Interprertation of statistics
Download as pptx, pdf0 likes145 views
This document discusses statistical concepts like variability, standard deviation, confidence intervals, p-values, and distributions. It provides examples to illustrate variability and how it is important when evaluating investments. It also includes tables showing output from statistical tests run on multiple variables, including sample sizes, means, standard deviations, and confidence intervals. The conclusion emphasizes the relationships between sample size, mean differences, and p-values in determining confidence intervals and statistical significance.
1 of 24
Download to read offline
Ad
Recommended
Biostatistics i
Biostatistics iThangamani Ramalingam The document discusses concepts related to measurement, scaling, and data analysis in statistics. It defines measurement as assigning numbers or symbols to characteristics of interest according to set rules. There are different types of scales (nominal, ordinal, interval, ratio) that provide varying levels of information based on their scaling properties of description, order, distance, and origin. The document also discusses topics like descriptive statistics, types of data collection and processing, methods of data presentation, and measures of relationships between variables like correlation and regression.
Statistical measures
Statistical measureslisawhipp This document defines and provides examples of statistical measures used to describe data, including measures of central tendency (mean, median, mode) and measures of variation (range, variance, standard deviation). It explains that the mean is the average value, the median is the middle number, and the mode is the most frequent value. The best measure of central tendency to use depends on whether the data is spread out or clustered around certain values. Measures of variation describe how spread out the data is, with the range being the difference between highest and lowest values, and the variance and standard deviation measuring differences from the mean. Step-by-step processes are provided for calculating these statistical measures.
Measures of dispersion
Measures of dispersionGnana Sravani This document discusses various measures of dispersion, which refer to how spread out or varied a set of data is from a central value. It describes standard deviation, which quantifies how far data points deviate from the mean on average. A lower standard deviation indicates less volatility or risk. The coefficient of variation allows comparison of volatility relative to expected returns. The range is the maximum minus minimum value but can be skewed by outliers, while the interquartile range ignores the highest and lowest quartiles to better represent the middle data.
Lesson 7 measures of dispersion part 2
Lesson 7 measures of dispersion part 2nurun2010 This document provides an introduction and overview of key concepts related to measures of variability and dispersion in statistics. It discusses average absolute deviation, which is the mean of the absolute values of deviations from the data's mean or median. It then covers standard deviation, which is the positive square root of the average of squared deviations from the mean, and is the most widely used measure of dispersion. Formulas and steps for calculating average absolute deviation and standard deviation are provided. An example of calculating standard deviation using a data set of ages is worked through.
Properties of Standard Deviation
Properties of Standard DeviationRizwan Sharif The document discusses standard deviation and its properties. Standard deviation is a measure of how spread out numbers are from the average (mean) value. It is always non-negative and can be impacted by outliers. A low standard deviation means values are close to the mean, while a high standard deviation means values are more spread out. Standard deviation can be used to calculate what percentage of data falls within certain intervals from the mean when data is normally distributed.
3.2 measures of variation
3.2 measures of variationleblance This document discusses measures of variation used to assess how far data points are from the average or mean. It defines key terms like range, variance, and standard deviation. Variance measures the mathematical dispersion of data relative to the mean, while standard deviation gives a value in the original units of measurement, making it easier to interpret. Formulas are provided for calculating sample variance and standard deviation versus population variance and standard deviation. Chebyshev's Theorem is introduced, stating that a certain minimum percentage of data must fall within a specified number of standard deviations of the mean. An example applies these concepts.
Measures of Dispersion
Measures of DispersionShariful Haque Robin If everything were the same, we would have no need of statistics. But, people's heights, ages, etc., do vary. We often need to measure the extent to which scores in a dataset differ from each other. Such a measure is called the dispersion of a distribution.
Statr sessions 4 to 6
Statr sessions 4 to 6Ruru Chowdhury The document discusses various measures of variability that can be used to describe the spread or dispersion of data, including the range, interquartile range, mean absolute deviation, variance, standard deviation, and coefficient of variation. It also covers how to calculate and interpret these measures of variability for both ungrouped and grouped data. Various other concepts are introduced such as the empirical rule, z-scores, skewness, the 5-number summary, and how to construct and interpret a box-and-whisker plot.
Measures of dispersion by Prof Najeeb Memon BMC lumhs jamshoro
Measures of dispersion by Prof Najeeb Memon BMC lumhs jamshoromuhammed najeeb This document discusses different measures of dispersion used to describe how data values are spread around the mean. It defines range as the difference between the highest and lowest values. Mean deviation is the average of the deviations from the mean. Variance is the average of the squared differences from the mean, while standard deviation is the square root of the variance. These measures, especially variance and standard deviation, are useful ways to quantify the variability in data values and how far they are from the mean. Understanding dispersion is important for making inferences from data patterns.
Measure of dispersion
Measure of dispersionSana butt Mean deviation is a measure of dispersion that calculates the average distance of data points from the central value (usually the mean). It is calculated by taking the absolute value of differences between each data point and the mean, then finding the average of those absolute differences. For a sample of data, the formula is the sum of the absolute values of (x - x-bar) divided by n. Mean deviation indicates how far data points typically are from the central value. It is useful for business applications but can be affected by outliers and does not retain positive and negative signs.
Measures of dispersion
Measures of dispersionNilanjan Bhaumik The document discusses various measures of central tendency, dispersion, and shape used to describe data numerically. It defines terms like mean, median, mode, variance, standard deviation, coefficient of variation, range, interquartile range, skewness, and quartiles. It provides formulas and examples of how to calculate these measures from data sets. The document also discusses concepts like normal distribution, empirical rule, and how measures of central tendency and dispersion do not provide information about the shape or symmetry of a distribution.
Propteties of Standard Deviation
Propteties of Standard DeviationSahil Jindal Standard deviation is a measure of how dispersed data points are from the average value. It is calculated by taking the square root of the variance, which is the average of the squared distances from the mean. For a set of egg weights, the standard deviation is calculated by first finding the mean, then determining the variance by taking the sum of the squared differences from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means values are more spread out. Standard deviation is not affected by adding or subtracting a constant from all values, but is affected by multiplying or dividing all values by a constant.
Dispersion
DispersionVimarsh Padha Measures of dispersion describe how similar or spread out scores in a data set are. The three main measures are range, semi-interquartile range (SIR), and variance/standard deviation. Range is the difference between the highest and lowest scores. SIR is less affected by outliers than range. Variance/standard deviation quantify the average squared distance of scores from the mean, with larger numbers indicating more spread out data. Skew and kurtosis further describe the shape of distributions.
