Presentation1
•
0 likes•91 views
Repeatability refers to the variation in measurements taken by a single instrument on the same item under the same conditions. Less than perfect repeatability causes test-retest variability due to factors like intra-individual and intra-observer variability. To measure repeatability, multiple readings should be taken in the same place and calculated to find the average range to avoid errors. Calculating the mean, variance, and standard deviation of data readings can help eliminate repeatability errors by identifying measurement variability. Ensuring good repeatability through taking multiple readings is important for obtaining precise values and avoiding potential problems in measurement work.
Report
Share
Report
Share
Recommended
Chapter 1
The document outlines several key scientific concepts:
1) The scientific method is a standardized approach used by scientists involving making observations, forming hypotheses, conducting experiments, analyzing results, and publishing conclusions.
2) Scientific notation is a way to write very large or small numbers using powers of ten. It involves moving the decimal and multiplying by a power of ten.
3) Significant figures refer to the number of digits known with certainty in a measurement, which determines the precision of calculations.
Measures of central tendency ppt
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Summarizing Data by a Single Number
The term "measures of central tendency“ is a single value that attempts to describe a set of data by identifying the central position within that set of data.
my reliability
The document discusses experimental design and analysis for measuring reliability between two therapists. It describes how interclass correlation coefficient (ICC) and Bland & Altman plots are used to assess reliability of ultrasound measurements of muscle tissue taken by two therapists on 10 participants. The ICC between the therapists was found to be 0.92, indicating high reliability, though the confidence interval of 0.72 to 0.98 was considered wide.
Presentation1
This document defines and provides examples of statistical terms including accuracy, precision, mean, median, mode, and range. Accuracy refers to how close a measurement is to the true value, while precision refers to how close repeated measurements are to each other. The mean is calculated by adding all values and dividing by the total. The median is the middle value of a sorted data set. The mode is the most frequent value. Range is the difference between the highest and lowest values. Examples are given for each term to illustrate their definitions.
Scale development -- Reliability
This document discusses the concept of reliability in scale development and measurement. It defines reliability as how similar scores would be if the same person completed the scale twice, representing the precision of measurement. Greater reliability occurs when there is less random error and more consistency among item responses. Common methods for estimating reliability include internal consistency (such as Cronbach's alpha), parallel measurements using correlations between two administrations, and split-half reliability which divides a scale in half. Higher inter-item correlations and more items result in higher reliability estimates.
Range
This document discusses measures of dispersion, or variation, in data sets. It describes absolute and relative measures of dispersion. Absolute measures give the dispersion in the same units as the original data but cannot be used to compare different data sets. Relative measures are dimensionless and can be used to compare dispersion across data sets.
The document then discusses the range as the simplest measure of absolute dispersion. The range is defined as the difference between the maximum and minimum values in a data set. It also defines the coefficient of range, which normalizes the range as a percentage to allow comparison between data sets. Two examples are given to demonstrate calculating the range and coefficient of range.
Chapter 3 Review
This document provides a summary and examples of different methods for estimating and rounding numbers, including rounding to place values, front-end estimation, and clustering. It also covers calculating the mean, median, and mode of data sets, using formulas, and estimating products and quotients of decimals. Students are assigned problems reviewing these concepts from pages 164-165.
Recommended
Chapter 1
The document outlines several key scientific concepts:
1) The scientific method is a standardized approach used by scientists involving making observations, forming hypotheses, conducting experiments, analyzing results, and publishing conclusions.
2) Scientific notation is a way to write very large or small numbers using powers of ten. It involves moving the decimal and multiplying by a power of ten.
3) Significant figures refer to the number of digits known with certainty in a measurement, which determines the precision of calculations.
Measures of central tendency ppt
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Summarizing Data by a Single Number
The term "measures of central tendency“ is a single value that attempts to describe a set of data by identifying the central position within that set of data.
my reliability
The document discusses experimental design and analysis for measuring reliability between two therapists. It describes how interclass correlation coefficient (ICC) and Bland & Altman plots are used to assess reliability of ultrasound measurements of muscle tissue taken by two therapists on 10 participants. The ICC between the therapists was found to be 0.92, indicating high reliability, though the confidence interval of 0.72 to 0.98 was considered wide.
Presentation1
This document defines and provides examples of statistical terms including accuracy, precision, mean, median, mode, and range. Accuracy refers to how close a measurement is to the true value, while precision refers to how close repeated measurements are to each other. The mean is calculated by adding all values and dividing by the total. The median is the middle value of a sorted data set. The mode is the most frequent value. Range is the difference between the highest and lowest values. Examples are given for each term to illustrate their definitions.
Scale development -- Reliability
This document discusses the concept of reliability in scale development and measurement. It defines reliability as how similar scores would be if the same person completed the scale twice, representing the precision of measurement. Greater reliability occurs when there is less random error and more consistency among item responses. Common methods for estimating reliability include internal consistency (such as Cronbach's alpha), parallel measurements using correlations between two administrations, and split-half reliability which divides a scale in half. Higher inter-item correlations and more items result in higher reliability estimates.
