if population means are equal. One-way and two-way classifications of ANOVA are described.
This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.
This document provides an overview of one-way ANOVA, including its assumptions, steps, and an example. One-way ANOVA tests whether the means of three or more independent groups are significantly different. It compares the variance between sample means to the variance within samples using an F-statistic. If the F-statistic exceeds a critical value, then at least one group mean is significantly different from the others. Post-hoc tests may then be used to determine specifically which group means differ. The example calculates statistics to compare the analgesic effects of three drugs and finds no significant difference between the group means.
This document discusses different statistical tests used to analyze experimental research data, including the t-test, analysis of variance (ANOVA), and chi-square test. It provides examples of how to apply each test and interpret the results. The t-test is used to compare the means of two groups, ANOVA is used for comparing more than two groups, and chi-square is used to analyze relationships between categorical variables. Computer programs like SPSS can perform these statistical analyses to help researchers evaluate experimental data.
Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not.
Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918, when Ronald Fisher created the analysis of variance method.
ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, "Statistical Methods for Research Workers."
It was employed in experimental psychology and later expanded to subjects that were more complex.ANOVA (Analysis Of Variance) is a collection of statistical models used to assess the differences between the means of two independent groups by separating the variability into systematic and random factors. It helps to determine the effect of the independent variable on the dependent variable. Here are the three important ANOVA assumptions:
1. Normally distributed population derives different group samples.
2. The sample or distribution has a homogenous variance
3. Analysts draw all the data in a sample independently.
ANOVA test has other secondary assumptions as well, they are:
1. The observations must be independent of each other and randomly sampled.
2. There are additive effects for the factors.
3. The sample size must always be greater than 10.
4. The sample population must be uni-modal as well as symmetrical.
TYPES OF ANOVA
1. One way ANOVA analysis of variance is commonly called a one-factor test in relation to the dependent subject and independent variable. Statisticians utilize it while comparing the means of groups independent of each other using the Analysis of Variance coefficient formula. A single independent variable with at least two levels. The one way Analysis of Variance is quite similar to the t-test.
2 TWO WAY ANOVA
The pre-requisite for conducting a two-way anova test is the presence of two independent variables; one can perform it in two ways –
Two way ANOVA with replication or repeated measures analysis of variance – is done when the two independent groups with dependent variables do different tasks.
Two way ANOVA sans replication – is done when one has a single group that they have to double test like one tests a player before and after a football game
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
This document provides an overview of parametric and nonparametric statistical methods. It defines key concepts like standard error, degrees of freedom, critical values, and one-tailed versus two-tailed hypotheses. Common parametric tests discussed include t-tests, ANOVA, ANCOVA, and MANOVA. Nonparametric tests covered are chi-square, Mann-Whitney U, Kruskal-Wallis, and Friedman. The document explains when to use parametric versus nonparametric methods and how measures like effect size can quantify the strength of relationships found.
Gulfstream Aerospace tested three prototypes of a new jet for flight range. Each prototype flew 10 routes and the range was recorded. ANOVA was used to determine if the average ranges were significantly different. It decomposes total variation into between-group and within-group components to calculate an F-ratio to test the null hypothesis that the means are equal. The results did not provide enough evidence to conclude the prototypes had different average ranges.
1. An independent samples t-test was conducted to determine if there were differences in anxiety scores between male and female participants before a major competition.
2. The results of the t-test showed no significant difference between the mean anxiety scores of males (M=17, SD=4.58) and females (M=18, SD=3.16), t(8)=0.41, p>0.05.
3. Therefore, the null hypothesis that there is no difference between male and female anxiety scores before a major competition was not rejected.
Variance component analysis by paravayya c pujeriParavayya Pujeri
This document discusses variance component analysis and provides examples of its applications and methodology. It begins by defining key concepts such as fixed and random factors, effects, and mixed effect models. It then explains that variance component analysis partitions total variation in a dependent variable into components associated with random effects variables. The document provides examples of estimating variance components using ANOVA and examples analyzing agricultural and interlaboratory study data. It concludes that variance component analysis helps partition variation and determine where to focus efforts to reduce variance.
- Analysis of variance (ANOVA) is a statistical technique used to determine if the means of different groups are significantly different from each other.
- ANOVA separates the total variation in a data set into component parts associated with different sources of variation to test their statistical significance.
- The document provides definitions of ANOVA, assumptions of ANOVA, techniques for one-way and two-way ANOVA including calculation of sum of squares, variance, and the F-ratio to test for significance of differences between means.
- An example illustrates a one-way ANOVA calculation to test for differences in crop yields between four varieties.
1. The document discusses key concepts in statistics including population, sampling, random sampling, standard error, and standard error of the mean.
2. A population is the total set of observations, while a sample is a subset selected from the population. Random sampling selects subjects entirely by chance so each member has an equal chance of being selected.
3. The standard error is the standard deviation of a statistic's sampling distribution and indicates how much a statistic may vary between samples. It decreases with larger sample sizes. The standard error of the mean specifically measures how much the sample mean may differ from the population mean.