Measures of Dispersion
Measures of DispersionMohit Mahajan This pused to find measures of dispersion.pt tells us what dispersion is, need of dispersion and the different methods
Dscriptive statistics
Dscriptive statisticsDr. Anees Alyafei This document discusses descriptive statistics used to summarize continuous data, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation, variance, range, interquartile range), and the normal distribution curve. It provides definitions and examples of how to calculate each measure. The key measures are the mean as the average, the median as the middle value, and the mode as the most frequent value. Standard deviation and variance quantify how spread out values are from the mean. Choosing the appropriate central tendency and dispersion measures depends on whether the data distribution is normal/symmetrical or skewed.
Ds vs Is discuss 3.1
Ds vs Is discuss 3.1Makati Science High School This document discusses descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, and summary statistics like the mean, median, mode, variance, and standard deviation. Inferential statistics allow generalization from samples to populations through hypothesis testing, where the null hypothesis is tested against the alternative hypothesis. Type I and type II errors are possible, and significance tests control the probability of type I errors through the alpha level while power analysis aims to reduce type II errors. Common inferential tests mentioned include t-tests, ANOVA, and meta-analysis.
MEASURE OF DISPERSION
MEASURE OF DISPERSIONHasnan Naveed This document discusses various measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. It provides formulas and steps to calculate each measure. The standard deviation is described as the most common measure of dispersion, as it takes into account every observation in the data and measures the average deviation from the mean. The coefficient of variation is also introduced as a relative measure of variability, expressed as a percentage.
Mean Deviation
Mean DeviationCarlo Luna This document defines and provides the formula for calculating mean deviation, which is a measure of variation that uses all the scores in a distribution. It is more reliable than range. Mean deviation is calculated by finding the absolute difference between each score and the mean, summing the absolute differences, and dividing by the number of observations. Two examples of calculating mean deviation for sets of data are provided, along with exercises asking students to find the mean deviation of additional data sets and define standard deviation.
Dispersion
DispersionDevendra Singh Rathod Introduction of Dispersion, measures, ranges, quartile deviation, mean deviation, standard deviation,
Measure of Dispersion
Measure of DispersionRyan Pineda Measures of dispersion describe how spread out or clustered the data is. The range is the difference between the highest and lowest values. Standard deviation measures how far each value deviates from the mean by finding the deviations, squaring them, summing them, dividing by n-1, and taking the square root. It accounts for all data points unlike the range. To calculate standard deviation, first find the mean, then deviations from it, square those values, sum them, and divide the sum by n-1 before taking the square root.
Mba i qt unit-2.1_measures of variations
Mba i qt unit-2.1_measures of variationsRai University This document discusses various measures of variability or variation used in statistics. It defines variability as the extent to which observations vary from each other or from the average. The key measures discussed are range, interquartile range, average deviation, and standard deviation. Range is the simplest but ignores the distribution within its limits. Interquartile range excludes outliers but also ignores half the data. Average deviation measures average distance from the mean/median and indicates how compact 50% of data is. Examples are provided to demonstrate calculating and comparing these measures.
Measures of dispersion
Measures of dispersionMayuri Joshi This document discusses measures of dispersion, which indicate how spread out or variable a set of data is. There are three main measures: the range, which is the difference between the highest and lowest values; the semi-interquartile range (SIR), which is the difference between the first and third quartiles divided by two; and variance/standard deviation. Variance is the average of the squared deviations from the mean, while standard deviation is the square root of the variance. These measures provide summaries of how concentrated or dispersed the observed values are from the average or expected value.
Measures of dispersion
Measures of dispersionSanoj Fernando This document discusses measures of dispersion, which indicate how spread out or variable observed data values are around a central measure like the mean. It describes two main types of dispersion measures: range measures, which consider the distance between extreme values, and deviation measures, which consider the average distance of values from the mean. Specific measures discussed include the range, interquartile range, variance, and standard deviation. The standard deviation is presented as the most commonly used measure because it is easily interpreted in the original units of measurement and captures how far values typically deviate from the mean.
Measures of-central-tendency-dispersion
Measures of-central-tendency-dispersionSanoj Fernando This document discusses measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation). It explains how to calculate each measure and their strengths and weaknesses. For example, the mean is more sensitive than the median but can be skewed by outliers, while the median is not affected by outliers but is less sensitive. The document also provides examples of calculating and interpreting the mean, range, and standard deviation using sample data.
Measures of Variation or Dispersion 
Measures of Variation or Dispersion Dr Athar Khan Dr Athar Khan
Associate Professor
Liaquat College of Medicine & Dentistry
Karachi, Pakistan 2019
matharm@yahoo.com
2. measures of dis[persion
2. measures of dis[persionKaran Kukreja This document discusses various measures of dispersion used to quantify how spread out or clustered data values are around a central tendency. It defines key terms like range, variance, standard deviation, and coefficient of variation. Examples are provided to demonstrate how to calculate these measures for both individual and grouped data. The normal distribution curve is also discussed to show how dispersion relates to the percentage of values that fall within a given number of standard deviations from the mean.
Statistics report THE RANGE
Statistics report THE RANGECharliez Jane Soriano The range is the simplest measure of variability, defined as the difference between the highest and lowest values in a data set. It is quick to calculate but does not provide a full picture of the data distribution and can be strongly influenced by outliers. Other measures of variability include the average deviation, which calculates the average amount each score deviates from the mean, and the interquartile range, which is less influenced by outliers than the range. The interquartile range only considers data between the first and third quartiles and ignores half the data points.
Bbs10 ppt ch03
Bbs10 ppt ch03Anwar Afridi This document provides an overview of key concepts in descriptive statistics that are covered in Chapter 3, including measures of central tendency, variation, and shape. It introduces the mean, median, mode, variance, standard deviation, range, interquartile range, and coefficient of variation as common statistical measures used to describe the properties of numerical data. Examples are given to demonstrate how to calculate and interpret these descriptive statistics. The chapter aims to help readers learn how to calculate summary measures for a population and construct graphical displays like box-and-whisker plots.
Semana9 ic
Semana9 icJorge Obando This document discusses interval of confidence and how to calculate it. It defines an interval of confidence as a range of values within which the true population value is likely to fall. It provides an example of how to calculate the limits of an 80% confidence interval using z-scores from a standard normal distribution table. The document also shows how to calculate the confidence interval mathematically using the standard error of the mean. Finally, it provides two examples of calculating confidence intervals for sample means.
Olah data
Olah datasuprastiyo The document contains analysis of correlations between various variables (X1.1, X1.2, etc.) and customer satisfaction (Kepuasan_Konsumen). It finds several variables to be highly correlated with customer satisfaction at a 0.01 significance level, including empathy, reliability, tangibles and location. A regression model was created to predict customer satisfaction finding a high R square of 0.925, with empathy having the strongest individual correlation. The analysis thus aims to understand the key drivers of customer satisfaction through correlational analysis and regression modeling of various service quality variables.
