This mini project is created by Md Halim from Haldia Insititute of Technology, Haldia WB. Disclaimer:- if any error is not the responsibility to team Halim
This document discusses measures of dispersion in statistics. It defines dispersion as the extent of variation in a data set from the average value. There are two main types of dispersion - absolute and relative. Absolute measures express variation in units of the data and include range, variance, standard deviation, and quartile deviation. Relative measures allow comparison between data sets by being unit-free, such as the coefficient of variation. Key absolute measures are then explained in more detail, along with their merits and demerits.
The geometric mean is a type of average that indicates the central tendency of a set of numbers using their product, as opposed to the arithmetic mean which uses their sum, and it is calculated by taking the nth root of the product of the numbers. The geometric mean is more appropriate than the arithmetic mean for describing proportional growth and ratios, and it has various applications in fields like optics, signal processing, geometry, and finance.
This document discusses various measures of central tendency including arithmetic mean, geometric mean, harmonic mean, median, and mode. It provides definitions and formulas for calculating each measure, as well as discussing their merits and demerits. The arithmetic mean is the sum of all values divided by the total number of values. The geometric mean uses products and logarithms. The harmonic mean gives more weight to smaller values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value.
The median is the middle value when values are ordered from lowest to highest. It divides the data set such that half the values are lower than the median and half are higher. For an even number of values, the median is the average of the two middle values. The mode is the most frequently occurring value. It indicates the most common result. Both the median and mode are less influenced by outliers than the mean. They provide a more representative central value for skewed or irregularly distributed data sets.
Slideshare notes about measures of central tendancy(mean,median and mode)IRADUKUNDA Fiston
This presentation discusses various measures of central tendency including the mean, median, mode, harmonic mean and geometric mean. It provides definitions and formulas for calculating each measure along with their merits and demerits. The mean is the sum of all values divided by the number of values and can be affected by outliers. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequent value. Harmonic and geometric means are other types of averages.
This document discusses measures of central tendency and dispersion. It defines mean, median and mode as measures of central tendency, which describe the central location of data. The mean is the average value, median is the middle value, and mode is the most frequent value. It also defines measures of dispersion like range, interquartile range, variance and standard deviation, which describe how spread out the data are. Standard deviation in particular measures how far data values are from the mean. Approximately 68%, 95% and 99.7% of observations in a normal distribution fall within 1, 2 and 3 standard deviations of the mean respectively.
The document discusses different measures of central tendency including the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value in the data set. The document provides examples of calculating each measure and discusses their advantages and disadvantages.
This document discusses measures of dispersion in statistics. It defines dispersion as the extent of variation in a data set from the average value. There are two main types of dispersion - absolute and relative. Absolute measures express variation in units of the data and include range, variance, standard deviation, and quartile deviation. Relative measures allow comparison between data sets by being unit-free, such as the coefficient of variation. Key absolute measures are then explained in more detail, along with their merits and demerits.
The geometric mean is a type of average that indicates the central tendency of a set of numbers using their product, as opposed to the arithmetic mean which uses their sum, and it is calculated by taking the nth root of the product of the numbers. The geometric mean is more appropriate than the arithmetic mean for describing proportional growth and ratios, and it has various applications in fields like optics, signal processing, geometry, and finance.
This document discusses various measures of central tendency including arithmetic mean, geometric mean, harmonic mean, median, and mode. It provides definitions and formulas for calculating each measure, as well as discussing their merits and demerits. The arithmetic mean is the sum of all values divided by the total number of values. The geometric mean uses products and logarithms. The harmonic mean gives more weight to smaller values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value.
The median is the middle value when values are ordered from lowest to highest. It divides the data set such that half the values are lower than the median and half are higher. For an even number of values, the median is the average of the two middle values. The mode is the most frequently occurring value. It indicates the most common result. Both the median and mode are less influenced by outliers than the mean. They provide a more representative central value for skewed or irregularly distributed data sets.