Range
This document discusses measures of dispersion, or variation, in data sets. It describes absolute and relative measures of dispersion. Absolute measures give the dispersion in the same units as the original data but cannot be used to compare different data sets. Relative measures are dimensionless and can be used to compare dispersion across data sets.
The document then discusses the range as the simplest measure of absolute dispersion. The range is defined as the difference between the maximum and minimum values in a data set. It also defines the coefficient of range, which normalizes the range as a percentage to allow comparison between data sets. Two examples are given to demonstrate calculating the range and coefficient of range.
Chapter 3 Review
This document provides a summary and examples of different methods for estimating and rounding numbers, including rounding to place values, front-end estimation, and clustering. It also covers calculating the mean, median, and mode of data sets, using formulas, and estimating products and quotients of decimals. Students are assigned problems reviewing these concepts from pages 164-165.
TECNOLOGY-FUNDAMENTALS-MODULE-4
Information can be defined as an ordered sequence of symbols that can be interpreted as a message. It is recorded as signs or transmitted as signals, and affects the state of a dynamic system. For data to be useful, it must be organized in a meaningful way to become information. Information is more valuable when it helps decision makers achieve organizational goals in a timely, accessible, and secure manner. The quality, usability, and delivery of information are important attributes.
ESS Internal Assessment Rubric
Scoring rubric for Internal Assessments (IA)s in the IBDP Environmental Systems and Societies course.
Trends in Data Analysis in 2020 - International Journal of Computational Scie...
The results of an experiment can be presented as a data table or as an equation that represents them In thecase of adjusting a polynomial, Excel allows us to change its degree and calculates the R2 of the Adjustedequation. It is considered that if it is 1 the equation goes through all the experimental points. By adjustingTungsten resistivity data, it is found that the equations do not go through all the points (even with R2 =1), which is verified by calculating the differences for each point. In those cases, the best fit is the linearinterpolation between consecutive points. The equation adjusted by Excel, Mat lab or Origin requireschecking if it corresponds to the minimum in the sum of squared differences, it is possible that it can bereduced by changing the coefficients value
Ib ess guidelines for lab design
This document provides guidelines and templates for designing an experiment for an IB Extended Essay in Sciences. It instructs students to state a clear research question and hypothesis, define the independent and dependent variables, control for confounding variables, develop a method for collecting sufficient and relevant data, and process the data for analysis. Tables are provided to help students document the independent variable levels, dependent variable measurements, and controlled variables for their experimental design.
Data Preparation with the help of Analytics Methodology
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Tree pruning
The process of adjusting Decision Tree to minimize “misclassification error” is called pruning. It is of 2 types prepruning and post pruning.
Measurement
The document discusses various concepts related to measurement and error including:
- Defining accuracy as closeness to the true value and precision as reproducibility of measurements.
- Types of errors such as determinate/systematic errors which can be corrected and indeterminate/random errors which average out with multiple trials.
- Assessing total error by treating a reference standard as a sample and calculating differences from the reference value.
- Expressing accuracy and precision using terms like mean, percent error, range, standard deviation, and percent coefficient of variation.
PPTS FOR 9th CLASS OF deviation in values of central tendency BY RAMBABU SI...
This document discusses how measures of central tendency (mean, mode, median) are affected when the same operation is applied to all data values. It shows that if a constant is added to or subtracted from each value, the measures of central tendency are shifted by the same amount. If each value is multiplied or divided by the same constant, the measures of central tendency are multiplied or divided by the same constant. The conclusion is that applying the same operation to all data values proportionally shifts or scales the measures of central tendency.
ENRON EMAIL TEXT ANALYTICS
The document analyzes emails from the Enron bankruptcy scandal using predictive analytics. It cleans and pre-processes a dataset of 158 users' emails, creates bags of words and extracts frequent words. It then classifies the sentiments and applies a CART model to train and test datasets, checking the accuracy with confusion matrices, ROC curves and AUC scores. The CART model depicts key terms like "California" and "Gas" and achieves 84.04% accuracy, outperforming the baseline model. The ROC shows tradeoffs between sensitivity and specificity, and AUC indicates the model can differentiate responsive from non-responsive emails 83.57% of the time.
When to use the inter quartile range
This document discusses when to use the Mann-Whitney U test. The Mann-Whitney U test can be used if the data is ordinal or if the distribution is skewed or non-normal. It is used when either one of two conditions are met: the data is ordinal or the distribution is skewed or non-normal.
When to use the median
The median is used to summarize data when either the data is ordinal or the distribution is skewed or non-normal. The median is appropriate when one of two conditions are met: if the data is ordinal or if the distribution is skewed or non-normal. The document discusses when to use the median to summarize data.