Ad
More Related Content
What's hot (20)
Measures of dispersion by Prof Najeeb Memon BMC lumhs jamshoro
Measures of dispersion by Prof Najeeb Memon BMC lumhs jamshoromuhammed najeeb This document discusses different measures of dispersion used to describe how data values are spread around the mean. It defines range as the difference between the highest and lowest values. Mean deviation is the average of the deviations from the mean. Variance is the average of the squared differences from the mean, while standard deviation is the square root of the variance. These measures, especially variance and standard deviation, are useful ways to quantify the variability in data values and how far they are from the mean. Understanding dispersion is important for making inferences from data patterns.
Measure of dispersion
Measure of dispersionSana butt Mean deviation is a measure of dispersion that calculates the average distance of data points from the central value (usually the mean). It is calculated by taking the absolute value of differences between each data point and the mean, then finding the average of those absolute differences. For a sample of data, the formula is the sum of the absolute values of (x - x-bar) divided by n. Mean deviation indicates how far data points typically are from the central value. It is useful for business applications but can be affected by outliers and does not retain positive and negative signs.
Measures of dispersion
Measures of dispersionNilanjan Bhaumik The document discusses various measures of central tendency, dispersion, and shape used to describe data numerically. It defines terms like mean, median, mode, variance, standard deviation, coefficient of variation, range, interquartile range, skewness, and quartiles. It provides formulas and examples of how to calculate these measures from data sets. The document also discusses concepts like normal distribution, empirical rule, and how measures of central tendency and dispersion do not provide information about the shape or symmetry of a distribution.
Propteties of Standard Deviation
Propteties of Standard DeviationSahil Jindal Standard deviation is a measure of how dispersed data points are from the average value. It is calculated by taking the square root of the variance, which is the average of the squared distances from the mean. For a set of egg weights, the standard deviation is calculated by first finding the mean, then determining the variance by taking the sum of the squared differences from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means values are more spread out. Standard deviation is not affected by adding or subtracting a constant from all values, but is affected by multiplying or dividing all values by a constant.
Dispersion
DispersionVimarsh Padha Measures of dispersion describe how similar or spread out scores in a data set are. The three main measures are range, semi-interquartile range (SIR), and variance/standard deviation. Range is the difference between the highest and lowest scores. SIR is less affected by outliers than range. Variance/standard deviation quantify the average squared distance of scores from the mean, with larger numbers indicating more spread out data. Skew and kurtosis further describe the shape of distributions.
Measures of Dispersion
Measures of DispersionMohit Mahajan This pused to find measures of dispersion.pt tells us what dispersion is, need of dispersion and the different methods
Dscriptive statistics
Dscriptive statisticsDr. Anees Alyafei This document discusses descriptive statistics used to summarize continuous data, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation, variance, range, interquartile range), and the normal distribution curve. It provides definitions and examples of how to calculate each measure. The key measures are the mean as the average, the median as the middle value, and the mode as the most frequent value. Standard deviation and variance quantify how spread out values are from the mean. Choosing the appropriate central tendency and dispersion measures depends on whether the data distribution is normal/symmetrical or skewed.
Ds vs Is discuss 3.1
Ds vs Is discuss 3.1Makati Science High School This document discusses descriptive and inferential statistics. Descriptive statistics summarize and organize data through frequency distributions, graphs, and summary statistics like the mean, median, mode, variance, and standard deviation. Inferential statistics allow generalization from samples to populations through hypothesis testing, where the null hypothesis is tested against the alternative hypothesis. Type I and type II errors are possible, and significance tests control the probability of type I errors through the alpha level while power analysis aims to reduce type II errors. Common inferential tests mentioned include t-tests, ANOVA, and meta-analysis.
MEASURE OF DISPERSION
MEASURE OF DISPERSIONHasnan Naveed This document discusses various measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. It provides formulas and steps to calculate each measure. The standard deviation is described as the most common measure of dispersion, as it takes into account every observation in the data and measures the average deviation from the mean. The coefficient of variation is also introduced as a relative measure of variability, expressed as a percentage.
Mean Deviation
Mean DeviationCarlo Luna This document defines and provides the formula for calculating mean deviation, which is a measure of variation that uses all the scores in a distribution. It is more reliable than range. Mean deviation is calculated by finding the absolute difference between each score and the mean, summing the absolute differences, and dividing by the number of observations. Two examples of calculating mean deviation for sets of data are provided, along with exercises asking students to find the mean deviation of additional data sets and define standard deviation.
Dispersion
DispersionDevendra Singh Rathod Introduction of Dispersion, measures, ranges, quartile deviation, mean deviation, standard deviation,
Measure of Dispersion
Measure of DispersionRyan Pineda Measures of dispersion describe how spread out or clustered the data is. The range is the difference between the highest and lowest values. Standard deviation measures how far each value deviates from the mean by finding the deviations, squaring them, summing them, dividing by n-1, and taking the square root. It accounts for all data points unlike the range. To calculate standard deviation, first find the mean, then deviations from it, square those values, sum them, and divide the sum by n-1 before taking the square root.
Mba i qt unit-2.1_measures of variations
Mba i qt unit-2.1_measures of variationsRai University This document discusses various measures of variability or variation used in statistics. It defines variability as the extent to which observations vary from each other or from the average. The key measures discussed are range, interquartile range, average deviation, and standard deviation. Range is the simplest but ignores the distribution within its limits. Interquartile range excludes outliers but also ignores half the data. Average deviation measures average distance from the mean/median and indicates how compact 50% of data is. Examples are provided to demonstrate calculating and comparing these measures.
Measures of dispersion
Measures of dispersionMayuri Joshi This document discusses measures of dispersion, which indicate how spread out or variable a set of data is. There are three main measures: the range, which is the difference between the highest and lowest values; the semi-interquartile range (SIR), which is the difference between the first and third quartiles divided by two; and variance/standard deviation. Variance is the average of the squared deviations from the mean, while standard deviation is the square root of the variance. These measures provide summaries of how concentrated or dispersed the observed values are from the average or expected value.
Measures of dispersion
Measures of dispersionSanoj Fernando This document discusses measures of dispersion, which indicate how spread out or variable observed data values are around a central measure like the mean. It describes two main types of dispersion measures: range measures, which consider the distance between extreme values, and deviation measures, which consider the average distance of values from the mean. Specific measures discussed include the range, interquartile range, variance, and standard deviation. The standard deviation is presented as the most commonly used measure because it is easily interpreted in the original units of measurement and captures how far values typically deviate from the mean.
