#Data Visualization #algorithm #Infographic
Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?
In my own case, I have not, until one day I found an obvious mistake on Excel’s dual vertical axes chart.
The mistake is resulted from that Excel does not have an algorithm that can address the most important and inevitable question for dual vertical axes charts: “How to set the upper limits and the lower limits on the TWO vertical axes?”. In fact, Excel simply adopts the same algorithm used for its single vertical axis chart on each vertical axis separately. And thus the elongations of the two axes are not coordinated to be the same, which leads to its misleading dual vertical axes charts.
To solve this critical mistake, Graphician invented a patented algorithm that can create 100% correct dual vertical axes chart. And we have also created a trial Excel Add-in which can adjust any dual vertical axes chart created by Excel 2007 or an advanced version with one single click.
You can now download the Add-in at http://www.graphician.com/patent-01.html. We hope you find the Add-in interesting and useful, and we would love to hear your comment about it if any. You may contact us at graphician1122@gmail.com or visit our website: "www.graphician.com" to find more information.
The document describes an algorithm for dual vertical axis charts that more accurately represents data relationships compared to Excel's approach. It presents examples where Excel's algorithm misleads viewers by making decreases or increases appear similar in size when they are not. The key steps of Graphician's algorithm are to: 1) Set the axis limits based on the data set with the larger percentage change. 2) Adjust one limit of the other axis to match the percentage change scale. This allows changes of different magnitudes to be clearly distinguished on the chart.
I am Walker D. I am a Civil and Environmental Engineering assignment Expert at statisticsassignmenthelp.com. I hold a Ph.D. in Civil and Environmental Engineering. I have been helping students with their homework for the past 8 years. I solve assignments related to Civil and Environmental Engineering Assignment. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Civil and Environmental Engineering assignments.
I am Watson A. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, Liberty University, USA
I have been helping students with their homework for the past 6 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
SAS Ron Cody Solutions for even Number problems from Chapter 16 to 20Ayapparaj SKS
The document discusses using SAS procedures like PROC MEANS, PROC FREQ, PROC TABULATE, PROC GCHART, and PROC GPLOT to analyze and visualize data from several SAS datasets. Examples include generating statistics and summaries, creating cross tabulations and frequency tables, formatting variables, producing HTML output, and creating bar charts, scatter plots and other visualizations. The document contains examples of using CLASS, BY, FORMAT, and ODS statements with these procedures.
Learning SAS With Example by Ron Cody :Chapter 16 to Chapter 20 SolutionVibeesh CS
1) The document demonstrates various SAS procedures to analyze and summarize data from multiple SAS data sets, including PROC MEANS, PROC FREQ, PROC TABULATE, PROC GCHART, and PROC GPLOT.
2) Examples include computing statistics by gender and school size using BY and CLASS statements in PROC MEANS, creating frequency tables and cross tabulations in PROC FREQ, producing customized tables using PROC TABULATE, and creating bar charts and scatter plots using PROC GCHART and PROC GPLOT.
3) The document also demonstrates using ODS to produce output files and control formatting and layout of results.
MANUAL DE ESTADÍSTICA BÁSICA CON EXCEL ERICK AGUILARERICKAGUILAR72
This document provides instructions for creating a frequency distribution table and graphs from a set of 180 invented data points on the weights of students from a communication sciences program. It explains how to calculate key descriptive statistics like minimum, maximum, range and class width. It then outlines the process for constructing the frequency table, including calculating relative frequencies. Finally, it describes how to make a histogram and frequency polygon graph from the data.
The document discusses various techniques for curve fitting data, including interpolation, linear regression, and higher-order polynomial fitting. It begins by explaining the motivation for curve fitting as creating a single function to represent trends in observed data. Linear regression finds the best-fit straight line by minimizing the squared errors between data points and the line. Higher-order polynomials allow fitting nonlinear trends by finding coefficients for polynomial functions up to a given order, such as quadratic, that also minimize the squared errors.
The document discusses four basic mathematical operations: addition, subtraction, multiplication, and division. It provides examples of each operation, showing how to write them using symbols like +, -, x, and ÷. For addition, it gives examples like 7 + 8 = 15 and 50 + 3 = 53. For subtraction, examples are 10 - 15 = -5 and 67 - 20 = 47. Multiplication examples include 6 x 10 = 60 and 9 x 2 = 18. Division is shown as 30 ÷ 6 = 5.