Measurements and error in experiments
Every measurement has some level of uncertainty that should be reported along with the measured value. There are several ways to determine the uncertainty, including making repeated measurements and reporting the standard deviation, or estimating uncertainty based on the precision of the measuring instrument. Accuracy refers to how close a measurement is to the true value, while precision refers to how reproducibly a measurement can be made. When reporting measurements, the number of significant figures should be based on the precision of the measuring device and include an estimated uncertainty.
lb dimens analysis
This document provides information on performing dosage calculations using dimensional analysis. It outlines the components, advantages, and steps of the dimensional analysis method. Examples are provided to demonstrate setting up and solving dosage calculation problems using this method. Converting between units and knowing equivalencies are essential skills required to set up and solve the equations successfully. Resources for additional practice are included.
When to use the mean
The mean should be used to summarize data that is either scaled with equal intervals like speed, weight, height, and temperature, or when the distribution is normal. The mean is sensitive to outliers in skewed distributions, so it should only be used when the distribution is close to normal.
When to use the mean (2)
The MEAN should only be used to summarize data that is both scaled and normally distributed. Scaled data has equal intervals like speed, weight, height, and temperature. The mean is sensitive to outliers in skewed distributions so it should only be used when the distribution is close to normal.
Reliability
Reliability refers to the consistency of scores obtained from the same test administered under different conditions when no actual change has occurred. There are two broad approaches to assessing reliability: internal consistency and repeatability. Internal consistency is measured using Cronbach's alpha or split-half reliability and indicates how well test items measure the same construct. Repeatability, measured using test-retest reliability or inter-observer reliability, indicates the variation in measurements taken under the same conditions. For continuous variables, intraclass correlation and Bland-Altman plots are used, while kappa statistics assess agreement for categorical variables. Reliability coefficients above 0.7 indicate moderate to good reliability.
effect of learning rate
The document discusses the learning rate hyperparameter in neural networks. It explains that the learning rate controls how much the model weights are adjusted with respect to the loss gradient at each iteration. It influences the rate of convergence, and choosing the right learning rate is important but challenging as a value that is too small or large can negatively impact training. The document also discusses using momentum to improve the rate of convergence by adding a fraction of the previous weight change to the current change.
Sample Work
The document discusses the assumptions of the difference-in-differences estimator and how valid those assumptions need to be for unbiased estimates. It notes that a policy change in the UK between 1997-2003 that increased child benefits at different rates for first and subsequent children would violate the assumptions and induce downward bias in the estimates. The model is specified with equations showing the change in working hours per individual between years as the dependent variable.
Effect Size
This document discusses the importance of evaluating effect size, not just statistical significance (p-value), when analyzing differences between groups. It provides examples to illustrate effect size and defines some common effect size measures, such as Cohen's d and Hedges' g. The document also discusses how effect size can be used to determine required sample sizes needed to achieve a desired p-value threshold and level of confidence in the results.
Week 6 lecture_math_221_dec_2012
This document provides examples of questions from a statistics lab involving probability, random variables, confidence intervals, and descriptive statistics. It includes worked examples calculating the mean, standard deviation, and confidence intervals for data representing rolling dice and student sleep hours. The document explains that the data values were changed from the actual lab questions. It summarizes the key results and comparisons between methods, finding that the mean is a better estimate than the median for this data, and that higher confidence intervals are necessarily wider.
Direct Variation
The document defines direct variation and provides examples of direct variation relationships. It explains that direct variation occurs when y varies directly with x, meaning y=kx where k is the constant of variation. As x increases or decreases, y also increases or decreases. The document provides examples of tables showing direct variation relationships and calculating the constant of variation k. It also shows how to use direct variation equations to solve word problems by finding the constant k and plugging values into y=kx. Finally, it discusses the graph of a direct variation relationship, noting it must pass through the origin.
Direct and inverse variations
This document discusses direct and inverse variations. Direct variation means that as one variable increases, the other increases or decreases at a constant rate. The relationship can be expressed as y1/x1 = y2/x2. Inverse variation means that as one variable increases, the other decreases. The relationship is expressed as x1y1 = x2y2. Examples are provided of setting up and solving direct and inverse variation problems to find unknown variables.
More Related Content
What's hot
TECNOLOGY-FUNDAMENTALS-MODULE-4
Information can be defined as an ordered sequence of symbols that can be interpreted as a message. It is recorded as signs or transmitted as signals, and affects the state of a dynamic system. For data to be useful, it must be organized in a meaningful way to become information. Information is more valuable when it helps decision makers achieve organizational goals in a timely, accessible, and secure manner. The quality, usability, and delivery of information are important attributes.
ESS Internal Assessment Rubric
Scoring rubric for Internal Assessments (IA)s in the IBDP Environmental Systems and Societies course.
Trends in Data Analysis in 2020 - International Journal of Computational Scie...