Measures of-central-tendency-dispersion
Measures of-central-tendency-dispersionSanoj Fernando This document discusses measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation). It explains how to calculate each measure and their strengths and weaknesses. For example, the mean is more sensitive than the median but can be skewed by outliers, while the median is not affected by outliers but is less sensitive. The document also provides examples of calculating and interpreting the mean, range, and standard deviation using sample data.
Measures of Variation or Dispersion
Measures of Variation or Dispersion Dr Athar Khan Dr Athar Khan
Associate Professor
Liaquat College of Medicine & Dentistry
Karachi, Pakistan 2019
matharm@yahoo.com
2. measures of dis[persion
2. measures of dis[persionKaran Kukreja This document discusses various measures of dispersion used to quantify how spread out or clustered data values are around a central tendency. It defines key terms like range, variance, standard deviation, and coefficient of variation. Examples are provided to demonstrate how to calculate these measures for both individual and grouped data. The normal distribution curve is also discussed to show how dispersion relates to the percentage of values that fall within a given number of standard deviations from the mean.
Statistics report THE RANGE
Statistics report THE RANGECharliez Jane Soriano The range is the simplest measure of variability, defined as the difference between the highest and lowest values in a data set. It is quick to calculate but does not provide a full picture of the data distribution and can be strongly influenced by outliers. Other measures of variability include the average deviation, which calculates the average amount each score deviates from the mean, and the interquartile range, which is less influenced by outliers than the range. The interquartile range only considers data between the first and third quartiles and ignores half the data points.
Bbs10 ppt ch03
Bbs10 ppt ch03Anwar Afridi This document provides an overview of key concepts in descriptive statistics that are covered in Chapter 3, including measures of central tendency, variation, and shape. It introduces the mean, median, mode, variance, standard deviation, range, interquartile range, and coefficient of variation as common statistical measures used to describe the properties of numerical data. Examples are given to demonstrate how to calculate and interpret these descriptive statistics. The chapter aims to help readers learn how to calculate summary measures for a population and construct graphical displays like box-and-whisker plots.
Similar to Interprertation of statistics (20)
Semana9 ic
Semana9 icJorge Obando This document discusses interval of confidence and how to calculate it. It defines an interval of confidence as a range of values within which the true population value is likely to fall. It provides an example of how to calculate the limits of an 80% confidence interval using z-scores from a standard normal distribution table. The document also shows how to calculate the confidence interval mathematically using the standard error of the mean. Finally, it provides two examples of calculating confidence intervals for sample means.
Olah data
Olah datasuprastiyo The document contains analysis of correlations between various variables (X1.1, X1.2, etc.) and customer satisfaction (Kepuasan_Konsumen). It finds several variables to be highly correlated with customer satisfaction at a 0.01 significance level, including empathy, reliability, tangibles and location. A regression model was created to predict customer satisfaction finding a high R square of 0.925, with empathy having the strongest individual correlation. The analysis thus aims to understand the key drivers of customer satisfaction through correlational analysis and regression modeling of various service quality variables.
3.2 Measures of variation
3.2 Measures of variationLong Beach City College This document summarizes chapter 3 section 2 of an elementary statistics textbook. It discusses measures of variation, including range, variance, and standard deviation. The standard deviation describes how spread out data values are from the mean and is used to determine consistency and predictability within a specified interval. Several examples demonstrate calculating range, variance, and standard deviation for data sets. Chebyshev's theorem and the empirical rule relate standard deviations to the percentage of values that fall within certain intervals of the mean.
Lyn
LynMichael Calica The document reports descriptive statistics for two groups ("sixtypercent" and "fiftypercent") across multiple trials. It provides the number of observations, minimum and maximum values, mean, and standard deviation for each group in each trial. One-sample t-tests are also reported comparing the mean of each group to a test value of 0 for each trial.
Dispersion 2
Dispersion 2Khadiza Begum The document discusses variance and standard deviation. Variance measures how dispersed or spread out values are from the mean, while standard deviation is the positive square root of variance. Standard deviation indicates the average amount of variation from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means more variation and dispersion from the mean. The coefficient of variation measures standard deviation relative to the mean and is used to compare the variability of different data sets even if the means are different.
Estimating a Population Standard Deviation or Variance
Estimating a Population Standard Deviation or VarianceLong Beach City College Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
Estimating a Population Standard Deviation or Variance 
Estimating a Population Standard Deviation or Variance Long Beach City College Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
Business Analytics _ Confidence Interval
Business Analytics _ Confidence IntervalRavindra Nath Shukla This document outlines key concepts related to constructing confidence intervals for estimating population means and proportions. It discusses how to calculate confidence intervals when the population standard deviation is known or unknown. Specifically, it provides the formulas and assumptions for constructing confidence intervals for a population mean using the normal and t-distributions. It also outlines how to calculate confidence intervals for a population proportion using the normal approximation. Examples are provided to demonstrate how to construct 95% confidence intervals for a mean and proportion based on sample data.
Measures of Variation 
Measures of Variation Long Beach City College Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.2: Measures of Variation
Quantitative Analysis for Emperical Research
Quantitative Analysis for Emperical ResearchAmit Kamble Overview for Approach Methods for quantitative analysis; which includes
1) Planning of Experiments
2) Data Generation
3) presentation of report
some numerical approach methods; data modeling; hypothesis methods
DESCRIBING VARIABILITY.pptx
DESCRIBING VARIABILITY.pptxSHAIKNIZMIMtech2021V This document discusses different measures of variability that can be used to describe how data values vary in a data set. It describes the range, interquartile range, variance, and standard deviation. The range is the difference between the highest and lowest values, but it is affected by outliers. The interquartile range describes the middle half of the data and is more robust to outliers. Variance and standard deviation measure how far data points deviate from the mean in a data set, on average, but they are also affected by outliers. The document provides examples of calculating these measures using a data set of driving speeds.
08 ch ken black solution
08 ch ken black solutionKrunal Shah This document provides an overview of Chapter 8 in a statistics textbook. The chapter covers statistical inference for estimating parameters of single populations, including: point and interval estimation, estimating the population mean when the standard deviation is known or unknown, estimating the population proportion, estimating the population variance, and estimating sample size. Key concepts introduced include confidence intervals, the t-distribution, chi-square distribution, and determining necessary sample size. The chapter outline and learning objectives are also summarized.
Normal distribution - Unitedworld School of Business
Normal distribution - Unitedworld School of BusinessArnab Roy Chowdhury The document discusses the normal distribution and standard deviation. Some key points:
1. The normal distribution is the most common continuous probability distribution and is characterized by a mean (μ) and standard deviation (σ). It is graphically represented by a normal curve.
2. The standard deviation is a measure of how spread out values are from the mean. About 68% of values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99% within 3 standard deviations.
3. Standard deviation has various interpretations including a measure of variation in a population or a process's ability to meet requirements. It enables determining where values are located in relation to the mean with accuracy.