Cellular adaptation involves alterations in cell structure and function in response to sublethal stimuli. There are several types of morphologic adaptation:
Hypertrophy is an increase in cell size without cell division. Hyperplasia is an increase in cell number. Atrophy is a decrease in cell size and number. Metaplasia is the replacement of one differentiated cell type with another. Dysplasia involves abnormal cell growth and maturation. These adaptive responses allow cells to withstand sublethal stimuli but may progress to more severe injury if the stimuli persist.
This document provides an overview of histopathology techniques, including different types of biopsy methods, principles of specimen handling and examination, tissue processing steps, and staining procedures. It discusses incisional, excisional, punch, core needle, and curettage biopsies. Key steps in tissue processing are described as fixation, dehydration, clearing, embedding, sectioning, and staining. Common stains like H&E and special stains are also outlined.
The document discusses various parametric statistical tests including t-tests, ANOVA, ANCOVA, and MANOVA. It provides definitions and assumptions for parametric tests and explains how they can be used to analyze quantitative data that follows a normal distribution. Specific parametric tests covered in detail include the independent samples t-test, paired t-test, one-way ANOVA, two-way ANOVA, and ANCOVA. Examples are provided to illustrate how each test is conducted and how results are interpreted.
The document provides an overview of analysis of variance (ANOVA), including what it is, how it works, key terminology, and the steps to conduct one-way and two-way ANOVA tests. ANOVA is a statistical technique used to test if there are significant differences between the means of two or more groups. It compares the variation within groups to the variation between groups to determine if observed differences are due to chance. The document outlines the null and alternative hypotheses, calculations for sums of squares, degrees of freedom, F-statistics, and how to interpret the results against critical values from the F-distribution table.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
The document provides information on statistical techniques for comparing means between groups, including t-tests, analysis of variance (ANOVA), and their assumptions and applications. T-tests are used to compare two groups, while ANOVA allows comparison of three or more groups and controls for increased Type I error rates. Steps for conducting t-tests, ANOVA, and post-hoc tests using SPSS are outlined along with examples and interpretations.
The document discusses statistical methods for comparing means between groups, including t-tests and analysis of variance (ANOVA). It provides information on different types of t-tests (one sample, independent samples, and paired samples t-tests), assumptions of t-tests, and how to perform t-tests in SPSS. It also covers one-way ANOVA, including its assumptions, components of variation, properties of the F-test, and how to run a one-way ANOVA in SPSS. Examples are provided for each statistical test.
(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
The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.
This document discusses analysis of variance (ANOVA), which is a statistical technique used to compare three or more population means. It begins by introducing the assumptions of ANOVA, including that populations are normally distributed and have equal variances. It then explains how ANOVA decomposes total variation into between-sample and within-sample variations. The ratio of between-sample to within-sample variation, known as the F-ratio, is used to test if population means are equal. One-way and two-way classifications of ANOVA are described.
This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.
This document provides an overview of one-way ANOVA, including its assumptions, steps, and an example. One-way ANOVA tests whether the means of three or more independent groups are significantly different. It compares the variance between sample means to the variance within samples using an F-statistic. If the F-statistic exceeds a critical value, then at least one group mean is significantly different from the others. Post-hoc tests may then be used to determine specifically which group means differ. The example calculates statistics to compare the analgesic effects of three drugs and finds no significant difference between the group means.
This document discusses different statistical tests used to analyze experimental research data, including the t-test, analysis of variance (ANOVA), and chi-square test. It provides examples of how to apply each test and interpret the results. The t-test is used to compare the means of two groups, ANOVA is used for comparing more than two groups, and chi-square is used to analyze relationships between categorical variables. Computer programs like SPSS can perform these statistical analyses to help researchers evaluate experimental data.
Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not.
Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918, when Ronald Fisher created the analysis of variance method.
ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, "Statistical Methods for Research Workers."
It was employed in experimental psychology and later expanded to subjects that were more complex.ANOVA (Analysis Of Variance) is a collection of statistical models used to assess the differences between the means of two independent groups by separating the variability into systematic and random factors. It helps to determine the effect of the independent variable on the dependent variable. Here are the three important ANOVA assumptions:
1. Normally distributed population derives different group samples.
2. The sample or distribution has a homogenous variance
3. Analysts draw all the data in a sample independently.
ANOVA test has other secondary assumptions as well, they are:
1. The observations must be independent of each other and randomly sampled.
2. There are additive effects for the factors.
3. The sample size must always be greater than 10.
4. The sample population must be uni-modal as well as symmetrical.
TYPES OF ANOVA
1. One way ANOVA analysis of variance is commonly called a one-factor test in relation to the dependent subject and independent variable. Statisticians utilize it while comparing the means of groups independent of each other using the Analysis of Variance coefficient formula. A single independent variable with at least two levels. The one way Analysis of Variance is quite similar to the t-test.