Slideshare notes about measures of central tendancy(mean,median and mode)IRADUKUNDA Fiston
This presentation discusses various measures of central tendency including the mean, median, mode, harmonic mean and geometric mean. It provides definitions and formulas for calculating each measure along with their merits and demerits. The mean is the sum of all values divided by the number of values and can be affected by outliers. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequent value. Harmonic and geometric means are other types of averages.
This document discusses measures of central tendency and dispersion. It defines mean, median and mode as measures of central tendency, which describe the central location of data. The mean is the average value, median is the middle value, and mode is the most frequent value. It also defines measures of dispersion like range, interquartile range, variance and standard deviation, which describe how spread out the data are. Standard deviation in particular measures how far data values are from the mean. Approximately 68%, 95% and 99.7% of observations in a normal distribution fall within 1, 2 and 3 standard deviations of the mean respectively.
The document discusses different measures of central tendency including the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value in the data set. The document provides examples of calculating each measure and discusses their advantages and disadvantages.
This document provides information about frequency distributions and constructing frequency distribution tables. It defines a frequency distribution as a representation of data in a tabular format showing the number of observations within intervals. It then outlines the general process for constructing a frequency table which includes determining the range, number of classes, class width, and recording the frequencies in a table. An example is provided of constructing a frequency table from data on the ages of 50 men who died from gunfire using 7 classes. Guidelines for constructing frequency tables are also listed.
The median is the middlemost score when values are arranged from lowest to highest. It divides the data set into two equal groups, with scores above and below the median. The median is not affected by extreme values and can be used when the mean would be skewed. To find the median of ungrouped data, arrange values from highest to lowest and take the middle value. For grouped data, use the formula Median = Ll + cfb/f, where Ll is the lower limit of the class containing N/2, cfb is the cumulative frequency below the assumed median, and f is the corresponding frequency.
Introduction to correlation and regression analysisFarzad Javidanrad
This document provides an introduction to correlation and regression analysis. It defines key concepts like variables, random variables, and probability distributions. It discusses how correlation measures the strength and direction of a linear relationship between two variables. Correlation coefficients range from -1 to 1, with values closer to these extremes indicating stronger correlation. The document also introduces determination coefficients, which measure the proportion of variance in one variable explained by the other. Regression analysis builds on correlation to study and predict the average value of one variable based on the values of other explanatory variables.
The mean deviation is a measure of how spread out values are from the average. It is calculated by:
1) Finding the mean of all values.
2) Calculating the distance between each value and the mean.
3) Taking the average of those distances. This provides the mean deviation, which tells us how far on average values are from the central mean. Examples show calculating mean deviation for both grouped and ungrouped data sets.
1. Sampling error occurs because sample means are not equal to the population mean and differ from each other.
2. The distribution of sample means follows a normal distribution if drawn from a normal population, and approximates a normal distribution if drawn from a non-normal population as the sample size increases.
3. A confidence interval for the population mean or probability can be constructed given the sample size, mean or probability, and standard deviation. The confidence level indicates the probability the true population parameter falls within the interval.
The document discusses measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. For the mean, formulas are given for both raw data and frequency data. The relationships between the mean, median, and mode are explored, including an empirical relationship that can be used to find one value if the other two are known for symmetrical data. The importance of each measure is discussed for different business applications depending on the characteristics of the data.
Measures of central tendency and dispersionAbhinav yadav
This document discusses various measures of central tendency and dispersion. It defines the mean, median, and mode as measures of central tendency, and describes how to calculate the arithmetic mean, geometric mean, harmonic mean, median, and mode. It also discusses measures of dispersion such as variance and standard deviation, and classifies them as either measures of absolute dispersion or relative dispersion.
Presentation on "Measure of central tendency"muhammad raza
This presentation introduces measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure using both ungrouped and grouped data. The mean is the average and is used for less scattered data. It is calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. For even numbers of values, the median is the average of the two middle values. The mode is the most frequently occurring value in a data set and there can be single or multiple modes. Formulas are provided for calculating the median and mode using grouped frequency data.