Presentation1group b
Presentation1group bAbhishekDas15 This document discusses various statistical concepts such as measures of central tendency, measures of dispersion, standard deviation, normal distribution, and tests of significance. It provides examples and formulas to calculate range, mean deviation, standard deviation, z-scores, confidence intervals, and the chi-square test. Biological variation is common and various statistical methods can be used to analyze data and find patterns in large datasets.
Six sigma statistics
Six sigma statisticsShankaran Rd This document provides an overview and examples of various statistical concepts and tools, including:
- Useful statistical measures such as mean, median, mode, range, variance, and standard deviation.
- The normal distribution and how to calculate proportions of values that fall within a certain range using normal distribution tables or Excel functions.
- Common values from the normal distribution such as what proportion of values fall within 1, 2, or 3 standard deviations of the mean.
- Six Sigma "sigma values" and how they correspond to defects per million opportunities.
- Visualization tools like histograms, Pareto charts, stem-and-leaf plots, scatter graphs, multi-vari charts, and box plots; including
Normal distribution 
Normal distribution Unitedworld School Of Business The document discusses standard deviation and the normal distribution. Some key points:
- Standard deviation is a measure of how spread out values are from the mean. It is the square root of the variance.
- For a normal distribution, approximately 68% of values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99% within 3 standard deviations.
- The normal distribution is the most common continuous probability distribution and is important because many variables tend toward a normal distribution as the number of trials increases. It is used in statistical quality control.
Measures of Dispersion .pptx
Measures of Dispersion .pptxVishal543707 This document discusses various measures of dispersion used to describe the spread or variability in a data set. It describes absolute measures of dispersion, such as range and mean deviation, which indicate the amount of variation, and relative measures like the coefficient of variation, which indicate the degree of variation accounting for different scales. Common measures discussed include range, variance, standard deviation, coefficient of variation, skewness and kurtosis. Formulas are provided for calculating many of these dispersion statistics.
Quantitative Methods for Lawyers - Class #7 - Probability & Basic Statistics ...
Quantitative Methods for Lawyers - Class #7 - Probability & Basic Statistics ...Daniel Katz Quantitative Methods for Lawyers - Class #7 - Probability & Basic Statistics (Part II) - Professor Daniel Martin Katz
ANACHEM LAB REPORT PRESENTATION G5.pptx
ANACHEM LAB REPORT PRESENTATION G5.pptxbaeaeyah This experiment analyzed the weight variations of Philippine one-peso coins using statistical analysis. Ten coins were measured and their weights were analyzed to calculate statistical parameters such as mean, median, standard deviation, and confidence intervals. The results showed that the mean weights were 5.98g for Set A and 5.99g for Set B. The Q-test results indicated that neither set had significant outliers, so the data was reliable for further statistical analysis.
Ad
More from Thangamani Ramalingam (20)
Research made easy
Research made easyThangamani Ramalingam This document provides an overview of research in physiotherapy and the research process. It discusses why research is important, different levels of evidence, and the overall research process. Key aspects of the research process that are covered include formulating the research problem, reviewing relevant literature, research design, data collection and analysis, and reporting results. Different types of research designs are explained including descriptive research, exploratory research, explanatory research, and clinical study designs. Guidance is also provided on writing research proposals, conducting pilot studies, and writing research reports.
Tips and tricks of writing and publishing
Tips and tricks of writing and publishingThangamani Ramalingam This document provides information on publishing a paper, including indexing journals, citation indexing services, finding an appropriate journal, preparing the paper using IMRD format and following author instructions, submitting the paper online, undergoing peer review, revising if needed, proofing, tracking the publication process, and promoting and sharing the published paper. Indexing and citation indexing help reflect the quality and impact of a journal.
Research article 
Research article Thangamani Ramalingam This document discusses research in physiotherapy and evidence-based practice. It addresses why research is important, different types of research paradigms and evidence. It notes that physiotherapists are often unaware of research designs and problems, and fail to quantify intervention effects. The document lists several databases and resources for finding evidence, and provides tips for creating keywords and following reporting guidelines when writing research papers.
Methods in reserch article
Methods in reserch articleThangamani Ramalingam This document outlines different research methods and designs. It discusses key parts of research including research designs, sampling design, observational design, and statistical analysis. It covers types of research like descriptive, exploratory, and explanatory research. Sampling probability and non-probability sampling are discussed. Observational design focuses on outcome measures, measurement errors, and data collection. Statistical design depends on study design, data type, sample size, and software. Clinical research designs covered include case series, case control studies, cross sectional studies, cohort studies, and experimental studies. Examples are provided.
Data collection reliability
Data collection reliabilityThangamani Ramalingam The document discusses reliability and validity in data collection. It defines reliability as the consistency of measurements and validity as the degree to which a measure accurately represents what it intends to measure. It describes different types of reliability, including internal consistency, test-retest reliability, and inter-rater reliability. It also discusses different types of validity, such as face validity, content validity, criterion-related validity, and construct validity. The document emphasizes that reliability and validity are both important for accurate and consistent research results.
Biostatistics ii
Biostatistics iiThangamani Ramalingam This document discusses various statistical tests that can be used to analyze data, including parametric and nonparametric tests. It provides examples of tests for comparing means from one group, two independent groups, two related groups, and multiple groups. These include t-tests, ANOVA, correlation, regression, chi-square tests, and nonparametric alternatives like the Wilcoxon and Kruskal-Wallis tests. It also discusses assumptions of parametric versus nonparametric tests and selecting the appropriate test based on the scale of the data.
Statistical tests 
Statistical tests Thangamani Ramalingam This document provides an overview of statistical tests and hypothesis testing. It discusses the four steps of hypothesis testing, including stating hypotheses, setting decision criteria, computing test statistics, and making a decision. It also describes different types of statistical analyses, common descriptive statistics, and forms of statistical relationships. Finally, it provides examples of various parametric and nonparametric statistical tests, including t-tests, ANOVA, chi-square tests, correlation, regression, and decision trees.
chronic pain management
chronic pain managementThangamani Ramalingam This document introduces a new comprehensive chronic pain management (CCPM) model. CCPM is a patient-centered, integrative and holistic approach that combines biomedical and psychosocial treatment modalities. It aims to educate patients and help reduce pain sensitivity through neurophysiological education, motivational interviews, exercise principles, thought modification, distress management and building self-efficacy. CCPM represents a paradigm shift from a reductionist to a transformative approach focusing on the whole person. The goal is to provide comprehensive care for chronic pain characterized by central sensitization.