2 TWO WAY ANOVA
The pre-requisite for conducting a two-way anova test is the presence of two independent variables; one can perform it in two ways –
Two way ANOVA with replication or repeated measures analysis of variance – is done when the two independent groups with dependent variables do different tasks.
Two way ANOVA sans replication – is done when one has a single group that they have to double test like one tests a player before and after a football game
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines ANOVA as a statistical tool used to test differences between two or more means by analyzing variance. One-way ANOVA tests the effect of one factor on the mean and splits total variation into between-groups and within-groups components. Two-way ANOVA controls for another variable as a blocking factor to reduce error variance and splits total variation into between treatments, between blocks, and residual components. The document reviews key ANOVA terms, assumptions, calculations including sum of squares, F-ratio and p-value, and provides examples of one-way and two-way ANOVA.
This document provides an overview of parametric and nonparametric statistical methods. It defines key concepts like standard error, degrees of freedom, critical values, and one-tailed versus two-tailed hypotheses. Common parametric tests discussed include t-tests, ANOVA, ANCOVA, and MANOVA. Nonparametric tests covered are chi-square, Mann-Whitney U, Kruskal-Wallis, and Friedman. The document explains when to use parametric versus nonparametric methods and how measures like effect size can quantify the strength of relationships found.
Gulfstream Aerospace tested three prototypes of a new jet for flight range. Each prototype flew 10 routes and the range was recorded. ANOVA was used to determine if the average ranges were significantly different. It decomposes total variation into between-group and within-group components to calculate an F-ratio to test the null hypothesis that the means are equal. The results did not provide enough evidence to conclude the prototypes had different average ranges.
1. An independent samples t-test was conducted to determine if there were differences in anxiety scores between male and female participants before a major competition.
2. The results of the t-test showed no significant difference between the mean anxiety scores of males (M=17, SD=4.58) and females (M=18, SD=3.16), t(8)=0.41, p>0.05.
3. Therefore, the null hypothesis that there is no difference between male and female anxiety scores before a major competition was not rejected.
Variance component analysis by paravayya c pujeriParavayya Pujeri
This document discusses variance component analysis and provides examples of its applications and methodology. It begins by defining key concepts such as fixed and random factors, effects, and mixed effect models. It then explains that variance component analysis partitions total variation in a dependent variable into components associated with random effects variables. The document provides examples of estimating variance components using ANOVA and examples analyzing agricultural and interlaboratory study data. It concludes that variance component analysis helps partition variation and determine where to focus efforts to reduce variance.
- Analysis of variance (ANOVA) is a statistical technique used to determine if the means of different groups are significantly different from each other.
- ANOVA separates the total variation in a data set into component parts associated with different sources of variation to test their statistical significance.
- The document provides definitions of ANOVA, assumptions of ANOVA, techniques for one-way and two-way ANOVA including calculation of sum of squares, variance, and the F-ratio to test for significance of differences between means.
- An example illustrates a one-way ANOVA calculation to test for differences in crop yields between four varieties.
1. The document discusses key concepts in statistics including population, sampling, random sampling, standard error, and standard error of the mean.
2. A population is the total set of observations, while a sample is a subset selected from the population. Random sampling selects subjects entirely by chance so each member has an equal chance of being selected.
3. The standard error is the standard deviation of a statistic's sampling distribution and indicates how much a statistic may vary between samples. It decreases with larger sample sizes. The standard error of the mean specifically measures how much the sample mean may differ from the population mean.
Cellular adaptation involves alterations in cell structure and function in response to sublethal stimuli. There are several types of morphologic adaptation:
Hypertrophy is an increase in cell size without cell division. Hyperplasia is an increase in cell number. Atrophy is a decrease in cell size and number. Metaplasia is the replacement of one differentiated cell type with another. Dysplasia involves abnormal cell growth and maturation. These adaptive responses allow cells to withstand sublethal stimuli but may progress to more severe injury if the stimuli persist.
This document provides an overview of histopathology techniques, including different types of biopsy methods, principles of specimen handling and examination, tissue processing steps, and staining procedures. It discusses incisional, excisional, punch, core needle, and curettage biopsies. Key steps in tissue processing are described as fixation, dehydration, clearing, embedding, sectioning, and staining. Common stains like H&E and special stains are also outlined.
Osteogenic cells are rounded cells with rounded nuclei that are rich in alkaline phosphatase and present in the endosteum. They cannot divide and have an acidophilic cytoplasm. Osteogenic cells are found in the endosteum. Osteoclasts are large, multinucleated cells that are found in Howship's lacunae and have foamy acidophilic cytoplasm containing lysosomes. They are derived from monocytes. Haversian systems in compact bone are interconnected by interstitial lamellae and contain concentric lamellae, central Haversian canals, and osteocytes in lacunae.
Osteogenic cells are rounded cells with rounded nuclei that are rich in alkaline phosphatase. They are present in the endosteum and are acidophilic cells. Osteogenic cells cannot divide. Osteoclasts are large, multinucleated cells that are found in Howship's lacunae and have foamy acidophilic cytoplasm containing lysosomes. They are derived from monocytes. Haversian systems in compact bone are interconnected by interstitial lamellae and contain concentric lamellae, osteocytes in lacunae, and a central Haversian canal.