The results of an experiment can be presented as a data table or as an equation that represents them In thecase of adjusting a polynomial, Excel allows us to change its degree and calculates the R2 of the Adjustedequation. It is considered that if it is 1 the equation goes through all the experimental points. By adjustingTungsten resistivity data, it is found that the equations do not go through all the points (even with R2 =1), which is verified by calculating the differences for each point. In those cases, the best fit is the linearinterpolation between consecutive points. The equation adjusted by Excel, Mat lab or Origin requireschecking if it corresponds to the minimum in the sum of squared differences, it is possible that it can bereduced by changing the coefficients value
Ib ess guidelines for lab design
This document provides guidelines and templates for designing an experiment for an IB Extended Essay in Sciences. It instructs students to state a clear research question and hypothesis, define the independent and dependent variables, control for confounding variables, develop a method for collecting sufficient and relevant data, and process the data for analysis. Tables are provided to help students document the independent variable levels, dependent variable measurements, and controlled variables for their experimental design.
Data Preparation with the help of Analytics Methodology
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Tree pruning
The process of adjusting Decision Tree to minimize “misclassification error” is called pruning. It is of 2 types prepruning and post pruning.
Measurement
The document discusses various concepts related to measurement and error including:
- Defining accuracy as closeness to the true value and precision as reproducibility of measurements.
- Types of errors such as determinate/systematic errors which can be corrected and indeterminate/random errors which average out with multiple trials.
- Assessing total error by treating a reference standard as a sample and calculating differences from the reference value.
- Expressing accuracy and precision using terms like mean, percent error, range, standard deviation, and percent coefficient of variation.
PPTS FOR 9th CLASS OF deviation in values of central tendency BY RAMBABU SI...
This document discusses how measures of central tendency (mean, mode, median) are affected when the same operation is applied to all data values. It shows that if a constant is added to or subtracted from each value, the measures of central tendency are shifted by the same amount. If each value is multiplied or divided by the same constant, the measures of central tendency are multiplied or divided by the same constant. The conclusion is that applying the same operation to all data values proportionally shifts or scales the measures of central tendency.
ENRON EMAIL TEXT ANALYTICS
The document analyzes emails from the Enron bankruptcy scandal using predictive analytics. It cleans and pre-processes a dataset of 158 users' emails, creates bags of words and extracts frequent words. It then classifies the sentiments and applies a CART model to train and test datasets, checking the accuracy with confusion matrices, ROC curves and AUC scores. The CART model depicts key terms like "California" and "Gas" and achieves 84.04% accuracy, outperforming the baseline model. The ROC shows tradeoffs between sensitivity and specificity, and AUC indicates the model can differentiate responsive from non-responsive emails 83.57% of the time.
When to use the inter quartile range
This document discusses when to use the Mann-Whitney U test. The Mann-Whitney U test can be used if the data is ordinal or if the distribution is skewed or non-normal. It is used when either one of two conditions are met: the data is ordinal or the distribution is skewed or non-normal.
When to use the median
The median is used to summarize data when either the data is ordinal or the distribution is skewed or non-normal. The median is appropriate when one of two conditions are met: if the data is ordinal or if the distribution is skewed or non-normal. The document discusses when to use the median to summarize data.
Measurements and error in experiments
Every measurement has some level of uncertainty that should be reported along with the measured value. There are several ways to determine the uncertainty, including making repeated measurements and reporting the standard deviation, or estimating uncertainty based on the precision of the measuring instrument. Accuracy refers to how close a measurement is to the true value, while precision refers to how reproducibly a measurement can be made. When reporting measurements, the number of significant figures should be based on the precision of the measuring device and include an estimated uncertainty.
lb dimens analysis
This document provides information on performing dosage calculations using dimensional analysis. It outlines the components, advantages, and steps of the dimensional analysis method. Examples are provided to demonstrate setting up and solving dosage calculation problems using this method. Converting between units and knowing equivalencies are essential skills required to set up and solve the equations successfully. Resources for additional practice are included.
When to use the mean
The mean should be used to summarize data that is either scaled with equal intervals like speed, weight, height, and temperature, or when the distribution is normal. The mean is sensitive to outliers in skewed distributions, so it should only be used when the distribution is close to normal.
When to use the mean (2)
The MEAN should only be used to summarize data that is both scaled and normally distributed. Scaled data has equal intervals like speed, weight, height, and temperature. The mean is sensitive to outliers in skewed distributions so it should only be used when the distribution is close to normal.
Reliability
Reliability refers to the consistency of scores obtained from the same test administered under different conditions when no actual change has occurred. There are two broad approaches to assessing reliability: internal consistency and repeatability. Internal consistency is measured using Cronbach's alpha or split-half reliability and indicates how well test items measure the same construct. Repeatability, measured using test-retest reliability or inter-observer reliability, indicates the variation in measurements taken under the same conditions. For continuous variables, intraclass correlation and Bland-Altman plots are used, while kappa statistics assess agreement for categorical variables. Reliability coefficients above 0.7 indicate moderate to good reliability.
effect of learning rate
The document discusses the learning rate hyperparameter in neural networks. It explains that the learning rate controls how much the model weights are adjusted with respect to the loss gradient at each iteration. It influences the rate of convergence, and choosing the right learning rate is important but challenging as a value that is too small or large can negatively impact training. The document also discusses using momentum to improve the rate of convergence by adding a fraction of the previous weight change to the current change.
