The document discusses methods for processing and statistically analyzing data. It describes how data (variables) are transformed into other variables for analysis through tabulation or graphs. Variables are then statistically analyzed to draw conclusions. Both descriptive and inferential statistical analyses are discussed, including correlation/regression analysis for continuous data, comparison of means through t-tests, ANOVA for multiple groups, and non-parametric tests. One-way and two-way ANOVA are given as examples to compare means between more than two groups of data.
This document provides an overview of quantitative descriptive research and statistics. It defines levels of measurement as nominal, ordinal, interval, and ratio scales. Descriptive statistics are used to summarize data through measures of central tendency like mean, median, and mode as well as measures of variability like standard deviation. Nominal data is described through frequencies and percentages. Ordinal and interval data can also be described graphically through stem-and-leaf plots and evaluations of distributions, skewness, and kurtosis. Reliability of measures is determined through methods like split-half analysis and Cronbach's alpha.
Measurement theory shows that strong assumptions are required for certain statistics to provide meaningful information about reality. The quality, validity, reliability, precision, accuracy, and types of errors of a measurement must be considered. Measurements are made by comparing the object of measurement to a standard using some type of scale, such as nominal, ordinal, interval, ratio, or absolute.
This document defines and provides examples of different types of data:
- Discrete and categorical data can be counted and sorted into categories.
- Nominal data involves assigning codes to values. Ordinal data allows values to be ranked.
- Interval and continuous data can be measured and ordered on a scale.
- Frequency tables, pie charts, bar charts, dot plots and histograms are used to summarize different types of data. Outliers, symmetry, skewness and scatter plots are also discussed.
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default and contributed datasets that students can use, focusing on nominal, ordinal, interval, and ratio variables.
- Optional late topics like microarray analysis, pattern recognition, and time series analysis.
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7 and a final take-home exam assigned in class 8. The default dataset for class participation contains data on 60 subjects across 3-4 treatment groups and various measure types. Special topics may include microarray analysis, pattern recognition, machine learning, and hidden Markov modeling.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default datasets with health data that students can use for assignments, and an option for students to bring their own de-identified data.
- Possible special topics like machine learning, time series analysis, and others.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default and contributed datasets that students can use, focusing on nominal, ordinal, interval, and ratio variables.
- Optional late topics like microarray analysis, pattern recognition, and time series analysis.
- A taxonomy of statistics, covering statistical description, presentation of data through graphs and numbers, and measures of center and variability.
This document provides an overview of quantitative descriptive research and statistics. It defines levels of measurement as nominal, ordinal, interval, and ratio scales. Descriptive statistics are used to summarize data through measures of central tendency like mean, median, and mode as well as measures of variability like standard deviation. Nominal data is described through frequencies and percentages. Ordinal and interval data can also be described graphically through stem-and-leaf plots and evaluations of distributions, skewness, and kurtosis. Reliability of measures is determined through methods like split-half analysis and Cronbach's alpha.
Measurement theory shows that strong assumptions are required for certain statistics to provide meaningful information about reality. The quality, validity, reliability, precision, accuracy, and types of errors of a measurement must be considered. Measurements are made by comparing the object of measurement to a standard using some type of scale, such as nominal, ordinal, interval, ratio, or absolute.
This document defines and provides examples of different types of data:
- Discrete and categorical data can be counted and sorted into categories.
- Nominal data involves assigning codes to values. Ordinal data allows values to be ranked.
- Interval and continuous data can be measured and ordered on a scale.
- Frequency tables, pie charts, bar charts, dot plots and histograms are used to summarize different types of data. Outliers, symmetry, skewness and scatter plots are also discussed.
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default and contributed datasets that students can use, focusing on nominal, ordinal, interval, and ratio variables.
- Optional late topics like microarray analysis, pattern recognition, and time series analysis.
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7 and a final take-home exam assigned in class 8. The default dataset for class participation contains data on 60 subjects across 3-4 treatment groups and various measure types. Special topics may include microarray analysis, pattern recognition, machine learning, and hidden Markov modeling.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default datasets with health data that students can use for assignments, and an option for students to bring their own de-identified data.
- Possible special topics like machine learning, time series analysis, and others.
The document provides an overview of the structure and content of a biostatistics class. It includes:
- Two instructors who will teach 8 classes, with 3 take-home assignments and a final exam.