Pro ms
Pro msThangamani Ramalingam This document discusses Patient Reported Outcome Measures (PROMs). It defines PROMs as measurement instruments that provide information on a patient's health status, experience with treatment, and quality of life directly from the patient without interpretation. The document outlines the objectives of PROMs, which are to improve healthcare quality and outcomes from the patient perspective. It describes different types of PROMs, who can use them, how to select them, how they are administered, when they should be used, and barriers to their use.
Research design
Research designThangamani Ramalingam This document provides an overview of research design in physiotherapy. It discusses why research is important, current scenarios in physiotherapy research, and levels of evidence. The main components of research design are described, including type of research, sampling design, observational design, statistical design, and operational design. The objectives are to move from information to knowledge and create empirical evidence rather than relying on anecdotal reasoning. Good research design can have an impact by answering questions and establishing the effectiveness of interventions through logical and quantitative methods.
Qualitative research design
Qualitative research designThangamani Ramalingam This document outlines the objectives and key aspects of qualitative research design. It discusses qualitative statement, types of qualitative designs including phenomenology, grounded theory, case study, narrative synthesis, ethnography, historical research and action research. It also covers sampling design, observational design, operational design, data analysis design and the differences between qualitative and quantitative designs. Key points covered include that qualitative research aims to understand and describe central experiences or processes for participants through methods like interviews and observations. Different qualitative designs have different focuses such as experiences for phenomenology or groups for ethnography. The document provides examples of studies for each design type.
METHODOLOGY IN RESEARCH
METHODOLOGY IN RESEARCHThangamani Ramalingam This document provides information about A.THANGAMANI RAMALINGAM, including his contact details and areas of expertise. It then outlines the structure and key aspects of methodology in research, including research design, sample design, observational design, and statistical design. Finally, it discusses common signs of dubious data and misuses of statistics.
UNDERSTANDING A RESEARCH ARTICLE
UNDERSTANDING A RESEARCH ARTICLEThangamani Ramalingam This document discusses various components of medical research articles and evaluating evidence from research. It begins by describing different types of articles such as original research, case reports, reviews, and editorials. It then discusses levels of evidence, with randomized controlled trials and systematic reviews considered the strongest levels. The document outlines how to read articles, including preparing, understanding different sections, and evaluating conclusions. It concludes by discussing signs of dubious data, such as misleading graphs, broad definitions, and convenient time frames.
CBT
CBTThangamani Ramalingam Cognitive behavioural therapy (CBT) leads to significant improvements in functioning and quality of life for chronic pain conditions like low back pain. Several studies show CBT is as effective or more effective than other therapies or medications for issues like reducing catastrophizing thoughts, pain levels, and disability. While evidence is limited, online CBT and web-based interventions show promise in improving outcomes for chronic low back pain. Overall, CBT aims to help patients better manage their pain by changing maladaptive thoughts and behaviors.
Vertigo
VertigoThangamani Ramalingam This document discusses the vestibular system and causes of vertigo. It describes the three main stabilizing sensory systems - vestibular, visual, and somatosensory. Vertigo can be caused by physiological or pathological issues in any of these systems. Common tests to evaluate vertigo include Dix-Hallpike maneuver, caloric testing, electronystagmography, and rotational chair testing. Positional maneuvers like Epley maneuver, Semont maneuver, and canalith repositioning therapy can be used to treat benign paroxysmal positional vertigo.
Relaxation
RelaxationThangamani Ramalingam This document discusses various relaxation and stress management techniques including breathing exercises, progressive muscle relaxation, autogenic relaxation, guided imagery, yoga, tai chi, and compares martial arts like kung fu, taekwondo, and karate. It lists relaxation techniques like breathing, Jacobson progressive relaxation, autogenic relaxation, and guided imagery. It also mentions yoga and tai chi exercises. Finally, it provides brief descriptions comparing the Chinese martial art of kung fu, the South Korean martial art of taekwondo, and the Japanese martial art of karate.
Counselling
CounsellingThangamani Ramalingam Counseling involves the mutual exploration of ideas, attitudes, and feelings between a counselor and client to address misperceptions, emotional overlays affecting self-concept, and disparities between thoughts and feelings. Effective counseling distinguishes between guidance, counseling, and psychotherapy. A counselor should rarely solve problems for the client but instead guide them to their own solutions, trust intuition, and provide direction while protecting feelings and allowing the client to progress at their own pace. Counseling from trained professionals can profoundly impact individuals, families and communities by helping people navigate difficult life situations.
Attitude & BEHAVIOUR MODIFICATION THERAPY
Attitude & BEHAVIOUR MODIFICATION THERAPYThangamani Ramalingam This document discusses attitudes, theories of attitude formation and change, and behavior modification therapy (BMT). It defines attitudes as predispositions involving thoughts, feelings, and behaviors. Major theories discussed include Heider's balance theory, Festinger's cognitive dissonance theory, and the Yale attitude change approach. BMT techniques are described that are based on classical conditioning principles like systematic desensitization and flooding, operant conditioning using reinforcement, and cognitive therapies aimed at changing thoughts. Factors influencing attitudes include beliefs, social factors, personal experiences, and institutions.
Ins and outs of clinical trials
Ins and outs of clinical trialsThangamani Ramalingam This document provides an overview of key concepts for understanding clinical trials, including types of trials, levels of evidence, randomization methods, statistical tests, and critical appraisal tools. It discusses randomized controlled trials and observational studies. It also outlines statistical analyses such as t-tests, ANOVA, relative risk, and numbers needed to treat. Critical appraisal aspects covered include sample size, selection bias, blinding, intention-to-treat analysis, and appropriate use of statistical tests to avoid biases. A variety of critical appraisal tools are also listed.
Complications of abdominal surgery
Complications of abdominal surgeryThangamani Ramalingam The document discusses various potential complications that can arise from surgery. It covers respiratory complications like atelectasis and pneumonia. It also discusses circulatory complications, including deep vein thrombosis, pulmonary embolism, hemorrhage, and shock. Additional complications covered are wound infections, joint and muscle weakness, edema, urinary issues, and kypho-scoliosis. Risk factors, signs, and treatments are provided for several of the discussed complications.