This document discusses the musculoskeletal system and provides details on cartilage, bone, and ossification. It describes three types of cartilage - hyaline, yellow elastic fibrocartilage, and white fibrocartilage. It also outlines the structure and function of compact and cancellous bone, as well as the cells and tissues involved in bone formation like osteoblasts, osteocytes, osteoclasts, periosteum, and endosteum. Finally, it summarizes the two types of ossification - intramembranous and endochondral/intracartilagenous ossification.
This document discusses the musculoskeletal system and provides details on cartilage, bone, and ossification. It describes the three types of cartilage - hyaline, elastic, and fibrocartilage - and their structure and locations in the body. It also outlines the structure of compact and spongous bone, including the cells and layers involved. Finally, it explains the two types of ossification - intramembranous and endochondral - and the stages of each process.
This document summarizes the structure and function of the musculoskeletal system. It describes the three types of cartilage - hyaline, elastic, and fibrocartilage. It also discusses the structure of bones, including compact and spongy bone. Finally, it examines the two processes of bone formation: intramembranous ossification which forms flat bones, and endochondral ossification where cartilage is replaced by bone in long bones.
The Hematoxylin and Eosin stain (H&E) is the most widely used histological stain due to its comparative simplicity and ability to clearly demonstrate many different tissue structures. Hematoxylin stains cell nuclei blue-black and shows good intranuclear detail, while Eosin stains cell cytoplasm and connective tissue in varying shades of pink, orange, and red. The H&E stain outlines tissues and cellular components and is essential for identifying tissues and establishing the presence or absence of disease processes. Several hematoxylin solutions are described in the document, varying in their mordants, methods of oxidation, and intended uses in histology.
The document provides guidance on preparing the Results section of a research paper. It recommends that the Results section:
- Summarize the key findings without providing excessive detail
- Present results objectively without interpretation
- Highlight important findings in text and use tables and figures to complement rather than repeat the data
- Use the past tense and cite results clearly while referring to tables and figures
Detection systems are used in immunohistochemistry and in-situ hybridization to detect bound primary antibodies or nucleic acid probes, using enzymes, substrates, and chromogens to produce a visible signal under microscopy; common detection systems are based on horseradish peroxidase or alkaline phosphatase enzymes with DAB or permanent red chromogens, while in-situ hybridization localizes DNA or RNA in tissue using labeled probes. Blocking steps help reduce background staining when using these detection systems.
The document provides guidance on writing the results section of a research paper. The key points are:
1) The results section should summarize the main findings of the study in relation to the research objectives, without providing extensive details or interpreting the results.
2) Visuals like tables and figures are generally better than text for presenting data findings. The text should highlight important points and complement the visuals without repeating all the information.
3) The results section only reports what was found and presents data objectively, leaving interpretation and discussion of what the results mean for the discussion section. It answers the question "what happened" based on analysis of the collected data.
This document provides guidance on writing the methodology and results sections of a research report. It discusses that the methodology section must provide enough detail that others could replicate the study, and should include subsections on participants, materials, and procedure. The participants subsection describes the sample. The materials subsection describes any tools or stimuli used. The procedure subsection outlines the steps of the experiment. The results section should report key findings through descriptive statistics, tables, and graphs. Statistical analyses and their results should also be presented.
This document summarizes the histology of bone. It describes bone as a mineralized connective tissue that provides structure and protection. The key components of bone are osteoblasts, osteocytes, osteoclasts, and a matrix containing collagen and calcium. Osteoblasts form new bone, osteocytes are embedded in the matrix, and osteoclasts resorb bone. Bone is further organized into compact bone, with dense osteons, and cancellous or spongy bone, with thin trabeculae. Bone remodeling is mediated by these cells to maintain bone strength and mineral homeostasis.
This document discusses the histology of cartilage. It defines cartilage and its main functions, which include bearing mechanical stresses, forming frameworks, and facilitating bone movement. The document then describes the three main types of cartilage - hyaline, elastic, and fibrocartilage - and discusses their composition, distribution in the body, and characteristics. It also addresses cartilage growth and repair mechanisms as well as common conditions that affect cartilage.
This document provides information about analyzing cerebrospinal fluid (CSF). It discusses the formation and function of CSF and describes routine laboratory assays performed on CSF samples, including examinations of appearance, cell counts, chemical analysis, and microbiology. Procedures for collecting and handling CSF samples are outlined. Normal ranges and pathological conditions associated with abnormal CSF results are also reviewed.
Microorganisms can be classified based on their size, shape, and cellular structure. Bacteria are single-celled organisms that can be further classified as cocci, bacilli, or spirochetes depending on their shape. Special stains like Gram stain and acid-fast stain are used to differentiate bacteria and identify medically important types. Fungi have cell walls containing chitin while viruses are protein-coated genes that need host cells. Protozoa are single-celled organisms that move using pseudopods, flagella, or cilia. A variety of staining techniques exist to identify bacteria, fungi, and other microorganisms in clinical samples and tissue sections.