The document describes an algorithm for dual vertical axis charts that more accurately represents data relationships compared to Excel's approach. It presents examples where Excel's algorithm misleads viewers by making decreases or increases appear similar in size when they are not. The key steps of Graphician's algorithm are to: 1) Set the axis limits based on the data set with the larger percentage change. 2) Adjust one limit of the other axis to match the percentage change scale. This allows changes of different magnitudes to be clearly distinguished on the chart.
I am Walker D. I am a Civil and Environmental Engineering assignment Expert at statisticsassignmenthelp.com. I hold a Ph.D. in Civil and Environmental Engineering. I have been helping students with their homework for the past 8 years. I solve assignments related to Civil and Environmental Engineering Assignment. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Civil and Environmental Engineering assignments.
I am Watson A. I am a Statistics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, Liberty University, USA
I have been helping students with their homework for the past 6 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
SAS Ron Cody Solutions for even Number problems from Chapter 16 to 20Ayapparaj SKS
The document discusses using SAS procedures like PROC MEANS, PROC FREQ, PROC TABULATE, PROC GCHART, and PROC GPLOT to analyze and visualize data from several SAS datasets. Examples include generating statistics and summaries, creating cross tabulations and frequency tables, formatting variables, producing HTML output, and creating bar charts, scatter plots and other visualizations. The document contains examples of using CLASS, BY, FORMAT, and ODS statements with these procedures.
Learning SAS With Example by Ron Cody :Chapter 16 to Chapter 20 SolutionVibeesh CS
1) The document demonstrates various SAS procedures to analyze and summarize data from multiple SAS data sets, including PROC MEANS, PROC FREQ, PROC TABULATE, PROC GCHART, and PROC GPLOT.
2) Examples include computing statistics by gender and school size using BY and CLASS statements in PROC MEANS, creating frequency tables and cross tabulations in PROC FREQ, producing customized tables using PROC TABULATE, and creating bar charts and scatter plots using PROC GCHART and PROC GPLOT.
3) The document also demonstrates using ODS to produce output files and control formatting and layout of results.
MANUAL DE ESTADÍSTICA BÁSICA CON EXCEL ERICK AGUILARERICKAGUILAR72
This document provides instructions for creating a frequency distribution table and graphs from a set of 180 invented data points on the weights of students from a communication sciences program. It explains how to calculate key descriptive statistics like minimum, maximum, range and class width. It then outlines the process for constructing the frequency table, including calculating relative frequencies. Finally, it describes how to make a histogram and frequency polygon graph from the data.
The document discusses various techniques for curve fitting data, including interpolation, linear regression, and higher-order polynomial fitting. It begins by explaining the motivation for curve fitting as creating a single function to represent trends in observed data. Linear regression finds the best-fit straight line by minimizing the squared errors between data points and the line. Higher-order polynomials allow fitting nonlinear trends by finding coefficients for polynomial functions up to a given order, such as quadratic, that also minimize the squared errors.
The document discusses four basic mathematical operations: addition, subtraction, multiplication, and division. It provides examples of each operation, showing how to write them using symbols like +, -, x, and ÷. For addition, it gives examples like 7 + 8 = 15 and 50 + 3 = 53. For subtraction, examples are 10 - 15 = -5 and 67 - 20 = 47. Multiplication examples include 6 x 10 = 60 and 9 x 2 = 18. Division is shown as 30 ÷ 6 = 5.
This document provides an overview of key concepts from Chapter 1 of a linear functions textbook, including:
- Solving linear equations and using data to create scatterplots and graph lines
- Finding equations of lines from their graphs or intercepts
- Using linear models to represent real-world situations like business costs and revenues
- Identifying the slope, intercepts, domain and range of linear equations and determining if sets of points represent functions
The chapter content is explained through examples like modeling the costs and profits of a golf cart refurbishing business.
This document provides an introduction to using R and RStudio. It discusses installing R and RStudio, the four windows in RStudio (source editor, console, environment/history, and plots/files), and basic commands and functions for running code, saving scripts, clearing the screen, commenting lines, and getting help. It also covers creating and manipulating variables and vectors, importing and exporting data, generating basic plots like bar plots, pie charts and histograms, and importing/exporting data.