Ad
Recently uploaded (20)
Evidence-based Management of Atrial Septal Defects and Patent Foramen Ovale
Evidence-based Management of Atrial Septal Defects and Patent Foramen OvaleDuke Heart Richard Krasuski, MD
Duke University
Valvular-Heart-Diseases-A-Comprehensive-Overview.pdf
Valvular-Heart-Diseases-A-Comprehensive-Overview.pdfAssist. Prof. Radhwan Hazem Alkhashab anaesthetic consideration for valvular heart diseases
TESTING OF NATURAL PRODUCTS AND DRUGS- DHANASHREE GIRIDHAR KOLHEKAR.pptx
TESTING OF NATURAL PRODUCTS AND DRUGS- DHANASHREE GIRIDHAR KOLHEKAR.pptxDHANASHREE KOLHEKAR TESTING OF NATURAL PRODUCTS AND DRUGS; Herbal and cosmetic analysis –II(MPA204T)
UNIT- Testing of natural products and drugs: Effect of herbal medicine on clinical
laboratory testing, Adulterant
Screening using modern analytical instruments, Regulation and dispensing of herbal drugs, Stability testing of natural products, protocol. Monographs of Herbal drugs: Study of monographs of herbal drugs and comparative study in IP, USP, Ayurvedic, Pharmacopoeia, American herbal Pharmacopoeia, British herbal Pharmacopoeia, Siddha and Unani Pharmacopoeia, WHO guidelines in quality assessment of herbal drugs.
PRESENTED BY
DHANASHREE G. KOLHEKAR
M PHARMA [1ST YEAR ]
( PHARMACUETICAL ANALYSIS)
BBAU, LUCKNOW, UP.
Basic Microbiology Notes for Diploma Students
Basic Microbiology Notes for Diploma StudentsSir. Stymass Kasty These were the Class notes for the students undertaking the Basic Technician Certificate in Animal Health and Production at Kilacha Agriculture and Livestock Training Institute, the Class of 2023
APPLICATION OF NANOTECHNOLOGY IN FOOD SCIENCE
APPLICATION OF NANOTECHNOLOGY IN FOOD SCIENCESchool of Biosciences, MACFAST College, Tiruvalla, Kerala, India Nanotechnology, the science of manipulating matter at the nanoscale (1-100 nanometers), has emerged as a groundbreaking tool in various fields, including food science.
Its applications in food science are vast and transformative, addressing challenges related to food safety, quality, nutrition, shelf life, and sustainability.
By leveraging the unique properties of nanomaterials, such as their high surface area, reactivity, and ability to interact with biological systems, nanotechnology is revolutionizing how food is produced, processed, packaged, and consumed.
Nanotechnology has significantly improved food processing techniques, leading to better quality, texture, and nutritional value of food products.
1. Nano-encapsulation
It involves enclosing sensitive bioactive compounds (e.g., vitamins, antioxidants, probiotics) within nanoscale capsules to protect them from degradation during processing and storage.
Example: Omega-3 fatty acids, which are prone to oxidation, are encapsulated to enhance their stability in functional foods like fortified bread and dairy products.
TH'e Oncology Meds for Medicine Services
TH'e Oncology Meds for Medicine ServicesTheoncologymeds TH'e Oncology Meds delivers cutting-edge, patient-focused cancer treatments with precision and care. Our innovative therapies are designed to target cancer at its core, improving outcomes and enhancing quality of life. We combine advanced research with compassionate support to empower patients through every stage of their oncology journey.
Pathophysiology of obesity - Rawa Muhsin
Pathophysiology of obesity - Rawa MuhsinRawa Muhsin This is my presentation about the pathophysiology of obesity, including its genetic causes, environmental/acquired factors, and the underlying mechanisms leading to the complications of obesity.
Physiology of Defense System - B-Cell Immunity
Physiology of Defense System - B-Cell ImmunityMedicoseAcademics Learning Objectives:
1. Discuss the processing of T & B lymphocytes in human body
2. Give a brief account on lymphocyte cloning
3. Comprehend the concept of humoral immunity
4. Discuss the structure of antibodies
5. Classify antibodies and discuss their functions
6. Explain the role of antibodies in B-cell immunity
7. Discuss the direct and indirect actions of antibodies
1.Surgery and indications ,incision its typespptx
1.Surgery and indications ,incision its typespptxBolan University of Medical and Health Sciences ,Quetta Absolute: Surgery is required to save life or prevent serious harm (e.g., perforated appendix).
Relative: Surgery is beneficial but not immediately necessary (e.g., elective hernia repair).
Diagnostic: When a definitive diagnosis cannot be made without surgical exploration (e.g., diagnostic laparoscopy).
Chosen based on anatomical landmarks, underlying pathology, and surgical approach.
Made with precision to minimize bleeding, injury to nearby structures, and scarring.
Closed with sutures, staples, or surgical glue after the procedure.
By: Dr Aliya Shair MUhammad PT
DPT OMPT
Lecturer: Bolan University Of Medical and Health Sciences ,Quetta
MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo ELmaaty Be...
MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo ELmaaty Be...Internal medicine department, faculty of Medicine Beni-Suef University Egypt
MSUS musculoskeletal ultrasound On The wrist
basic level
Marwa Abo ELmaaty Besar
Lecturer of Internal Medicine
(Rheumatology Immunology unit)
Faculty of medicine
Mansoura University
GERD (Gastroesophageal reflux disease) tejas.pptx.
GERD (Gastroesophageal reflux disease) tejas.pptx.TejaswitaRani Gastroesophageal reflux disease (GERD)
Pathology pptx
A complete knowledge of gerd epidemiology etiology causes symptoms complications and diagnosis......... to help you get a proper knowledge about it..
#Gerd #PPT #Medicine #Education #MBBS #BDS #BAMS #BHMS #BUMS #NURSING #PHARMA #PATHOLOGY #PHYSIOLOGY
Analgesia system & Abnormalities of Pain_AntiCopy.pdf
Analgesia system & Abnormalities of Pain_AntiCopy.pdfMedicoseAcademics This comprehensive lecture by Dr. Faiza (MBBS – Best Graduate, AIMC Lahore | FCPS Physiology | ICMT, CHPE, DHPE – STMU | MPH – GC University Faisalabad | MBA – Virtual University of Pakistan) provides an expert-level overview of the central analgesia system, pain modulation mechanisms, and various clinical abnormalities of pain.
Designed for undergraduate and postgraduate medical learners, this session integrates neurophysiology, pharmacology, and clinical neurology to explain how the body perceives, modulates, and at times misinterprets pain.
🧠 Key Learning Objectives:
Understand the central pain modulation system and its neural architecture.
Explore the role of endogenous and exogenous opioids in analgesia.
Analyze the physiological basis of non-pharmacological pain relief (massage, acupuncture, liniments, electrical stimulation).
Enumerate and explain abnormal pain conditions such as hyperalgesia, allodynia, shingles, trigeminal neuralgia, and different types of headaches.
Interpret the pathophysiology of migraines including vascular and cortical spreading depression theories.