This document discusses various biological, infectious, chemical, mechanical, and ergonomic hazards found in laboratory environments. It outlines policies and procedures for implementing safety precautions like universal precautions, personal protective equipment, exposure control plans, hazard communication standards, and proper handling of sharps, waste and hazardous chemicals. Specific guidance is provided for pathogens such as HIV, hepatitis viruses, tuberculosis, and prion diseases. Engineering and administrative controls are emphasized to reduce risks from all identified hazards.
This document appears to be a series of 10 case studies presenting cytology samples from fine needle aspirations and other specimens. Each case includes images of cell morphologies and characteristics along with multiple choice options for possible diagnoses. In case 7, images show poorly cohesive epithelial-like cells associated with fibromyxoid stroma, suggesting a diagnosis of adenoid cystic carcinoma. Case 10 features images consistent with Warthin's tumor, characterized by lymphocytes and oncocytic cells. The document seems to be a teaching aid to help learners identify cell types and arrive at cytological diagnoses.
This document provides guidance on writing effective multiple choice questions (MCQs) for assessment. It discusses the benefits of MCQs, such as wide topic coverage and fast feedback, as well as disadvantages like guessing and lower-order thinking. Tips are provided for writing high-quality question stems and plausible distractors that avoid clues. The document emphasizes shifting focus from recall to application and using novel contexts to make questions less "googleable." Strategies are presented for engaging students with formative assessment through varied feedback, social learning, and emphasis on intrinsic motivation.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
3. STATISTICAL SOFTWARE PACKAGES
Follow the link >>>
http://www.coedu.usf.edu/main/departments/me/MeasurementandResearchStati
sticalSoftwarePackages.html
5. WHAT IS SPSS?
• SPSSStatisticsis asoftware package used for statistical analysis.
• S
P
S
Scan be used for:
– Processing Questionnaire
– Reporting in tables andgraphs
– Analyzing
• Mean, Median, Mode
• Mean Dev& Std. Dev.,
• Correlation & Regression,
• ChiSquare,T-Test,Z-test,ANOVA, MANOVA, FactorAnalysis, ClusterAnalysis, Multidimensional Scalingetc.
• Founded in 1968 and acquired by IBM in 2009.
6.
7. WHAT IS HYPOTHESIS?
“The statement speculating the outcome of a research or experiment.”
• H0=There is no difference in performance of Div. A, B and C in Semester I
• Ha=Business Communication subject has been effective in developing communication skills of students
• H0=Biometric system has not improved the attendance of faculties
• Ha=Excessive fishing has affected marine life
• H0=There is no significant difference in salary of males and females in particular organization.
Here,
H0=Null Hypothesis
Ha=Alternate Hypothesis
8. WHAT IS LEVEL OF SIGNIFICANCE
When null hypothesis is true, you accept it.
When it is false, you reject it.
5% level of significance means you are taking 5% risk of rejecting null hypothesis when it
happens to be true.
It is the maximum value of probability of rejecting H0 when it is true.
9. TYPES OF STATISTICAL TESTS
Tests Meaning When it is used Statistical tests
used
Parametric
Tests
Based on assumption that
population from where the
sample is drawn is normally
distributed.
Used to test parameters
like mean, standard
deviation, proportions
etc.
• T-test
• ANOVA
• ANCOVA
• MANOVA
• Karl Pearson
Non
parametric
Tests
Don’t require assumption
regarding shape of
population distribution.
Used mostly for
categorical variable or in
case of small sample
size which violates
normality.
• Chi Square
• Mann-Whitney U
• Wilcoxon Signed Rank
• Kruskal-Wallis
• Spearman’s
11. INTRODUCTION
• Significance of difference between means of two samples can bejudged using:
– Ztest (>30)
– Ttest (<30)
• Difficulty ariseswhile measuring difference between means of morethan 2samples
• ANOVAis usedin suchcases
• ANOVAis usedto test the significance of the difference between morethan two sample means and
to makeinferences aboutwhether our samples are drawnfrom population havingsame means
Significanceofdifferenceof IQof 2 divisions Ztest orTTest
Significanceof differencebetweenperformanceof 5 differenttypesofvehicles ANOVA
12. WHEN TO USE ANOVA?
Compare yield of crop from several variety of seeds
Mileage of 4 automobiles
Spending habits of five groups of students
Productivity of 4 different types of machine during a given period of time
Effectiveness of fitness programme on increase in stamina of 5 players
13. WHY ANOVA INSTEAD OF MULTIPLE T TEST?
• If more than two groups,why notjust doseveraltwo samplet-tests to compare the
meanfrom one group with the mean from each of the other groups?
• The problem with the multiple t-tests approach isthat asthe number of groups
increases,the number of two samplet-tests also increases.