- Default and contributed datasets that students can use, focusing on nominal, ordinal, interval, and ratio variables.
- Optional late topics like microarray analysis, pattern recognition, and time series analysis.
- A taxonomy of statistics, covering statistical description, presentation of data through graphs and numbers, and measures of center and variability.
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data on 60 subjects across 3-4 treatment groups with various measure types. Students can also bring their own de-identified datasets. The course covers topics like microarray analysis, pattern recognition, machine learning and more.
STATISTICS BASICS INCLUDING DESCRIPTIVE STATISTICSnagamani651296
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data on 60 subjects across 3-4 treatment groups with various measure types. Students can also bring their own de-identified datasets. The course covers topics like microarray analysis, pattern recognition, machine learning and more.
Data Types and Graphical Representation.pptxanayanoor28
The document defines key concepts in data classification including scores, data sets, variables, and levels of measurement. It explains that a score is a measurement of an individual, the data set is the complete set of scores, and a variable is a characteristic that can differ between individuals. Variables can be qualitative or quantitative, discrete or continuous. The levels of measurement are nominal, ordinal, interval, and ratio. The document also introduces common statistical notations used to represent variables, sample sizes, and sums.
The class consists of 8 classes taught by two instructors. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data from 60 subjects across 3-4 groups with different variable types. Students can also bring their own de-identified datasets. Special topics may include microarray analysis, pattern recognition, machine learning, and time series analysis.
April Heyward Research Methods Class Session - 8-5-2021April Heyward
This document provides an overview of key concepts in research methods for public administration, including:
1. Levels of measurement for variables, including nominal, ordinal, interval, and ratio levels. Examples are provided for each level.
2. Common research designs such as experimental, quasi-experimental, cross-sectional, and longitudinal designs.
3. Quantitative data analysis techniques including descriptive statistics, inferential statistics like ANOVA and regression, and correlation analysis. Frequency distributions, measures of central tendency and variability are covered.
4. Confidence intervals and how they are used to estimate population parameters more accurately than point estimates, by providing a probability assessment through setting a confidence level. Common confidence levels like 90%, 95%,
This document provides an overview of descriptive statistics used in cardiovascular research. Descriptive statistics summarize and describe data through calculations of central tendency, dispersion, and shape. They are used to analyze variables that are discrete (categorical nominal and ordinal) or continuous. Common descriptive statistics include mean, median, mode, range, variance, standard deviation, quartiles, interquartile range, skewness, and kurtosis. Graphs such as dot plots, box plots, and histograms can complement tabular descriptive statistics to display patterns in the data. Univariate analysis examines one variable at a time to understand its distribution, central tendency, and dispersion.
This document provides definitions and explanations of key concepts in biostatistics and statistical hypothesis testing, including:
- Types of data/variables, measures of central tendency, measures of dispersion
- Descriptive vs inferential statistics, populations and samples
- Assumptions of parametric tests, tests of normality, homogeneity of variance
- Components of hypothesis testing, types of errors, significance levels and p-values
- T-tests, ANOVA, within-subjects and between-subjects designs
The document discusses various statistical concepts including:
- The functions of statistics such as expressing facts numerically and establishing relationships between facts.
- The importance of statistics to fields like administration, economics, research, and education.
- Common measures of central tendency including the mean, median, and mode.
- The difference between theoretical and empirical probabilities.
- Types of correlation like positive, negative, simple, and multiple correlation.
- Key statistical tests including t-tests, chi-square, F-tests, and measures of accuracy, precision, and confidence intervals.
The document discusses measures of dispersion, which describe how varied or spread out a data set is around the average value. It defines several measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. The standard deviation is described as the most important measure, as it takes into account all values in the data set and is not overly influenced by outliers. The document provides a detailed example of calculating the standard deviation, which involves finding the differences from the mean, squaring those values, summing them, and taking the square root.
The document discusses various concepts related to measurement, scaling, instrument design, and sampling. It defines measurement as assigning numbers to objects or observations according to specific rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the types of mathematical operations and statistical analyses that can be conducted. Good measurement is reliable, valid, and practical. Reliability refers to consistency over time, validity is the ability to measure what is intended, and practicality considers cost, convenience and interpretability.
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
This document summarizes quantitative data analysis techniques. It discusses how to summarize data using simple statistics like means and standard deviations. It also covers effect statistics that summarize relationships between variables, such as slopes from regression. Statistical tests like t-tests and ANOVA are used to generalize sample results to populations and assess statistical significance. Precision is expressed using confidence intervals rather than just p-values. More complex models can also be reduced to these foundational analyses.