This document discusses various aspects of data distributions including their shape, modality, symmetry, and skewness. It provides definitions and examples of key terms such as:
- Modality, which refers to the number of peaks in a distribution. Unimodal distributions have one peak while multimodal distributions have two or more.
- Symmetry, which means a distribution could be split down the middle to form mirror images. Asymmetric or skewed distributions have an off-center peak with a tail on one side.
- Skewness, which is assessed using measures like Pearson's coefficient and Fisher's measure that quantify the degree of asymmetry. Positive skewness indicates a right tail while negative indicates a left tail
This document discusses various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate each from both ungrouped and grouped data. The mean is the average value and is calculated by summing all values and dividing by the total number of values. The median is the middle value when values are arranged in order and divides the data set in half. The mode is the most frequently occurring value.
This document discusses various measures of central tendency including arithmetic mean, median, mode, geometric mean, and harmonic mean. It provides definitions and formulas for calculating each measure, along with examples to illustrate how to calculate the median, mode, geometric mean, and harmonic mean. The document is intended as a guide for understanding and calculating different types of averages and measures of central tendency.
The document discusses quartiles, which divide a data set into four equal parts. The first quartile contains the smallest 25% of values, the second quartile contains values between the 25th and 50th percentiles, the third quartile contains values between the 50th and 75th percentiles, and the fourth quartile contains the largest 25% of values. Formulas are provided for calculating the lower quartile (Q1), median (Q2), and upper quartile (Q3). The quartile deviation is defined as half the distance between Q3 and Q1, while the interquartile range is the full distance between Q3 and Q1. Examples are given to illustrate quartile calculations.
This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
This document discusses the calculation of quartile deviation from both ungrouped and grouped data. It defines quartiles as values that divide a data distribution into four equal parts (Q1, Q2, Q3). The quartile deviation is half the difference between the first (Q1) and third (Q3) quartiles. It provides the steps to find Q1, Q3, and quartile deviation from ungrouped data by ranking scores and using quartile locators. For grouped data, it uses formulas involving class limits and cumulative frequencies to determine Q1 and Q3, then takes half their difference. An example calculation is shown.
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using data sets. The mean is the average value obtained by dividing the sum of all values by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. The document outlines advantages and disadvantages of each measure and concludes that measures of central tendency describe the typical or central value in a data set.
This document discusses different measures of central tendency, including the mean, median, and mode. It defines each measure and provides the relevant formulas. The mean is the sum of all values divided by the total number of values and can be arithmetic, geometric, or weighted. The median is the middle value of a data set. The mode is the most frequently occurring value. Formulas and examples are given for calculating each measure from a data set.
This document discusses a one-way analysis of variance (ANOVA) used to compare the effects of different oil types (A, B, C) on car mileage. It tests the null hypothesis that the mean mileages are equal against the alternative that at least two means differ. The ANOVA calculates sums of squares and F statistics to determine if there are significant differences between the treatment means, rejecting the null hypothesis if F exceeds the critical value. If differences exist, pairwise comparisons estimate the size of differences between each pair of means using confidence intervals.
This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.
Measures of central tendency describe the middle or center of a data set and include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the number of values. The median is the middle number in a data set arranged in order. The mode is the value that occurs most frequently. These measures are used to understand the typical or common values in a data set.
This document provides information about frequency distributions and constructing frequency distribution tables. It defines a frequency distribution as a representation of data in a tabular format showing the number of observations within intervals. It then outlines the general process for constructing a frequency table which includes determining the range, number of classes, class width, and recording the frequencies in a table. An example is provided of constructing a frequency table from data on the ages of 50 men who died from gunfire using 7 classes. Guidelines for constructing frequency tables are also listed.