The document discusses analyzing stock price data using multiple linear regression and an Adaline neural network. It describes downloading stock price data for four companies from different industries over three years. It details handling missing data by filling in prices using neighboring days. Log returns and z-scores are calculated from the time series data. Multiple linear regression is used to predict closing prices, with the model performance varying by company based on industry relationships. An Adaline neural network is also trained to predict prices using the same input features and error feedback process.
This document discusses curve fitting in Matlab. It introduces the Curve Fitting tool and how to use it to fit data to functions. Key aspects covered include importing data into the tool, selecting fitting functions, viewing and analyzing fit results, and plotting residuals. Examples are provided of fitting data to a sine wave, linear function with and without weights, and examining fit confidence bounds and predictions over different ranges. The document provides a tutorial on using Matlab's Curve Fitting tool to model experimental data with functions.
This document provides an overview of basic concepts in MATLAB including:
- The MATLAB environment and how it is used interactively and for programming.
- Creating and manipulating arrays, matrices, and vectors through built-in functions.
- Saving, loading, selecting, changing, and deleting array elements.
- Performing arithmetic operations like multiplication and addition on arrays.
- Writing user-defined functions and scripts to extend MATLAB's capabilities.
- An example function to calculate the Euclidean distance between a point and multiple other points.
Microsoft Office Execel 2013 Question Bank PresentationLiladhar Meshram
This document contains a series of 26 multiple choice questions about Microsoft Excel 2013. The questions cover a range of Excel topics including toolbars, templates, sharing workbooks, formatting cells, formulas, functions, sorting/filtering data, charts, conditional formatting, and goal seek. After each question the user is prompted to select the correct answer before being shown the next question.
Abstract: This PDSG workshop introduces basic concepts of multiple linear regression in machine learning. Concepts covered are Feature Elimination and Backward Elimination, with examples in Python.
Level: Fundamental
Requirements: Should have some experience with Python programming.
This document provides an introduction to algebraic operations in MATLAB, including scalar calculations using basic arithmetic operators and order of operations, and matrix calculations such as addition, subtraction, multiplication, and transposition. It also discusses special matrices like identity matrices that can be created with functions like eye(), and zeros and ones matrices that can be made with functions like zeros() and ones().
This document discusses different types of mathematical modeling including linear, quadratic, logistic, and exponential models. It describes regression modeling as developing an equation of a curve that best fits a set of known data points using the least squares method to minimize errors between the actual data points and the curve. Specifically, it covers linear regression by fitting an equation of a line through data points, and quadratic regression by using an equation of a parabola to better predict data points that follow a parabolic path.
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
Abstract: This PDSG workshop introduces basic concepts of simple linear regression in machine learning. Concepts covered are Slope of a Line, Loss Function, and Solving Simple Linear Regression Equation, with examples.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
The document discusses linear regression analysis performed on a dataset with variables X and Y. It shows the dataset with X and Y values, plots the data in polynomial, exponential, and linear graphs, and performs manual calculations to derive the linear regression equation. The calculations show setting up and solving simultaneous equations to find the coefficients a, b, and c, yielding the final regression equation Y = 0.7X^2 + 0.25X + 8.27.
Curve fitting is the process of finding the best fit mathematical function for a series of data points. It involves constructing curves or equations that model the relationship between dependent and independent variables. The least squares method is commonly used, which finds the curve that minimizes the sum of the squares of the distances between the data points and the curve. This provides a single curve that best represents the overall trend of the data. Examples of linear and nonlinear curve fitting are provided, along with the process of linearizing nonlinear relationships to apply linear regression techniques.
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...Dr.Summiya Parveen
Outline of the lecture:
Introduction of Regression
Application of Regression
Regression Techniques
Types of Regression
Goodness of fit
MATLAB/MATHEMATICA implementation with some example
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the casual effect relationship between the variables. Regression analysis is an important tool for modelling and analysing data. Here, we fit a curve / line to the data points in such a manner that the differences between the distances of data points from the curve or line is minimized.
By DR. SUMMIYA PARVEEN
This document discusses two types of curve fitting: least squares regression and interpolation. Least squares regression fits a curve to data that has some error or noise by minimizing the sum of squared residuals, while interpolation fits curves that pass through each precise data point. The document then provides details on performing linear, polynomial, and multiple linear regression using the least squares approach to derive regression coefficients. It also covers Newton's divided differences method for polynomial interpolation of data points.