Sample Work
The document discusses the assumptions of the difference-in-differences estimator and how valid those assumptions need to be for unbiased estimates. It notes that a policy change in the UK between 1997-2003 that increased child benefits at different rates for first and subsequent children would violate the assumptions and induce downward bias in the estimates. The model is specified with equations showing the change in working hours per individual between years as the dependent variable.
Effect Size
This document discusses the importance of evaluating effect size, not just statistical significance (p-value), when analyzing differences between groups. It provides examples to illustrate effect size and defines some common effect size measures, such as Cohen's d and Hedges' g. The document also discusses how effect size can be used to determine required sample sizes needed to achieve a desired p-value threshold and level of confidence in the results.
Week 6 lecture_math_221_dec_2012
This document provides examples of questions from a statistics lab involving probability, random variables, confidence intervals, and descriptive statistics. It includes worked examples calculating the mean, standard deviation, and confidence intervals for data representing rolling dice and student sleep hours. The document explains that the data values were changed from the actual lab questions. It summarizes the key results and comparisons between methods, finding that the mean is a better estimate than the median for this data, and that higher confidence intervals are necessarily wider.
What's hot (20)
Trends in Data Analysis in 2020 - International Journal of Computational Scie...
Trends in Data Analysis in 2020 - International Journal of Computational Scie...
Data Preparation with the help of Analytics Methodology
Data Preparation with the help of Analytics Methodology
PPTS FOR 9th CLASS OF deviation in values of central tendency BY RAMBABU SI...
PPTS FOR 9th CLASS OF deviation in values of central tendency BY RAMBABU SI...
Viewers also liked
Direct Variation
The document defines direct variation and provides examples of direct variation relationships. It explains that direct variation occurs when y varies directly with x, meaning y=kx where k is the constant of variation. As x increases or decreases, y also increases or decreases. The document provides examples of tables showing direct variation relationships and calculating the constant of variation k. It also shows how to use direct variation equations to solve word problems by finding the constant k and plugging values into y=kx. Finally, it discusses the graph of a direct variation relationship, noting it must pass through the origin.
Direct and inverse variations
This document discusses direct and inverse variations. Direct variation means that as one variable increases, the other increases or decreases at a constant rate. The relationship can be expressed as y1/x1 = y2/x2. Inverse variation means that as one variable increases, the other decreases. The relationship is expressed as x1y1 = x2y2. Examples are provided of setting up and solving direct and inverse variation problems to find unknown variables.
Inverse Variation
The document discusses different types of variations:
1) Direct variation results in a straight line graph, while direct square variation results in a parabolic graph.
2) Inverse variation means that as one variable increases, the other decreases, maintaining a constant product. The graph of an inverse variation is a hyperbola.
3) Examples show inverse variations between variables like pressure and volume of a gas, or jobs completed and number of workers.
4) Quiz questions test understanding of direct, inverse, and their equation representations.
Direct and inverse variation
This document discusses direct and inverse variation. It defines direct variation as a relationship that can be written as y=kx, where k is a constant, and inverse variation as a relationship that can be written as y=k/x, where k is a constant and x is not equal to 0. It provides examples of direct and inverse variation problems and their solutions. It also includes two worksheets with additional problems involving direct and inverse variation and asks the reader to determine the solutions.
Calibration
Calibration establishes the relationship between instrument measurements and known standard values through a series of steps. Key aspects of calibration include identifying instruments and sources, following calibration procedures, documenting results, accounting for sources of error, and ensuring traceability to national standards. Calibration procedures vary based on instrument type, but generally involve evaluating instrument performance, establishing calibration curves using certified reference materials at multiple concentration levels, and quantifying samples based on the calibration curves.
Direct inverse variation
The document discusses direct and inverse variation. It provides examples of questions determining if a relationship demonstrates direct or inverse variation. It also shows how to write the equation for a direct or inverse variation relationship given data points. Direct variation follows the equation y=kx, where k is the constant rate of change. Inverse variation follows y=k/x. The document provides step-by-step workings for multiple examples of identifying and modeling direct and inverse variation relationships from sets of data points.
2.2 Direct Variation
Direct variation functions have the form y = kx, where k is the constant of variation. The graphs of direct variation functions pass through the origin, and the ratio of all output to input pairs equals the constant k. To determine if a relationship is direct variation, check if the ratios of all y/x pairs are equal. Direct variation can be identified from equations by solving for y in the form y = kx. Proportions can be set up to solve direct variation problems. The graph of a direct variation is always a line through the origin.
Instrument Calibration
The document discusses measurement, calibration, and units of measurement. Some key points:
- Measurement is the first step to control and improvement. If you can't measure something, you can't understand or control it.
- The International System of Units (SI) defines seven base units including the meter, kilogram, second, ampere, kelvin, mole, and candela. Other units are derived from these base units.
- Calibration establishes the relationship between measurement instruments and reference standards under specific conditions. Regular calibration helps ensure accuracy and traceability to national standards.
- Factors like instrument specifications, use, environment, and measurement accuracy needed should be considered when determining calibration frequency.