This document discusses inferential statistics and various statistical tests used to analyze differences between groups. It describes measures of difference such as the t-test, analysis of variance (ANOVA), chi-square test, Mann-Whitney test, and Kruskal-Wallis test. It also covers regression analysis techniques like simple and multiple linear regression. Key steps are outlined for conducting t-tests, ANOVA, and interpreting their results from SPSS output. Degrees of freedom and their role in statistical tests are also explained.
This document discusses parametric and non-parametric statistical methods. It defines different levels of measurement and provides examples of parametric and non-parametric tests. Key points include:
- Parametric tests assume normal distributions and make inferences about population parameters, while non-parametric tests do not require assumptions about the distribution and can be used on ordinal or nominal data.
- Common non-parametric tests described are the sign test, Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis one-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
- Non-parametric tests are recommended when the data does not
Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between sample means to the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and control groups.
Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between two sample means over the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and untreated groups.
This document provides an overview of key concepts related to data in biology including:
1. Qualitative and quantitative data types. Qualitative data relates to characteristics or descriptions while quantitative data uses numerical scales.
2. Methods for displaying and analyzing data including graphs, measures of central tendency (mean, median, mode), and standard deviation.
3. Statistical hypothesis testing using t-tests to compare two samples and determine if differences are statistically significant.
4. Correlation and scatter plots which show the relationship between two variables but do not prove causation.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
This document discusses various measures of dispersion used to quantify how spread out or varied values in a data set are. It defines dispersion as the difference or deviation of values from the central value. Measures of dispersion described include range, standard deviation, quartile deviation, mean deviation, variance, and coefficient of variation. Both absolute measures, which use numerical variations, and relative measures, which use statistical variations based on percentages, are examined. Relative measures allow for comparison between different data sets.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data on 60 subjects across 3-4 treatment groups with various measure types. Students can also bring their own de-identified datasets. The course covers topics like microarray analysis, pattern recognition, machine learning and more.
STATISTICS BASICS INCLUDING DESCRIPTIVE STATISTICSnagamani651296
The class consists of 8 classes taught by two instructors over biostatistics and psychology. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data on 60 subjects across 3-4 treatment groups with various measure types. Students can also bring their own de-identified datasets. The course covers topics like microarray analysis, pattern recognition, machine learning and more.
Data Types and Graphical Representation.pptxanayanoor28
The document defines key concepts in data classification including scores, data sets, variables, and levels of measurement. It explains that a score is a measurement of an individual, the data set is the complete set of scores, and a variable is a characteristic that can differ between individuals. Variables can be qualitative or quantitative, discrete or continuous. The levels of measurement are nominal, ordinal, interval, and ratio. The document also introduces common statistical notations used to represent variables, sample sizes, and sums.
The class consists of 8 classes taught by two instructors. There are 3 take-home assignments due in classes 3, 5, and 7. A final take-home exam is assigned in class 8. The default dataset contains data from 60 subjects across 3-4 groups with different variable types. Students can also bring their own de-identified datasets. Special topics may include microarray analysis, pattern recognition, machine learning, and time series analysis.
April Heyward Research Methods Class Session - 8-5-2021April Heyward
This document provides an overview of key concepts in research methods for public administration, including:
1. Levels of measurement for variables, including nominal, ordinal, interval, and ratio levels. Examples are provided for each level.
2. Common research designs such as experimental, quasi-experimental, cross-sectional, and longitudinal designs.
3. Quantitative data analysis techniques including descriptive statistics, inferential statistics like ANOVA and regression, and correlation analysis. Frequency distributions, measures of central tendency and variability are covered.
4. Confidence intervals and how they are used to estimate population parameters more accurately than point estimates, by providing a probability assessment through setting a confidence level. Common confidence levels like 90%, 95%,
This document provides an overview of descriptive statistics used in cardiovascular research. Descriptive statistics summarize and describe data through calculations of central tendency, dispersion, and shape. They are used to analyze variables that are discrete (categorical nominal and ordinal) or continuous. Common descriptive statistics include mean, median, mode, range, variance, standard deviation, quartiles, interquartile range, skewness, and kurtosis. Graphs such as dot plots, box plots, and histograms can complement tabular descriptive statistics to display patterns in the data. Univariate analysis examines one variable at a time to understand its distribution, central tendency, and dispersion.