The document discusses various techniques for fitting curves to data including linear regression, polynomial regression, and linearization of nonlinear relationships.
Linear regression finds the line that best fits a set of data points by minimizing the sum of the squared residuals. The normal equations are derived and solved to determine the slope and intercept. Polynomial regression extends this to find the best-fit polynomial curve through the data. An example shows fitting a second-order polynomial. Nonlinear relationships can sometimes be linearized by a transformation of variables to apply linear regression. Examples demonstrate applying these techniques.
The document discusses graphing absolute value functions. It notes that absolute value functions always form a V-shape graph. It provides the general form of an absolute value function as y = a |x - h| + k, where (h, k) are the coordinates of the vertex and a describes the slope. It explains that horizontal shifts are performed by adding or subtracting h from x and vertical shifts add or subtract k from the function. Reflecting the graph over the x-axis flips it vertically. Examples demonstrate shifting graphs left, right, up, down and reflecting over the x-axis.
Absolute value functions have a V-shaped graph. There are three main ways to graph an absolute value function: using a table of values, using a graphing calculator, or interpreting the equation. The vertex of an absolute value graph is always the x-coordinate inside the absolute value signs. To graph, one finds the vertex and then plots additional points on either side, connecting them to form the V-shape. The number outside the absolute value signs moves the graph up or down, while the number inside moves it left or right.
This document provides instructions for performing various operations on matrices using Microsoft Excel. It begins by stating the intended audience and objectives, which are to learn how to add and subtract matrices, multiply a matrix by a scalar, multiply two matrices, and find the determinant and inverse of a matrix using Excel. The document then provides definitions and examples of matrices. It proceeds to explain how to add and subtract matrices by adding the corresponding elements. It also explains how to multiply a matrix by a scalar and multiply two matrices according to the standard rules. The document demonstrates how to use Excel formulas like MMULT, MDETERM, and MINVERSE to calculate the product, determinant, and inverse of matrices. It concludes by providing some additional resources on the topic
This document discusses various polynomial functions in MATLAB. It covers defining and manipulating polynomials, including evaluation, finding roots, addition/subtraction, multiplication/division, derivatives, and curve fitting using polynomial regression. Polynomials in MATLAB are defined as row vectors of coefficients. Key functions include polyval for evaluation, roots for finding roots, conv for multiplication, deconv for division, and polyfit for curve fitting.
This document is a curriculum vitae for Abdullah Omar Ali Aldhaibani. It provides his personal details, education history, work experience, publications, awards, and a recommendation. He obtained his PhD from University Malaysia Perlis in 2015 and is currently a postdoc fellow at University of Technology Malaysia working on 5G networks. He has over 20 publications in journals and conferences and has received several awards for his research on optical wireless technologies and fiber networks.
El documento presenta una historia del desarrollo de las computadoras a través de las diferentes generaciones, desde los primeros dispositivos mecánicos como el ábaco y la máquina calculadora de Pascal hasta las computadoras modernas de sexta generación. Destaca invenciones clave como la máquina analítica de Babbage, la tarjeta perforada de Hollerith y el microprocesador, y cómo cada generación trajo avances en miniaturización, velocidad y capacidad de procesamiento.
This document provides an overview of key concepts from Chapter 1 of a linear functions textbook, including:
- Solving linear equations and using data to create scatterplots and graph lines
- Finding equations of lines from their graphs or intercepts
- Using linear models to represent real-world situations like business costs and revenues
- Identifying the slope, intercepts, domain and range of linear equations and determining if sets of points represent functions
The chapter content is explained through examples like modeling the costs and profits of a golf cart refurbishing business.
This document provides an introduction to using R and RStudio. It discusses installing R and RStudio, the four windows in RStudio (source editor, console, environment/history, and plots/files), and basic commands and functions for running code, saving scripts, clearing the screen, commenting lines, and getting help. It also covers creating and manipulating variables and vectors, importing and exporting data, generating basic plots like bar plots, pie charts and histograms, and importing/exporting data.
The document discusses analyzing stock price data using multiple linear regression and an Adaline neural network. It describes downloading stock price data for four companies from different industries over three years. It details handling missing data by filling in prices using neighboring days. Log returns and z-scores are calculated from the time series data. Multiple linear regression is used to predict closing prices, with the model performance varying by company based on industry relationships. An Adaline neural network is also trained to predict prices using the same input features and error feedback process.