Direct and Inverse variations
Direct variation describes a relationship where two quantities vary proportionally such that as one increases, the other increases or decreases at a constant rate. The formula for direct variation is: y1/x1 = y2/x2.
Inverse variation also describes a proportional relationship between two quantities, but as one increases the other decreases. With inverse variation, the quantities are multiplied rather than divided.
Examples are provided to demonstrate calculating unknown values using the direct and inverse variation formulas when given certain known values from the relationships.
Chapter 5 Direct Variation
This document discusses direct variation and how to identify, write equations for, solve, and graph direct variations. It provides examples of identifying direct variations from equations and ordered pairs by checking if the equations are in the form y=kx or if the ratio k is the same for each pair. It also gives examples of writing and solving direct variation equations using the constant k or proportions, and graphing direct variations by choosing x-values and generating ordered pairs.
direct variation grade9 module 3 by mr. joel garcia
This document discusses direct variation and provides examples to illustrate the concept. Direct variation means that as one variable (the independent variable) increases or decreases, the other variable (the dependent variable) also increases or decreases by a constant factor. The constant factor is called the constant of variation. Several examples of direct variation relationships in tables and graphs are presented, along with exercises for students to practice determining if a relationship demonstrates direct variation and identifying the constant of variation. Word problems involving direct variation are also presented and solved step-by-step.
Variation
Here are the steps to solve this joint variation problem:
1) Find the constant of variation k: V = kBh, 24 = k(12)(6), k = 1
2) Use the variation function: V = Bh, 54 = B(9), B = 18 ft^2
The answer is 18 ft^2.
Calibration and validation of analytical instruments
This document discusses the calibration and validation of various analytical instruments used in pharmaceutical analysis. It provides details on calibrating UV-Vis spectrophotometers, IR spectrophotometers, spectrofluorimeters, HPLC, and GC. Calibration ensures instrument readings are accurate against standards, while validation confirms the instrument is correctly installed and operating as intended. The document outlines tests and acceptance criteria for evaluating characteristics like wavelength accuracy, resolution, noise, baseline flatness, sensitivity, flow rate, and linearity during calibration and validation of different analytical instruments.
Presentation on inverse proportion
This document discusses direct and inverse proportions.
Direct proportion means that as one quantity increases, the other increases by the same ratio. They form a straight line graph passing through the origin.
Inverse proportion means that as one quantity increases, the other decreases. The quantities are inversely related by a constant factor. Their relationship forms a curved line graph.
Examples of direct and inverse proportions are provided along with their equation representations and graphs. Key features of these graphs are highlighted.
direct and inverse variations
This document discusses direct and inverse variations. It provides examples of how to set up and solve direct and inverse variation problems. For direct variation, as one variable increases, the other increases at a constant rate. The formula is y=kx, where k is the constant rate of change. For inverse variation, as one variable increases, the other decreases. To solve inverse problems, the formula used is x1y1=x2y2, where x1 and y1 are the known values and x2 is the unknown value being solved for. Examples of setting up and solving both direct and inverse variation word problems are provided.
Direct and inverse variation
This document provides information about direct and inverse variation. It defines direct variation as a relationship where y increases or decreases at a constant rate as x increases. The direct variation formula is provided as y=kx, where k is the constant rate of change. An example problem is worked out step-by-step to find the value of x when y=15 given that y=10 when x=2.4. Inverse variation is then defined as a relationship where as one value increases, the other decreases. The inverse variation formula is provided as x1y1=x2y2. Another example problem is worked out to find the value of y when x=8 given that y=12 when x=2.
Grade 9: Mathematics Unit 3 Variation
This document provides information about a mathematics instructional material for Grade 9 learners in the Philippines. It was developed collaboratively by educators and reviewed by DepEd. The material covers variations, including direct, inverse, joint, and combined variations. It encourages teachers to provide feedback to DepEd to help improve the material. The material aims to help learners understand different types of variations and solve problems involving variations.
4.6 model direct variation day 1
The document discusses direct variation, which is a relationship where two quantities vary proportionally such that their ratio remains constant. The key points are:
1) A direct variation relationship can be expressed as y = ax, where a is the constant of variation.
2) For a relationship to be direct variation, the equation must be able to be rewritten in the form y = ax.
3) A direct variation equation will result in a line passing through the origin when graphed.
Powerpoint variation
Within species, there is usually a great deal of variation between individuals. Variations can be inherited through genes or acquired through environmental factors and experiences over a lifetime. Inherited variations are genetically controlled and cannot be changed, while acquired variations are influenced by activities, nutrition, and environment during one's life. Examples of inherited variations include hair and eye color, while acquired variations include skills, behaviors, and physical characteristics developed over time like tanning or obesity. Both genetic and environmental factors influence many traits exhibiting continuous variation, where there is a range of possible expressions between extremes. Height is an example that depends on both inherited genes and acquired nutrition.