The median is the middlemost score when values are arranged from lowest to highest. It divides the data set into two equal groups, with scores above and below the median. The median is not affected by extreme values and can be used when the mean would be skewed. To find the median of ungrouped data, arrange values from highest to lowest and take the middle value. For grouped data, use the formula Median = Ll + cfb/f, where Ll is the lower limit of the class containing N/2, cfb is the cumulative frequency below the assumed median, and f is the corresponding frequency.
Introduction to correlation and regression analysisFarzad Javidanrad
This document provides an introduction to correlation and regression analysis. It defines key concepts like variables, random variables, and probability distributions. It discusses how correlation measures the strength and direction of a linear relationship between two variables. Correlation coefficients range from -1 to 1, with values closer to these extremes indicating stronger correlation. The document also introduces determination coefficients, which measure the proportion of variance in one variable explained by the other. Regression analysis builds on correlation to study and predict the average value of one variable based on the values of other explanatory variables.
The mean deviation is a measure of how spread out values are from the average. It is calculated by:
1) Finding the mean of all values.
2) Calculating the distance between each value and the mean.
3) Taking the average of those distances. This provides the mean deviation, which tells us how far on average values are from the central mean. Examples show calculating mean deviation for both grouped and ungrouped data sets.
1. Sampling error occurs because sample means are not equal to the population mean and differ from each other.
2. The distribution of sample means follows a normal distribution if drawn from a normal population, and approximates a normal distribution if drawn from a non-normal population as the sample size increases.
3. A confidence interval for the population mean or probability can be constructed given the sample size, mean or probability, and standard deviation. The confidence level indicates the probability the true population parameter falls within the interval.
The document discusses measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. For the mean, formulas are given for both raw data and frequency data. The relationships between the mean, median, and mode are explored, including an empirical relationship that can be used to find one value if the other two are known for symmetrical data. The importance of each measure is discussed for different business applications depending on the characteristics of the data.
Measures of central tendency and dispersionAbhinav yadav
This document discusses various measures of central tendency and dispersion. It defines the mean, median, and mode as measures of central tendency, and describes how to calculate the arithmetic mean, geometric mean, harmonic mean, median, and mode. It also discusses measures of dispersion such as variance and standard deviation, and classifies them as either measures of absolute dispersion or relative dispersion.
Presentation on "Measure of central tendency"muhammad raza
This presentation introduces measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure using both ungrouped and grouped data. The mean is the average and is used for less scattered data. It is calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. For even numbers of values, the median is the average of the two middle values. The mode is the most frequently occurring value in a data set and there can be single or multiple modes. Formulas are provided for calculating the median and mode using grouped frequency data.
This document discusses various aspects of data distributions including their shape, modality, symmetry, and skewness. It provides definitions and examples of key terms such as:
- Modality, which refers to the number of peaks in a distribution. Unimodal distributions have one peak while multimodal distributions have two or more.
- Symmetry, which means a distribution could be split down the middle to form mirror images. Asymmetric or skewed distributions have an off-center peak with a tail on one side.
- Skewness, which is assessed using measures like Pearson's coefficient and Fisher's measure that quantify the degree of asymmetry. Positive skewness indicates a right tail while negative indicates a left tail
This document discusses various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate each from both ungrouped and grouped data. The mean is the average value and is calculated by summing all values and dividing by the total number of values. The median is the middle value when values are arranged in order and divides the data set in half. The mode is the most frequently occurring value.
This document discusses various measures of central tendency including arithmetic mean, median, mode, geometric mean, and harmonic mean. It provides definitions and formulas for calculating each measure, along with examples to illustrate how to calculate the median, mode, geometric mean, and harmonic mean. The document is intended as a guide for understanding and calculating different types of averages and measures of central tendency.
The document discusses quartiles, which divide a data set into four equal parts. The first quartile contains the smallest 25% of values, the second quartile contains values between the 25th and 50th percentiles, the third quartile contains values between the 50th and 75th percentiles, and the fourth quartile contains the largest 25% of values. Formulas are provided for calculating the lower quartile (Q1), median (Q2), and upper quartile (Q3). The quartile deviation is defined as half the distance between Q3 and Q1, while the interquartile range is the full distance between Q3 and Q1. Examples are given to illustrate quartile calculations.