This document provides definitions and explanations of key concepts in biostatistics and statistical hypothesis testing, including:
- Types of data/variables, measures of central tendency, measures of dispersion
- Descriptive vs inferential statistics, populations and samples
- Assumptions of parametric tests, tests of normality, homogeneity of variance
- Components of hypothesis testing, types of errors, significance levels and p-values
- T-tests, ANOVA, within-subjects and between-subjects designs
The document discusses various statistical concepts including:
- The functions of statistics such as expressing facts numerically and establishing relationships between facts.
- The importance of statistics to fields like administration, economics, research, and education.
- Common measures of central tendency including the mean, median, and mode.
- The difference between theoretical and empirical probabilities.
- Types of correlation like positive, negative, simple, and multiple correlation.
- Key statistical tests including t-tests, chi-square, F-tests, and measures of accuracy, precision, and confidence intervals.
The document discusses measures of dispersion, which describe how varied or spread out a data set is around the average value. It defines several measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. The standard deviation is described as the most important measure, as it takes into account all values in the data set and is not overly influenced by outliers. The document provides a detailed example of calculating the standard deviation, which involves finding the differences from the mean, squaring those values, summing them, and taking the square root.
The document discusses various concepts related to measurement, scaling, instrument design, and sampling. It defines measurement as assigning numbers to objects or observations according to specific rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the types of mathematical operations and statistical analyses that can be conducted. Good measurement is reliable, valid, and practical. Reliability refers to consistency over time, validity is the ability to measure what is intended, and practicality considers cost, convenience and interpretability.
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
This document summarizes quantitative data analysis techniques. It discusses how to summarize data using simple statistics like means and standard deviations. It also covers effect statistics that summarize relationships between variables, such as slopes from regression. Statistical tests like t-tests and ANOVA are used to generalize sample results to populations and assess statistical significance. Precision is expressed using confidence intervals rather than just p-values. More complex models can also be reduced to these foundational analyses.
This document discusses inferential statistics and various statistical tests used to analyze differences between groups. It describes measures of difference such as the t-test, analysis of variance (ANOVA), chi-square test, Mann-Whitney test, and Kruskal-Wallis test. It also covers regression analysis techniques like simple and multiple linear regression. Key steps are outlined for conducting t-tests, ANOVA, and interpreting their results from SPSS output. Degrees of freedom and their role in statistical tests are also explained.
This document discusses parametric and non-parametric statistical methods. It defines different levels of measurement and provides examples of parametric and non-parametric tests. Key points include:
- Parametric tests assume normal distributions and make inferences about population parameters, while non-parametric tests do not require assumptions about the distribution and can be used on ordinal or nominal data.
- Common non-parametric tests described are the sign test, Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis one-way ANOVA. Examples are provided to demonstrate how to perform and interpret each test.
- Non-parametric tests are recommended when the data does not
Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between sample means to the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and control groups.
Student's t-test is used to determine if two population means are statistically different based on random samples from those populations. It calculates a ratio of the difference between two sample means over the variability within each sample. If the t-value is large enough based on the sample sizes and pre-set significance level (often 0.05), then the population means are considered statistically different. The t-test is commonly used to compare outcomes before and after an intervention or between treated and untreated groups.
This document provides an overview of key concepts related to data in biology including:
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2. Methods for displaying and analyzing data including graphs, measures of central tendency (mean, median, mode), and standard deviation.
3. Statistical hypothesis testing using t-tests to compare two samples and determine if differences are statistically significant.
4. Correlation and scatter plots which show the relationship between two variables but do not prove causation.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
This document discusses various measures of dispersion used to quantify how spread out or varied values in a data set are. It defines dispersion as the difference or deviation of values from the central value. Measures of dispersion described include range, standard deviation, quartile deviation, mean deviation, variance, and coefficient of variation. Both absolute measures, which use numerical variations, and relative measures, which use statistical variations based on percentages, are examined. Relative measures allow for comparison between different data sets.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Main Java[All of the Base Concepts}.docxadhitya5119
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How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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2. PENGOLAHAN DATA
Umumnya data (variabel) diolah menjadi
variable lain (parameter) yang akan dianalisis
Variabel yang diperoleh disampaikan dalam
bentuk Tabel atau Grafik/Diagram
Variabel diolah secara statistik untuk menarik
kesimpulan
3. Jenis penyajian data
Tabulasi data
Visual display for discrete variables
Visual display for continuous variables
Visual displays for two or more continuous
variables
5. Visual display for discrete variables
Contoh Pie-chart
Fig.3. Response of patients to a new analgesic drug.