5.3 Direct Variation C
The document discusses direct variation equations and examples of finding the constant of variation and slope from equations relating two variables. It provides examples of identifying the constant of variation from equations, finding the slope, and writing direct variation equations. It also includes an example of graphing a direct variation equation.
Viewers also liked (20)
direct variation grade9 module 3 by mr. joel garcia
direct variation grade9 module 3 by mr. joel garcia
Calibration and validation of analytical instruments
Calibration and validation of analytical instruments
Similar to Presentation1
Error analytical
This document discusses experimental errors in scientific measurements. It defines experimental error as the difference between a measured value and the true value. Experimental errors can be classified as systematic errors or random errors. Systematic errors affect accuracy and can result from faulty instruments, while random errors affect precision and arise from unpredictable fluctuations. The document also discusses ways to quantify and describe experimental errors, including percent error, percent difference, mean, and significant figures. Understanding experimental errors is important for analyzing measurement uncertainties and improving experimental design.
Central tendancy 4
This document discusses different types of data analysis and measures of central tendency. It begins by outlining descriptive and inferential statistics, which provide an overview and measures of how well data supports hypotheses. It then discusses measures of central tendency including the mean, median, and mode. The document provides definitions, formulas, properties and comparisons of each measure. It explains that the mean, median and mode can be related for symmetrical distributions but may differ for asymmetrical distributions. Additional topics covered include weighted mean, geometric mean, harmonic mean, and partitions such as quartiles and deciles.
Reseaech methodology reena
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.
DATA HANDLING UNIT 2 (PART 2).pptx
This document provides an introduction to analyzing experimental errors and data. It discusses evaluating potential sources of errors before, during, and after an analysis. There are two types of experimental errors - determinate errors that affect accuracy and indeterminate errors that affect precision. Determinate errors can be constant or proportional while indeterminate errors are random. The document outlines various sources of these errors and methods to identify and minimize them, such as analyzing samples of different sizes or using reference standards.
VCE Physics: Dealing with numerical measurments
The document discusses experimental data and uncertainty. It explains that all data has some uncertainty due to limitations of instruments and humans. It also discusses accuracy, precision, and significant figures when reporting results. The mean, uncertainty in the mean, and fractional and percentage uncertainties are also covered.
Data analysis
This document discusses various concepts related to errors and accuracy in chemical analysis. It defines different types of errors like gross errors, systematic errors, and random errors. It explains how to classify errors based on their origin and how to minimize different types of errors. The document also covers key statistical concepts like mean, median, standard deviation, normal distribution, precision and accuracy that are important for understanding errors in chemical analysis.
Accuracy precision and significant figures
This document defines accuracy, precision, and significant figures in analytical chemistry.
Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of repeated measurements. A measurement can be precise without being accurate.
There are two methods to determine accuracy - absolute and comparative. The absolute method uses samples of known composition, while the comparative method uses secondary standards. Precision is measured by tests of repeatability and reproducibility.
The number of significant figures indicates the certainty of a measurement and must be considered when performing calculations to avoid losing accuracy. Rules are provided for determining significant figures in addition, subtraction, multiplication, division and other operations.
Characteristics of a good test
chapter 6 of the book "testing language skills from theory to practice" characteristics of a good Test
6 Sigma - Chapter3
This document discusses data, variation, and process capability. It defines two types of data: continuous data which can be measured, and discrete data which can only be observed and counted. It also explains how to measure the location and spread of data using statistics like the mean, median, mode, standard deviation, and range. The document distinguishes between chance variation from random causes and assignable variation from non-random causes. It states that a process is considered statistically controlled when it only exhibits chance variation. Finally, it defines process capability as representing a process's best performance when under statistical control, and describes different indices used to measure potential capability and demonstrated excellence.
Measures of reliability in sports medicine and science
1. The document discusses various measures of reliability that are important when assessing tests and measurements that produce quantitative results.
2. The most important measure of reliability is within-subject variation, also called typical error, which captures the random variability between repeated measurements of the same individual. Having a smaller typical error allows for easier detection of real changes.
3. Other important measures include systematic changes in the mean between trials, and retest correlation, though this can be difficult to interpret. Reasonable precision requires testing at least 50 individuals over 3 or more trials.
Lecture note 2
This document provides an overview of standards of measurement and discusses key concepts:
- Standards are classified as primary or secondary, with primary standards defining fundamental units and secondary standards calibrated against primary standards.
- Standard units discussed include the meter (length), kilogram (mass), second (time), Kelvin (temperature), candela (light intensity), mole (amount of substance), and ampere (electric current).
- Random and systematic errors are defined, with random errors averaging out over repeated measurements but systematic errors requiring correction. Significant figures and calculating relative/absolute errors are also covered.
Practical Work In Biology
This document outlines key concepts for designing and conducting effective biology experiments, including formulating a testable hypothesis, identifying independent and dependent variables, controlling other factors, collecting precise measurements, analyzing sources of error, interpreting results, and drawing valid conclusions. Key aspects are designing experiments to test hypotheses, minimizing random and identifying systematic errors to improve reliability and accuracy, and repeating experiments to verify findings.