This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
This document discusses the calculation of quartile deviation from both ungrouped and grouped data. It defines quartiles as values that divide a data distribution into four equal parts (Q1, Q2, Q3). The quartile deviation is half the difference between the first (Q1) and third (Q3) quartiles. It provides the steps to find Q1, Q3, and quartile deviation from ungrouped data by ranking scores and using quartile locators. For grouped data, it uses formulas involving class limits and cumulative frequencies to determine Q1 and Q3, then takes half their difference. An example calculation is shown.
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using data sets. The mean is the average value obtained by dividing the sum of all values by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. The document outlines advantages and disadvantages of each measure and concludes that measures of central tendency describe the typical or central value in a data set.
This document discusses different measures of central tendency, including the mean, median, and mode. It defines each measure and provides the relevant formulas. The mean is the sum of all values divided by the total number of values and can be arithmetic, geometric, or weighted. The median is the middle value of a data set. The mode is the most frequently occurring value. Formulas and examples are given for calculating each measure from a data set.
This document discusses a one-way analysis of variance (ANOVA) used to compare the effects of different oil types (A, B, C) on car mileage. It tests the null hypothesis that the mean mileages are equal against the alternative that at least two means differ. The ANOVA calculates sums of squares and F statistics to determine if there are significant differences between the treatment means, rejecting the null hypothesis if F exceeds the critical value. If differences exist, pairwise comparisons estimate the size of differences between each pair of means using confidence intervals.
This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.
Measures of central tendency describe the middle or center of a data set and include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the number of values. The median is the middle number in a data set arranged in order. The mode is the value that occurs most frequently. These measures are used to understand the typical or common values in a data set.
The three main measures of central tendency are the mean, median, and mode. The mean is the average and is calculated by adding all values and dividing by the total number. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value. For symmetric distributions, the mean, median, and mode will be equal. However, for skewed distributions the mean will be pulled higher or lower than the median depending on the direction of skew.
This document provides an overview of descriptive statistics and statistical inference. It discusses key concepts such as populations, samples, census surveys, sample surveys, raw data, frequency distributions, measures of central tendency including the arithmetic mean, median, and mode. It provides examples and formulas for calculating averages from both grouped and ungrouped data. The arithmetic mean can be used to find the combined mean of two groups or a weighted mean when values have different levels of importance. The median divides a data set into two equal halves.
The modal rating is the rating value that occurs most frequently in the dataset. To find the mode, we would need to analyze the rating frequencies and identify which rating has the highest count. Without access to the actual dataset values and frequencies, I cannot determine the modal rating directly. The mode is a measure of central tendency that is best for identifying the most common or typical value in a dataset.
The document discusses various measures of central tendency (averages) and dispersion that are used to summarize and describe data in statistics. It defines common averages like the arithmetic mean, median, mode, harmonic mean, and geometric mean. It also covers measures of dispersion such as the range, quartile deviation, mean deviation, and standard deviation. As an example, it analyzes test score data from 5 students using the arithmetic mean to find the average score.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the central tendency and dispersion of data distributions.
This document discusses measures of central tendency and dispersion used to analyze and summarize data. It defines key terms like mean, median, mode, range, variance, and standard deviation. It explains how to calculate these measures both mathematically and using grouped or sample data, and the importance of understanding the distribution, central tendency and dispersion of data.
Describing quantitative data with numbersUlster BOCES
1. Quantitative data can be summarized using measures of center (mean, median), spread (range, IQR, standard deviation), and position (quartiles, percentiles, z-scores).
2. The mean is more affected by outliers than the median. The median is more resistant to outliers and a better measure of center for skewed data.
3. Additional summaries like the five-number summary and boxplots provide a graphical view of the distribution and identify potential outliers.