45%
35%
20%
Good response
Fair response
Poor response
7. Visual display for continuous variables
Classification according to class-intervals
Determining the size of class interval:
i = R/(1 + 3.3 log N)
Where
i = size of class interval;
R = Range (i.e., difference between the values of the
largest item and smallest item among the given
items);
N = Number of items to be grouped.
8. Histogram yang melibatkan semua nilai
Frequency polygon
Cumulative frequency polygon
16.1
62.4
80.8
88.3
91.7
57.0
81.9
87.0
90.4
92.5
3.0
23.8
40.5
47.9
50.1
2.3
7.3
15.1
23.4
28.5
0.0
20.0
40.0
60.0
80.0
100.0
2 4 6 8 10
Time (hours)
Cumulative
amount
excreted
(%)
Cefadroxil
Cephalexin
Cefuroxime
Cefixime
Cumulative amounts of unchanged drug excreted in urine during the first 10 hours
following the administration of each drug.
9. Visual displays for two or more
continuous variables
Diagram Garis/Grafik
DISSOLUTION DATA
F2 = 42.1, Generic < Innovator
BIOAVAILABILITY DATA
NOT BIOEQUIVALENT
Rata-rata 2 subjek
0
50
100
150
200
250
300
350
400
0 4 8 12 16 20 24
Waktu (jam)
Kadar
(ng/ml)
AMARYL
ANPIRIDE CF5
0
20
40
60
80
100
0 10 20 30
Time (minutes)
%
drug
dissolved
Generic
Amaryl 4-mg
10. PENARIKAN KESIMPULAN
Keberadaan hubungan/korelasi
Membandingkan nilai rataan (uji
kebermaknaan atau test of significance)
Dll.
13. Kategori
Nominal
Suatu bentuk data numerik berupa hasil pengkategorian data
(kualitatif atau deskriftif) atau berupa pengkodean.
Misalnya jenis kelamin laki laki dan perempuan diberi kategori 1
dan 2, atau pengkategorian dari suatu keadaan yang
dinumerisasi dalam bentuk angka.
Nominal data are numerical in name only, because they do not
share any of the properties of the numbers we deal in ordinary
arithmetic.
For instance if we record marital status as 1, 2, 3, or 4 (blm
menikah, menikah, janda cerai, janda ditinggal mati) as stated
above, we cannot write 4 > 2 or 3 < 4 and we cannot write 3 – 1
= 4 – 2, 1 + 3 = 4 or 4 ¸ 2 = 2.
14. Ordinal scales
In those situations when we cannot do anything except set up
inequalities, we refer to the data as ordinal data. For instance, if
one mineral can scratch another, it receives a higher hardness
number and on Mohs’ scale the numbers from 1 to 10 are
assigned respectively to talc, gypsum, calcite, fluorite, apatite,
feldspar, quartz, topaz, sapphire and diamond. With these
numbers we can write 5 > 2 or 6 < 9 as apatite is harder than
gypsum and feldspar is softer than sapphire, but we cannot write
for example 10 – 9 = 5 – 4, because the difference in hardness
between diamond and sapphire is actually much greater than
that between apatite and fluorite. It would also be meaningless
to say that topaz is twice as hard as fluorite simply because their
respective hardness numbers on Mohs’ scale are 8 and 4.
15. Numerik
Interval scales,
When in addition to setting up inequalities we can also form differences, we refer
to the data as interval data. Suppose we are given the following temperature
readings (in degrees Fahrenheit): 58°, 63°, 70°, 95°, 110°, 126° and 135°. In this
case, we can write 100° > 70° or 95° < 135° which simply means that 110° is
warmer than 70° and that 95° is cooler than 135°. We can also write for example
95° – 70° = 135° – 110°, since equal temperature differences are equal in the
sense that the same amount of heat is required to raise the temperature of an
object from 70° to 95° or from 110° to 135°.