• Asthe number of tests increasesthe probability of making aType I error also
increases.
14. ANOVA HYPOTHESES
• The Null hypothesis for ANOVAisthat the meansfor allgroups
are equal.
• TheAlternative hypothesis for ANOVAisthat at least two of
the meansare not equal.
16. What is 1-way ANOVA and 2-way ANOVA?
Ifwe take only one factor and investigatethe difference among its various categories having numerous possible
values, it is called asOne-way ANOVA.
Incasewe investigatetwo factors at the sametime, then we useTwo-way ANOVA
•
•
TrainingType Productivity
Advanced 200
Advanced 193
Advanced 207
Intermediate 172
Intermediate 179
Intermediate 186
Beginners 130
Beginners 125
Beginners 119
One-wayANOVA
Gender Educational
Level
Marks
Male School 89
Male College 50
Male School 90
Male College 80
Female College 50
Female University 40
Female School 91
Female University 56
Two-wayANOVA
17. HOW ANOVA WORKS?
• Three methods usedto dissolve a powder in water are compared bythe time (in minutes) it
takes until the powder isfully dissolved. The results are summarized in the following table:
• It isthought that the population means of the three methods m1, m2and m3are not all
equal (i.e., at least one m is different from the others). How can this betested?
18. • Oneway isto use multiple two-sample t-tests and
• compare Method 1with Method 2,
• Method 1with Method 3 and
• Method 2with Method 3 (comparing all the pairs)
• But if eachtest is0.05,the probability of making aType 1error when runningthree tests would
increase.
• Better method isANOVA(analysis of variance)
• Thetechnique requiresthe analysis of different forms of variances– hencethe name.
Important:ANOVAis usedto showthat means are different and not variance are different.
19. • ANOVAcomparestwo types of variances
• Thevariance withineachsample and
• Thevariance between different samples.
• The blackdottedarrows showthe per-sample variation of the individual data points aroundthe
sample mean (the variancewithin).
• The red arrowsshowthe variation of the sample meansaroundthe grand mean (thevariance
between).
20. STEPS FOR USING ANOVA
Null Hypothesis H0: μ1= μ2= μ3
=………=μk
Alternate Hypothesis Ha : μ1≠ μ2≠ μ3≠ …
…
…≠ μk
1. Calculate meanof each sample (x
̄ 1, x
̄ 2, x
̄ 3……x
̄ k)
2. Calculate meanof sample means:
Where k=Total number samples
3. Calculate Sumof Square between the samples:
Where n1=Total number of item in sample 1
n2=Total number of item in sample 2
n3=Total number of item in sample 3 …
…
…
…
…
…
…
…
.
Step 1 :State NullandAlternate Hypothesis
Step2 :ComputeVariance Betweenthe samples
X
K
k
X1
X2
X3
....... X
SSbetween n1(x1 x) n2(x2 x) n3(x3 x) ...... nk(xk x)
2 2 2 2
21. 1. Calculate Sumof Squarewithin the samples:
SSTotal=SSBetween+ SSWithin
Step3 :ComputeVarianceWithin samples
2 2 2 2
SSwithin i(x1i x1) i(x2i x2) i(x3i x3) .... i(xki xk)
Step4 :Calculatetotalvariance
Step5 :Calculateaveragevariance betweenandwithin
samples
k
SS Between
MSbetween
1
SSwithin
MSwithin
n k
N=Totalno of items in
all samples
K=Numberof samples
22. Step6 :Calculate F-ratio
within
between
MS
MS
Fratio
Step7 :Set upANOVAtable
Sourceof
variation
Sumof
squares(SS)
Degreeof
freedom (d.f)
MeanSquares F-Value
(Calculated)
Between
Samples
S
SBetween k-1 MSBetween=
S
SBetween/k-1
F=MSBetween/MS
Within
Within
Samples
S
SWithin n-k MSWithin=
S
SWithin/n-k
Total S
STotal n-1
23. Decision Rule: Reject H0if
Calculated value of F>Tabulated value of F
Otherwise accept H
Or
Accept H0if
Calculated value of F<Tabulated value of F
Otherwise reject H
0
0
Step8 : Lookfor Tablevalueof F
Steps:
1. Findout two degree of freedom (one for between and onefor
within)
2. Denote xfor between and yfor within [F(x,y)]
3. In F-distribution table, go along x columns, and down y rows.
Thepoint of intersection isyour tabulated F-ratio
24. EXAMPLE
• Set up ananalysis of variance table for the following per acre production
datafor three varieties of wheat, eachgrown on4 plots and state if the
variety differences are significant.
• Testat 5%level of significance
25. H0= The difference between varieties is not significant
Ha=The difference in varieties is significant
26. Interpretation:
Calculated Value of F<TableValue of F
∴Accept Null Hypothesis
Difference inwheatoutputdueto varieties isnotsignificantandisjusta matter of chance.
27. EXAMPLE
• Ranbaxy Ltd. has purchasedthree new machinesof different makesand
wishesto determine whether oneof them isfaster than the others in
producingacertain output.