Reliability Analysis
Very useful reliability analysis available in this file.Through this you can also enable to explain the reliability of all variables and Reliability should be high for further procedure.
GATHER-AND-TABULATE-THE-RECORDED-DATA.pptx
The document discusses measurement and data collection. It defines key terms like accuracy, precision, absolute error, relative error, sampling, and population. Accuracy refers to how close a measurement is to the accepted value and is expressed as an absolute or relative error. Relative error is calculated as a percentage. Precision refers to the reproducibility of measurements. Sampling involves examining a few samples from a larger population to assess quality or gather data.
What makes a good testA test is considered good” if the .docx
A good test is valid, reliable, job-relevant, and allows for effective decision making. A test is valid if it measures what it claims to measure, and reliability refers to a test's consistency. A test must demonstrate both reliability and validity to be considered a good assessment tool. Reliability is determined by coefficients like Cronbach's alpha, and validity is established through methods like criterion-related, content, and construct validation involving the target population. Test manuals provide information on a test's reliability, validity, appropriate uses and populations.
Statistical analysis in analytical chemistry
This document provides an outline and overview of key concepts related to experimental errors and statistics. It discusses significant figures in calculations, propagation of uncertainty, measures of central tendency and spread, characterizing experimental errors, and treating random errors with statistics. Specific topics covered include calculating uncertainties, confidence intervals, normal distributions, and distinguishing between random and systematic errors. The document uses examples and sample problems to illustrate key points about analyzing and interpreting experimental data.
RELIABILITY.pptx
The document discusses the concept of reliability in measuring instruments. It defines reliability as the consistency of scores or measurements obtained from repeated administrations of a measuring tool. A reliable instrument will yield similar or stable results when used to measure the same object or individual in separate instances. The document outlines different types of reliability, including test-retest, parallel-forms, internal consistency, and inter-rater reliability. It provides examples and methods for estimating reliability, such as calculating correlations between test administrations, form scores, and item-total scores. Maintaining high reliability is important for ensuring measuring tools provide accurate and reproducible measurements.
Accuracy-and-precision.ppt
John's measurement is closer to the accepted value of 2.7 g/ml.
John's measurement: 2.649 g/ml
Difference from accepted value: 2.7 - 2.649 = 0.051 g/ml
Percentage accuracy = (Difference/Accepted value) x 100% = (0.051/2.7) x 100% = 1.89%
Sally's measurement: 2.731 g/ml
Difference from accepted value: 2.7 - 2.731 = -0.031 g/ml
Percentage accuracy = (Difference/Accepted value) x 100% = (-0.031/2.7) x 100% = 1.15%
Therefore
Error.pptx
As per Pharmacy council of India syllabus of B.Pharmacy, First year, semester first this PPT covers point from unit I
Similar to Presentation1 (20)
Measures of reliability in sports medicine and science
Measures of reliability in sports medicine and science
What makes a good testA test is considered good” if the .docx
What makes a good testA test is considered good” if the .docx
Recently uploaded
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
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 Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
How to Setup Warehouse & Location in Odoo 17 Inventory
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.
The Diamonds of 2023-2024 in the IGRA collection
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.
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Fix the Import Error in the Odoo 17
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
DRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
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.
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Recently uploaded (20)
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Presentation1
- 1. DEFINATION OF REPEATABILITY Repeatability or test-retest reliability is the variation in measurements taken by a single person or instrument on the same item and under the same conditions. A less-than-perfect test-retest reliability causes test-retest variability. Such variability can be caused by, for example, intra-individual variability and intra-observer variability. A measurement may be said to be repeatable when this variation is smaller than some agreed limit.
- 2. HOW REPEATABILITY MEASUREMENT ERROR 1. various reasons can cause errors, which are a problem with the equipment, the reading problem with the equipment, and many more. 2. in cause to avoid the error, the reading at the same place over and over again to be taken to get the average range. 3. to get the average range, the value should be in the same place and with the same tools on the table last post.
- 3. HOW TO ELIMINATE REPEATABILITY 1. Calculate the mean or average of your data. To do this, add your measurements and divide by the number of elements in your sample. For example, if your measurements are 1, 2, 2, 3, 2, the average would be 2 -- 10 divided by 5. 2. Calculate the variance of your data. To do this, calculate the difference of each result from the mean, square each result and work out the average. For example, if the difference from the mean in a set of data were 1, 2, -2 and -1, the variance would be 2.5. That is, the average of 1, 4, 4 and 1. 3. Calculate the square root of the variance. This is the repeatability standard deviation of your data. Following our previous example, the standard deviation would be 1.5811.
- 4. IMPORTANT OF REAPITABILITY 1. It is important to get a reading repeatibility, this is to get the precises value. 2. important to take readings 3 times or more to get the average range to avoid the error because there is no tool that can provide accurate readings over and over again.
- 5. CONCLUSION Repeatability is important in every measurement work, it is to get the precises value to avoid any problem, because, a tools cannot give a precises value in every single calculation