On the other hand, it would not mean much if we said that 126° is twice as hot
as 63°, even though 126°/ 63° = 2. To show the reason, we have only to change
to the centigrade scale, where the first temperature becomes 5/9 (126 – 32) =
52°, the second temperature becomes 5/9 (63 –32) = 17° and the first figure is
now more than three times the second. This difficulty arises from the fact that
Fahrenheit and Centigrade scales both have artificial origins (zeros) i.e., the
number 0 of neither scale is indicative of the absence of whatever quantity we
are trying to measure.
Interval scales can have an arbitrary zero, but it is not possible to determine for
them what may be called an absolute zero or the unique origin
16. Ratio scales,
When in addition to setting up inequalities and forming
differences we can also form quotients (i.e., when we can
perform all the customary operations of mathematics), we
refer to such data as ratio data. In this sense, ratio data
includes all the usual measurement (or determinations) of
length, height, money amounts, weight, volume, area,
pressures etc
Ratio scales have an absolute or true zero of
measurement. The term ‘absolute zero’ is not as precise
as it was once believed to be. We can conceive of an
absolute zero of length and similarly we can conceive of
an absolute zero of time.
17. Scale types with their properties according to Stanley Smith Stevens
Logical/
math
operations
Nominal Ordinal Interval Ratio
X
/
Ӿ Ӿ Ӿ √
+
-
Ӿ Ӿ √ √
<
>
Ӿ √ √ √
=
#
√ √ √ √
Contoh Jenis
kelamin
Kesehatan, Tanggal
Altitude
Umur
25. Analisis keberadaan hubungan
Analisis korelasi-regresi (untuk data kontinyu)
Analisis korelasi peringkat (untuk data
peringkat)
26. In modern times, with the availability of computer facilities, there has been a
rapid development of multivariate analysis which may be defined as “all
statistical methods which simultaneously analyse more than two variables on a
sample of observations”3. Usually the following analyses* are involved when we
make a reference of multivariate analysis:
(a) Multiple regression analysis: This analysis is adopted when the researcher
has one dependent variable which is presumed to be a function of two or
more independent variables. The objective of this analysis is to make a
prediction about the dependent variable based on its covariance with all the
concerned independent variables.
(b) Multiple discriminant analysis: This analysis is appropriate when the
researcher has a single dependent variable that cannot be measured, but
can be classified into two or more groups on the basis of some attribute. The
object of this analysis happens to be to predict an entity’s possibility of
belonging to a particular group based on several predictor variables.
(c) Multivariate analysis of variance (or multi-ANOVA): This analysis is an
extension of two way ANOVA, wherein the ratio of among group variance to
within group variance is worked out on a set of variables.
(d) Canonical analysis: This analysis can be used in case of both measurable
and non-measurable variables for the purpose of simultaneously predicting a
set of dependent variables from their joint covariance with a set of
independent variables.
27. Membandingkan nilai rataan
Uji parametrik
- t-test atau uji hipotesis
- Analisis variansi (ANOVA)
Uji non-parametrik (bebas distribusi)
- Uji tanda/Sign test
- Wilcoxon signed rank test,
- Wilcoxon rank sum test,
- Kruskal-Wallis test,
- Friedman test
Uji untuk data hitung: Uji Khi-kuadrat
28. Membandingkan dua nilai rataan
Goup A Group B
70 60
60 56
59 55
56 53
56 48
54 45
52 45
51 44
44 42
44 38
n 10 10
Mean 54,6 49,8
Variance 53,4 61,8
Standar deviation 7,3 7,9
Contoh nilai ujian dari dua
kelompok yang berbeda
p
X z
n
p = 0,05
Group A = 50,1 – 59,1
Group B = 44,91 - 54,69
29. The total population may be too to be tested or the testing may be destructive. In
such cases, the variance must be estimated from data obtained from samples.
The appropriate test in this case is Student’s t-test
Disintegration Time
(Minutes) of Hard-Shell
Capsules
Containing Two
Formulations, A and B
Form. A Form B
11,1 9,2
10,3 10,3
13 11,2
14,3 11,3
11,2 1,5
14,7 9,5
n 6 6
Mean 12,43 10,33
Variance 3,36 0,74
Standar deviation 1,83 0,86
Statistical tests such as Student’s
t involve comparison of a value of
t calculated from the data with a
tabulated value. If the calculated
value exceeds the tabulated
value, then a significant difference
between the means of the two
groups has been detected.
30. The calculated value of t is also altered by changing the number of
replicates. If the number of degrees of freedom is increased, the calculated
value of t will rise, and a significant difference between the means is again
more likely to be detected.