• Four hourly productionfigures are observed at randomfrom each
machine andthe results are given below:
• UseANOVAand determine whether machinesare significantly different in
their meanspeed.
Observations M1 M2 M3
1 28 31 30
2 32 37 28
3 30 38 26
4 34 42 28
31. TWO WAY ANOVA
• Two-wayANOVAtechnique is usedwhenthe data are classified onthe basis of two factors.
• For example, the agricultural output may be classified onthe basis of different varieties of seedsand
also onthe basis of different varieties of fertilizers used.
• Twotypes of 2-wayANOVA
– Without repeated values
– With repeated values
40. WHAT IS RESEARCH PROPOSAL?
Aresearch proposal is adocument that provides adetailed description of the intended
program. It is like an outline of the entire research processthat gives a reader a
summary of the information discussed in a project.
41. WHAT IS RESEARCH PROPOSAL?
• Research proposal sets out
– Broadtopic you want to research
– What is it trying to achieve?
– How would you do research?
– What would betime need?
– What results it might produce?
42. PURPOSE OF RESEARCH PROPOSAL
• Convince others that research is worth
• Sellyour idea to funding agency
• Convince the problem is significant and worth study
• Approach is new and yield results
43. ELEMENTS OF RESEARCH PROPOSAL
Introduction
Statement of Problem
Purposeof the Study
Reviewof Literature
Questionsand Hypothesis
The Design– Methods & Procedures
Limitationsof the Study
Significanceof the Study
References
46. FACTOR ANALYSIS
“Factor analysis is astatistical method used to describe variability among
observed, correlated variables in terms of a potentially lower number of
unobserved variables called factors.”
48. PURPOSE OF FACTOR ANALYSIS
• Toidentify underlying constructs in the data.
• To reduce number of variables
• To reduce redundancy of data (E.g. Quantitative Aptitude)
49. APPLICATION OF FACTOR ANALYSIS
• Market Segmentation
• Product Research
• Advertising Studies
• Pricing Studies
52. WAYS OF FACTOR ANALYSIS
1. Confirmative FactorAnalysis
– Factors and corresponding variables are already known
– Onthe basis of literature review or past experience/expertise
2. Exploratory FactorAnalysis
– Algorithm is usedto explore pattern among variables
– Thenfactors are explored
– No prior hypothesisto start with
53. CONDITIONS FOR FACTOR ANALYSIS
• Use interval or ratio data
• Variables are related
• Sufficient number of variables (min 4-5 variables for one factor)
• Large no of observations
• All variables should be normally distributed
54. STEPS IN FACTOR ANALYSIS
Formulatethe Problem
Constructthe Correlation Matrix
Determinethe method of FactorAnalysis
Determine Numberof Factors
Estimatethe Factor Matrix
Rotatethe Factors
EstimatingPracticalSignificance
56. EXAMPLE
• Basketballer or volleyballer on the basis of anthropometric variables.
• High or low performer on the basis of skill.
• Juniors or seniors category on the basis of the maturity parameters.
58. OBJECTIVE
• To understand group differences and to predict the likelihood
that a particular entity will belong to a particular class or group
basedon independent variables.
59. PURPOSE
• Toclassify asubject into one of the two groups on the basis of
some independent traits.
• Tostudy the relationship between group membership and the
variables usedto predict the group membership.
60. SITUATIONS FOR ITS USE
• When the dependent variable is dichotomous or multichotomous.
• Independent variables are metric, i.e. interval or ratio.
• Example:
• Basketballer or volleyballer on the basis of anthropometricvariables.
• Highor low performer onthe basis of skill.
• Juniors or seniors category onthe basis of the maturity parameters.
61. ASSUMPTIONS
1. Samplesize
– Should be at least five times the number of independent variables.
2. Normal distribution
– Eachof the independent variable is normally distributed.
3. Homogeneityof variances/ covariances
– All variables have linear and homoscedastic relationships.
62. ASSUMPTIONS
• Outliers
– Outliers should not be present in the data. DAis highly sensitive to the inclusion
of outliers.
• Non-multicollinearity
– There should be any correlation among the independent variables.
• Mutually exclusive
– Thegroups must be mutually exclusive,with every subject or case belonging to
only one group.
63. ASSUMPTIONS
• Variability
– No independent variables should have azerovariability in either of the groups
formed bythe dependent variable.
67. DEFINITION
• “Cluster analysis is agroup of multivariate techniques whose primary purpose isto
group objects (e.g., respondents, products, or other entities) based on the
characteristicsthey possess.”
• It is a meansof grouping records based upon attributesthat makethem similar.
• If plotted geometrically,the objects within the clusters will be close together, while
the distance between clusters will befarther apart.
68. CLUSTER VS FACTOR ANALYSIS
Cluster analysis is about grouping subjects (e.g. people). Factoranalysis is about
grouping variables.
Cluster analysis is aform of categorization, whereas factor analysis is aform of
simplification.