Biostatistics cource for clinical pharmacyBatizemaryam
This document discusses methods for summarizing data, including measures of central tendency and dispersion. It defines the mean, median, and mode as common measures of central tendency. For grouped data, it explains how to calculate the mean, median, and mode. The document also discusses measures of dispersion such as range, interquartile range, variance, and standard deviation. It provides examples of calculating various summary statistics for both ungrouped and grouped data.
Central tendency refers to measures that describe the center or typical value of a dataset. The three main measures of central tendency are the mean, median, and mode.
The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when data is arranged in order. For even datasets, the median is the average of the two middle values. The mode is the value that occurs most frequently in the dataset.
This document provides an overview of key concepts in statistics including measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation), and central moments (skewness, kurtosis). It discusses calculating and comparing the mean, median, mode, and how they each describe the central position of a data distribution. It also explains how variance and standard deviation measure how spread out the data is from the mean. The document is intended as a textbook for students and general readers to learn basic statistical concepts.
Measures of Central Tendency, Variability and ShapesScholarsPoint1
The PPT describes the Measures of Central Tendency in detail such as Mean, Median, Mode, Percentile, Quartile, Arthemetic mean. Measures of Variability: Range, Mean Absolute deviation, Standard Deviation, Z-Score, Variance, Coefficient of Variance as well as Measures of Shape such as kurtosis and skewness in the grouped and normal data.
The document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and examples of how to calculate each measure. It also discusses how transformations of data and outliers can affect measures of central tendency, with the mean being most impacted by outliers and the mode least impacted. The median and mode are considered more resistant measures of central tendency than the mean.
Basic Statistical Descriptions of Data.pptxAnusuya123
This document provides an overview of 7 basic statistical concepts for data science: 1) descriptive statistics such as mean, mode, median, and standard deviation, 2) measures of variability like variance and range, 3) correlation, 4) probability distributions, 5) regression, 6) normal distribution, and 7) types of bias. Descriptive statistics are used to summarize data, variability measures dispersion, correlation measures relationships between variables, and probability distributions specify likelihoods of events. Regression models relationships, normal distribution is often assumed, and biases can influence analyses.
This document discusses the concept and calculation of the mean as a measure of central tendency. It defines the mean as the sum of all values divided by the total number of items. It provides the formula for calculating the mean from both ungrouped and grouped data, using frequency tables. It gives an example of calculating the mean from an ungrouped data set and from a grouped frequency table using midpoints. It also describes a shortcut formula that can be used to calculate the mean from grouped data. Finally, it discusses when the mean is most appropriate to use, noting that it is the most reliable measure when accuracy is needed and when further statistical analysis will be done.
This document discusses various measures of central tendency used in statistics including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average value found by summing all values and dividing by the total count. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value. The document also discusses weighted mean, geometric mean, harmonic mean, and compares the properties of each central tendency measure.
This document provides an introduction to measures of central tendency in statistics. It defines measures of central tendency as statistical measures that describe the center of a data distribution. The three most commonly used measures are the mean, median, and mode. The document focuses on explaining the arithmetic mean in detail, including how to calculate the mean from individual data series, discrete data series, and continuous data series using different methods. It also discusses weighted means, combined means, and the relationship between the mean, median and mode. The objectives and advantages and disadvantages of the mean are provided.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
1. Haldia Institute of
Technology, Haldia
Measures of Central
Tendency:
Mean, Mode, Median
Course Name:- Mathematics-III
Course Code:- M401
Dept:- Computer Science & Engineering
Name:- Md. Halim
Class Roll:- L18/CS/154
University Roll:- 103011018015
Date:-
3. Introduction================================================================================================
Apart from the mean, median and mode are the two
commonly used measures of central tendency. The median
is sometimes referred to as a measure of location as it tells
us where the data are.[1] This article describes about
median, mode, and also the guidelines for selecting the
appropriate measure of central tendency.
➢Measures of central tendency are statistical measures
which describe the position of a distribution.