Changes in the Calculated and Tabulated Values of t with Increased
Replication
Jumlah
pengukuran
T hitung Derajat
kebebasan
Two tail test One tail test
P=0,05 P=0,01 P=0,05 P=0,01
6 2,540 10 2,228 3,169 1,812 2,764
12 3,593 22 2,074 2,819 1,717 2,508
18 4,400 34 2,042 2,750 1,679 2,457
24 5,081 46 2,021 2,704 1,684 2,423
31. TREATMENT OF OUTLYING DATA POINTS
Identification of Outlying Data Points Using Hampel’s Rule
Group B Deviasi dari
median
Deviasi yang
diabsolutkan
Deviasi absolut yang
dinormalkan
66 19,5 19,5 2,42
56 9,5 9,5 1,18
55 8,5 8,5 1,06
53 6,5 6,5 0,81
48 1,5 1,5 0,49
45 -1,5 1,5 0,19
45 -1,5 1,5 0,19
44 -2,5 2,5 0,31
42 -4,5 4,5 0,56
0 -46,5 46,5 5,78
Median 46,5 5,5 Any result greater
than 3.5 is
considered an
outlier
MAD (1,483x5,5)
8,16
32. COMPARISON OF MEANS BETWEEN MORE THAN
TWO GROUPS OF DATA
ANOVA (contoh kekerasan tablet)
1. ONE-WAY ANALYSIS OF VARIANCE (One factor is deliberately
changed (e.g., Batch A, B, or C), yang membedakan antar group hanya
formula saja)
2. TWO-WAY ANALYSIS OF VARIANCE (misal kekerasan tablet
yang disebabkan oleh perbedaan formula dan alat yang digunakan)
33. One-way analysis of variance
Batch A Batch B Batch C
5,2 5,5 3,8
5,9 4,5 4,8
6,0 6,6 5,1
4,4 4,2 4,2
7,0 5,6 3,3
5,4 4,5 3,5
4,4 4,4 4,0
5,6 4,8 1,7
5,6 5,3 5,9
5,1 3,8 4,8
N 10 10 10
Mean 54,6 49,2 41,11
Total 5,46 4,92 4,11
Grand total 144,9
Variance 0,59 0,69 1,34
Standard deviation 0,77 0,83 1,16
34. 1. Calculate the total and the mean of every column.
2. Calculate the grand total.
3. Calculate the (grand total)2/(number of observations) = (144.9)2/30 =
699.87. This term is used several times in this calculation. It is often called
the correction term and denoted by the letter C.
4. Calculate the sum of (every result)2 = (5.2)2+ (5.9)2+ . . . + (4.8)2= 732.71.
5. Subtract C from the result of Step 4 = 732.71 − 699.87 = 32.84. This gives
the value of the term (Sx2− (Sx)2/n) and is known as the total sum of
squares.
6. Calculate the sums of squares between means = [(54.6)2/10 + (49.2)2/10 +
(41.1)2/10] −C= (298.12 + 242.06 + 168.92) − 699.87 = 9.23.
7. Calculate the difference between the total sum of squares and the sum of
squares between means = 32.84 − 9.23 = 23.61. This is known as the
residual sum of squares.
8. The degrees of freedom for the whole experiment are (3 × 10) − 1 = 29.
There are three groups of tablets and hence three means. There are
hence (3 − 1) 2 degrees of freedom here. Thus, the residual sum of
squares has (29 − 2) 27 degrees of freedom.
35. Source of Error Sum of
Squares
Degrees of
Freedom
Mean
Square
F
Between means 9.23 2 4.62 5.31
Within each group 23.61 27 0.87
Total 32.84 29
Editor's Notes
Nominal bisa diseut juga data tidak kontinyu seperti data atribute, lulus tidak lulus, terima tidak terima,
Ordinal adalah peringkat, derajat kesukaan, derajat keefektivan suatu obat,
nferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data
descriptive statistics aim to summarize a data set quantitatively without employing a probabilistic formulation,[2] rather than to supporting inferential statements about the population that the data are thought to represent. Most statistics can be used either as a descriptive statistic, or in an inductive analysis. For example, we can report the average reading test score for the students in each classroom in a school, to give a descriptive sense of the typical scores and their variation. If we perform a formal hypothesis test on the scores, we are doing inductive rather than descriptive analysis