In Cluster analysis, grouping is based on the distance (proximity), in Factoranalysis it
is based on variation (correlation)
69. EXAMPLE
• Suppose a marketing researcher wishes to determine market segments in a community based on
patterns of loyalty to brands and stores a small sample of seven respondents is selected as a pilot
test of how cluster analysis is applied. Two measures of loyalty- V1(store loyalty) and V2(brand
loyalty)- were measuredfor each respondents on 0-10scale.
70.
71. HOW DO WE MEASURE SIMILARITY?
• Proximity Matrix of EuclideanDistance Between Observations
Observation
Observations
A B C D E F G
A
B
C
D
E
F
G
---
3.162
5.099
5.099
5.000
6.403
3.606
---
2.000
2.828
2.236
3.606
2.236
---
2.000
2.236
3.000
3.606
---
4.123
5.000
5.000
---
1.414
2.000
---
3.162 ---
72. HOW DO WE FORM CLUSTERS?
• Identify the two most similar(closest) observations not already in the samecluster and combine
them.
• Weapply this rule repeatedlyto generate a numberof cluster solutions, starting with each
observation as its own “cluster” andthen combiningtwo clusters at atime until all observations are
inasingle cluster.
• This processistermed a hierarchical procedure becauseit moves in astepwise fashionto form an
entire rangeof cluster solutions. It is also anagglomerative method becauseclusters areformed by
combiningexisting clusters.
75. • Dendogram:
Graphical representation (tree graph) of the results of a hierarchical procedure. Starting with each
object as a separate cluster, the dendogram shows graphically how the clusters are combined at
eachstep of the procedure until all are contained in asingle cluster
76. USAGE OF CLUSTER ANALYSIS
Market Segmentation:
Splitting customers into different groups/segments where customers havesimilar requirements.
Segmentingindustries/sectors:
Segmenting Markets:
Cities or regions having commontraits like population mix, infrastructure development, climatic
condition etc.
Career Planning:
Grouping people on the basis of educational qualification, experience, aptitude and aspirations.
Segmentingfinancialsectors/instruments:
Grouping according to raw material cost,financial allocation, seasonability etc.
79. MEANING
• Concerned with understanding how people makechoices between products or
services or
• Combination of product and service
• Businesses can design new products or services that better meet customers
underlying needs.
• Conjoint analysis is a popular marketing researchtechnique that marketers useto
determine what features a new product should have and how it should be priced.
80. • Supposewe want to market a new golf ball. We know from experience and from
talking with golfers that there arethree important product features:
1. Average Driving Distance
2. Average Ball Life
3. Price
81. TYPES OF CONJOINT ANALYSIS
1. ChoiceBased
– Respondentsselectfrom groupedoptions
82. TYPES OF CONJOINT ANALYSIS
2. Adaptive Choice
– It is usedfor studying how people makedecisions regarding complex products or services
– Packagesadapt basedon previous selections
– It gets ‘smarter’ asthe survey progresses
84. TYPES OF CONJOINT ANALYSIS
3. Menu-based
1. Respondentsare showna list of features
and levels
2. They haveto chooseamongoptions
3. Example:Airtel My Plan
86. 4. Full profile rating based
– Display series of product profile
– Typically rated on likelihoodto purchase or
preferencescale
87. 5. Selfexplicate
– Direct askof features and levels
– Eachfeature is presented separately
for evaluation
– Respondents rate all remaining
features accordingto desirability
88. ADVANTAGES
• Estimates psychological tradeoffs that consumers makewhen evaluating several
attributes together
• Measures preferences at the individual level
• Uncovers real or hidden drivers which may not be apparent to the respondent
themselves
• Realistic choice or shopping task
• Usedto develop needs based segmentation
89. DISADVANTAGES
• Designing conjoint studies can becomplex
• With too many options, respondents resort to simplification strategies
• Respondents are unable to articulate attitudes toward new categories
• Poorly designed studies mayover-value emotional/preference variables and
undervalue concrete variables
• Does not take into account the number items per purchase so it cangive a poor
reading of market share
91. EXAMPLE
A researcher may give test subjects
several varieties of apple and have
them make comparisons on several
criteria between two apples at a time.
Once all the apples are directly
compared to each other variety, the
data is plotted on a graph that shows
how similar one type is to another.
92. MEANING
• Multidimensional scaling (MDS) is a meansof visualizing the level of similarity of
individual casesof adataset.
• Multidimensional scaling is a method usedto createcomparisons between things
that are difficult to compare.
• The end result of this process is generally atwo-dimensional chart that shows a level
of similarity between various items, all relative to one another.
93.
94. APPLICATIONS OF MDS
• Understanding the position of brands in the marketplace relative to groups of
homogeneous consumers.
• Identifying new products by looking for white space opportunities or gaps.
• Gaugingthe effectiveness of advertising by identifying the brands position before
and after acampaign.
• Assessingthe attitudes and perceptions of consumers.
• Determine what attributes the brand owns and what attributes competitors own.