This document discusses curve fitting in Matlab. It introduces the Curve Fitting tool and how to use it to fit data to functions. Key aspects covered include importing data into the tool, selecting fitting functions, viewing and analyzing fit results, and plotting residuals. Examples are provided of fitting data to a sine wave, linear function with and without weights, and examining fit confidence bounds and predictions over different ranges. The document provides a tutorial on using Matlab's Curve Fitting tool to model experimental data with functions.
This document provides an overview of basic concepts in MATLAB including:
- The MATLAB environment and how it is used interactively and for programming.
- Creating and manipulating arrays, matrices, and vectors through built-in functions.
- Saving, loading, selecting, changing, and deleting array elements.
- Performing arithmetic operations like multiplication and addition on arrays.
- Writing user-defined functions and scripts to extend MATLAB's capabilities.
- An example function to calculate the Euclidean distance between a point and multiple other points.
Microsoft Office Execel 2013 Question Bank PresentationLiladhar Meshram
This document contains a series of 26 multiple choice questions about Microsoft Excel 2013. The questions cover a range of Excel topics including toolbars, templates, sharing workbooks, formatting cells, formulas, functions, sorting/filtering data, charts, conditional formatting, and goal seek. After each question the user is prompted to select the correct answer before being shown the next question.
Abstract: This PDSG workshop introduces basic concepts of multiple linear regression in machine learning. Concepts covered are Feature Elimination and Backward Elimination, with examples in Python.
Level: Fundamental
Requirements: Should have some experience with Python programming.
This document provides an introduction to algebraic operations in MATLAB, including scalar calculations using basic arithmetic operators and order of operations, and matrix calculations such as addition, subtraction, multiplication, and transposition. It also discusses special matrices like identity matrices that can be created with functions like eye(), and zeros and ones matrices that can be made with functions like zeros() and ones().
This document discusses different types of mathematical modeling including linear, quadratic, logistic, and exponential models. It describes regression modeling as developing an equation of a curve that best fits a set of known data points using the least squares method to minimize errors between the actual data points and the curve. Specifically, it covers linear regression by fitting an equation of a line through data points, and quadratic regression by using an equation of a parabola to better predict data points that follow a parabolic path.
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
Abstract: This PDSG workshop introduces basic concepts of simple linear regression in machine learning. Concepts covered are Slope of a Line, Loss Function, and Solving Simple Linear Regression Equation, with examples.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
The document discusses linear regression analysis performed on a dataset with variables X and Y. It shows the dataset with X and Y values, plots the data in polynomial, exponential, and linear graphs, and performs manual calculations to derive the linear regression equation. The calculations show setting up and solving simultaneous equations to find the coefficients a, b, and c, yielding the final regression equation Y = 0.7X^2 + 0.25X + 8.27.
Curve fitting is the process of finding the best fit mathematical function for a series of data points. It involves constructing curves or equations that model the relationship between dependent and independent variables. The least squares method is commonly used, which finds the curve that minimizes the sum of the squares of the distances between the data points and the curve. This provides a single curve that best represents the overall trend of the data. Examples of linear and nonlinear curve fitting are provided, along with the process of linearizing nonlinear relationships to apply linear regression techniques.
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...Dr.Summiya Parveen
Outline of the lecture:
Introduction of Regression
Application of Regression
Regression Techniques
Types of Regression
Goodness of fit
MATLAB/MATHEMATICA implementation with some example
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the casual effect relationship between the variables. Regression analysis is an important tool for modelling and analysing data. Here, we fit a curve / line to the data points in such a manner that the differences between the distances of data points from the curve or line is minimized.
By DR. SUMMIYA PARVEEN
This document discusses two types of curve fitting: least squares regression and interpolation. Least squares regression fits a curve to data that has some error or noise by minimizing the sum of squared residuals, while interpolation fits curves that pass through each precise data point. The document then provides details on performing linear, polynomial, and multiple linear regression using the least squares approach to derive regression coefficients. It also covers Newton's divided differences method for polynomial interpolation of data points.
The document discusses various techniques for fitting curves to data including linear regression, polynomial regression, and linearization of nonlinear relationships.