➢They are also called statistics of location, and are the
complement of statistics of dispersion, which provide
information concerning the variance or distribution of
observations.
➢ In the univariate context, the mean, median and mode
are the most commonly used measures of central
tendency.
➢computable values on a distribution that discuss
the behavior of the center of a distribution.
3
4. Methodologies==================================================================================================
1.Arithmetic Mean:-
Arithmetic mean is a mathematical average and it is the
most popular measures of central tendency. It is frequently
referred to as ‘mean’ it is obtained by dividing sum of the
values of all observations in a series (ƩX) by the number of
items (N) constituting the series. Thus, mean of a set of
numbers X1, X2, X3,………..Xn denoted by and is defined
as
2.Median:-
Median is a central value of the distribution, or the
value which divides the distribution in equal parts,
each part containing equal number of items. Thus it is
the central value of the variable, when the values are
arranged in order of magnitude. Connor has defined
as “ The median is that value of the variable which
divides the group into two equal parts, one part
comprising of all values greater, and the other, all
values less than median”
4
5. 3.Mode:-
Croxton and Cowden : defined it as “the mode of a
distribution is the value at the point armed with the
item to most tend to most heavily concentrated. It may
be regarded as the most typical of a series of value”.
The exact value of mode can be obtained by the
following formula.
Mode (Z) = L1+
1.1 fig. Positively(right) skewed distribution
5
6. = 407.6
Example : Calculated the Arithmetic MeanDIRC
Monthly Users Statistics in the UniversityLibrary
No. of
Working
Days
Average
Users per
month
Month Total Users
Sep- 2011
Oct- 2011
Nov-2011
Dec-2011
Jan- 2012
Feb-2012
Total
-----------------------------------------------------------------------------
- 24
21
23
25
24
23
140
11618
08857
11459
08841
05478
10811
570664
484.08
421.76
498.22
353.64
228.25
470.04
Month
6
7. Median(M) = 40 +
= 40 +
Example: Median of a set Grouped Data in a
Distribution of Respondents by age
Age Group Frequency of
Median class(f)
Cumulative
frequencies(cf)
-----------------------------------------------------------------------------
-
00-20
20-40
40-60
60-80
80-100
Total
1
15
32
54
30
19
150
15
47
101
131
150
= 40+0.52X20 = 40+10.37 = 50.37
7
8. Z = 2000 +
Z = 2000 +
Z = 2000 + 0.8 X 500 = 400
Z = 2400
Example: Calculate Mode for the distribution of
monthly rent Paid by Libraries in Karnataka
Monthly rent (Rs)
0500-1000
1000-1500
1500-2000
2000-2500
2500-3000
3000 & Above
Total
Number of Libraries (f)
5
10
8
16
14
12
65
8
9. Conclusion======================================================================================================
➢A measure of central tendency is a measure that tells
us where the middle of a bunch of data lies.
➢Mean is the most common measure of central
tendency. It is simply the sum of the numbers divided
by the number of numbers in a set of data. This is also
known as average.
➢Median is the number present in middle when the
numbers in a set of data are arranged in ascending or
descending order. If the number of numbers in a data
set is even, then the median is the mean of the two
middle numbers.
➢Mode is the value that occurs most frequently in a set
of data.
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10. References======================================================================================================
1. Balasubramanian , P., & Baladhandayutham, A.
(2011).Research methodology in library science. (pp. 164-
170). New Delhi: Deep & Deep Publications.
2. Sehgal, R. L. (1998). Statistical techniques for librarians. (pp.
117-130). New Delhi: Ess Ess Publications.
3. Busha,Charles, H., & Harter,Stephen, P. (1980). Research
methods in librarianship: techniques and interpretation. (pp.
372-395). New York: Academic Press.
4. Krishnaswami, O. R. (2002). Methodology of research in social
sciences. (pp. 361-366). Mumbai: Himalaya Publishing House.
5. Kumar,Arvind. (2002). Research methodology in social
science. (pp. 278-289). New Delhi: Sarup & Sons.
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