🔬 Lecture Highlights:
✅ Central Analgesia System:
Neural pathways: Periaqueductal gray, Raphe magnus nucleus, spinal dorsal horn
Neurochemicals involved: Enkephalins, Serotonin
Gate Control Theory: Tactile input via Aβ fibers inhibits pain transmission
✅ Pain Suppression Mechanisms:
Massage & Rubbing: Local tactile inhibition via Aβ fibers
Acupuncture & Liniments: Dual role in stimulating pain gating and central analgesia
Electrical Stimulation: From surface electrodes to stereotactic thalamic implants
Patient-controlled neuromodulation: Tailoring stimulation for chronic pain
✅ Abnormalities of Pain:
Hyperalgesia: Heightened sensitivity to painful stimuli (primary and secondary)
Allodynia: Pain perception from non-painful stimuli
Herpes Zoster: Segmental dermatomal pain from dorsal root ganglion infection
Tic Douloureux: Trigeminal neuralgia characterized by sudden, stabbing facial pain
✅ Headache Pathophysiology:
Intracranial: Meningitis, low CSF pressure, migraines, alcohol
Extracranial: Muscle spasm, sinusitis, eye strain, light exposure
Migraine Mechanisms: Vascular spasm, cortical depression, serotonin imbalance, and familial genetics
✅ Clinical Correlation:
Brown-Séquard Syndrome: Sensory and motor dissociation explained by hemisection
Central vs. Peripheral Lesions in pain disorders
👨⚕️ Ideal for:
MBBS, BDS, and Allied Health Students
FCPS/MD/MS Physiology Trainees
Residents in Neurology, Anesthesia, and Internal Medicine
Physiology educators and academic examiners
Candidates preparing for USMLE, PLAB, FCPS, MDCAT, or Step 1
Presented by:
Dr. Faiza
Assistant Professor of Physiology
FCPS Physiology | CHPE | DHPE | MPH | MBA
Allama Iqbal Medical College (Best Graduate)
Advanced trauma life support for medical students
Advanced trauma life support for medical studentsAbdelraouf Abheiri Advanced trauma life support for medical students
Physiology of Central Nervous System - Somatosensory Cortex
Physiology of Central Nervous System - Somatosensory CortexMedicoseAcademics Learning Objectives:
1. Describe the organisation of somatosensory areas
2. Discuss the significance of sensory homunculus
3. Briefly describe the functions of the layers of the somatosensory cortex
4. Delineate the functions of somatosensory area I and somatosensory association areas
Revision MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo E...
Revision MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo E...Internal medicine department, faculty of Medicine Beni-Suef University Egypt Revision
MSUS On The wrist
basic level
Marwa Abo ELmaaty Besar
Lecturer of Internal Medicine
(Rheumatology Immunology unit)
Faculty of medicine
Mansoura University
09/05/2025
#مبادرة_ياللا_نذاكر_روماتولوجي
مجموعة الواتس
https://chat.whatsapp.com/JXpRq1eFxBj8OEZ97h1BDl
مجموعة التيليجرام
https://t.me/+-uyXK85Jr-1mY2Fk
قائمة تشغيل #مبادرة_ياللا_نذاكر_روماتولوجي
https://youtube.com/playlist?list=PLeE8TxEnM-wjdpwkKFl_Mt8W7MGFpbzfT&si=2jLAyxVMzbnU6_3-
ECG. OR EKG ELECTRO CARDIO GRAPHY .pptx
ECG. OR EKG ELECTRO CARDIO GRAPHY .pptxrekhapositivity ECG is a three letter acronym for ElectroCardioGraphy.
The word is derived from electro(greek for electricity),cardio(greek for heart) and graph(Greek root meaning "to write“)
An ECG (electrocardiogram) is a test that records the electrical activity of your heart over time
Echocardiography for Structural Heart Disease Interventions
Echocardiography for Structural Heart Disease InterventionsDuke Heart Sreekanth Vemulapalli, MD
Duke University
APPLICATION OF NANOTECHNOLOGY IN FOOD SCIENCE
APPLICATION OF NANOTECHNOLOGY IN FOOD SCIENCESchool of Biosciences, MACFAST College, Tiruvalla, Kerala, India
1.Surgery and indications ,incision its typespptx
1.Surgery and indications ,incision its typespptxBolan University of Medical and Health Sciences ,Quetta
MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo ELmaaty Be...
MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo ELmaaty Be...Internal medicine department, faculty of Medicine Beni-Suef University Egypt
Revision MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo E...
Revision MSUS musculoskeletal ultrasound On The wrist basic level Marwa Abo E...Internal medicine department, faculty of Medicine Beni-Suef University Egypt
Interprertation of statistics
- 1. INTERPRETATION OF STATISTICS A.THANGAMANI RAMALINGAM PT, MSc(PSY),PGDRM,ACS atramalingam@gmail.com
- 2. Objectives
- 3. DATA
- 4. Data
- 5. “Variability” Indicates dispersion, spread, variation, deviation For single population or sample data: where σ2 and s2 = population and sample variance respectively, xi = individual observations, μ = population mean, = sample mean, and n = total number of individual observations. The square roots are: standard deviation standard deviation
- 6. “Variability” Why “measure of dispersion” important? Consider returns from two categories of shares: * Shares A (%) = {1.8, 1.9, 2.0, 2.1, 3.6} * Shares B (%) = {1.0, 1.5, 2.0, 3.0, 3.9} Mean A = mean B = 2.28% But, different variability! Var(A) = 0.557, Var(B) = 1.367 * Would you invest in category A shares or category B shares?
- 7. One-SampleStatistics N Mean Std.Deviation Std.ErrorMean VAR00001 5 4.0000 1.58114 .70711 VAR00002 3 4.0000 2.00000 1.15470 VAR00003 3 3.0000 1.00000 .57735 VAR00004 3 5.0000 1.00000 .57735
- 11. One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper VAR00001 5.657 4 .005 4.00000 2.0368 5.9632 VAR00002 3.464 2 .074 4.00000 -.9683 8.9683 VAR00003 5.196 2 .035 3.00000 .5159 5.4841 VAR00004 8.660 2 .013 5.00000 2.5159 7.4841
- 13. P Value
- 14. One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper VAR00001 5.657 4 .005 4.00000 2.0368 5.9632 VAR00002 3.464 2 .074 4.00000 -.9683 8.9683 VAR00003 5.196 2 .035 3.00000 .5159 5.4841 VAR00004 8.660 2 .013 5.00000 2.5159 7.4841 VAR00005 6.928 2 .020 4.00000 1.5159 6.4841 VAR00006 3.051 2 .093 3.66667 -1.5045 8.8378 VAR00007 3.606 2 .069 4.33333 -.8378 9.5045 VAR00008 5.292 2 .034 4.66667 .8721 8.4612
- 15. TEST STATISTIC
- 19. AREA UNDER CURVE
- 23. Take home message Frequency data distribution/ sampling distribution Standard normal distribution/ t, z, f and chi-square distributions Large the sample ,narrow the CI Large the mean difference, less the p value. Why CI when p less than 0,05