Linear regression finds the line that best fits a set of data points by minimizing the sum of the squared residuals. The normal equations are derived and solved to determine the slope and intercept. Polynomial regression extends this to find the best-fit polynomial curve through the data. An example shows fitting a second-order polynomial. Nonlinear relationships can sometimes be linearized by a transformation of variables to apply linear regression. Examples demonstrate applying these techniques.
The document discusses graphing absolute value functions. It notes that absolute value functions always form a V-shape graph. It provides the general form of an absolute value function as y = a |x - h| + k, where (h, k) are the coordinates of the vertex and a describes the slope. It explains that horizontal shifts are performed by adding or subtracting h from x and vertical shifts add or subtract k from the function. Reflecting the graph over the x-axis flips it vertically. Examples demonstrate shifting graphs left, right, up, down and reflecting over the x-axis.
Absolute value functions have a V-shaped graph. There are three main ways to graph an absolute value function: using a table of values, using a graphing calculator, or interpreting the equation. The vertex of an absolute value graph is always the x-coordinate inside the absolute value signs. To graph, one finds the vertex and then plots additional points on either side, connecting them to form the V-shape. The number outside the absolute value signs moves the graph up or down, while the number inside moves it left or right.
This document provides instructions for performing various operations on matrices using Microsoft Excel. It begins by stating the intended audience and objectives, which are to learn how to add and subtract matrices, multiply a matrix by a scalar, multiply two matrices, and find the determinant and inverse of a matrix using Excel. The document then provides definitions and examples of matrices. It proceeds to explain how to add and subtract matrices by adding the corresponding elements. It also explains how to multiply a matrix by a scalar and multiply two matrices according to the standard rules. The document demonstrates how to use Excel formulas like MMULT, MDETERM, and MINVERSE to calculate the product, determinant, and inverse of matrices. It concludes by providing some additional resources on the topic
This document discusses various polynomial functions in MATLAB. It covers defining and manipulating polynomials, including evaluation, finding roots, addition/subtraction, multiplication/division, derivatives, and curve fitting using polynomial regression. Polynomials in MATLAB are defined as row vectors of coefficients. Key functions include polyval for evaluation, roots for finding roots, conv for multiplication, deconv for division, and polyfit for curve fitting.
This document is a curriculum vitae for Abdullah Omar Ali Aldhaibani. It provides his personal details, education history, work experience, publications, awards, and a recommendation. He obtained his PhD from University Malaysia Perlis in 2015 and is currently a postdoc fellow at University of Technology Malaysia working on 5G networks. He has over 20 publications in journals and conferences and has received several awards for his research on optical wireless technologies and fiber networks.
El documento presenta una historia del desarrollo de las computadoras a través de las diferentes generaciones, desde los primeros dispositivos mecánicos como el ábaco y la máquina calculadora de Pascal hasta las computadoras modernas de sexta generación. Destaca invenciones clave como la máquina analítica de Babbage, la tarjeta perforada de Hollerith y el microprocesador, y cómo cada generación trajo avances en miniaturización, velocidad y capacidad de procesamiento.
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)Aditya Rana
This document provides an overview of the N K Minda Group, an automotive components manufacturer based in India. It details the group's vision, mission, milestones, facilities, partnerships, products, financials, capabilities, and team. Key points include:
- The group has annual turnover of Rs. 31,500 million and operates 37 plants across India and internationally.
- Its vision is to become a global benchmark in quality, cost, delivery, and services, with a target turnover of Rs. 100 billion by 2017-18.
- It manufactures a wide range of automotive components and has partnerships with global technology leaders.
- Major product lines include switches, lighting, horns, safety components, batteries
Mapa conceptual simulacion de audiencia en juicio oral equipo 5Yorjan Coa
Este documento describe los pasos de una audiencia de juicio oral y público. Explica que en esta audiencia, el tribunal escucha pruebas como testigos, peritos y evidencia documental presentadas por la fiscalía y la defensa. El acusado también puede dar una declaración. Después de los alegatos finales de la fiscalía y la defensa, el juez fija una fecha para dar el veredicto de condena o absolución del acusado.
Oorja Systems Consultants provides used lube oil recycling services using environmentally sound technologies. Used lube oil constitutes around 80% recyclable lubricating oil. Government regulations mandate using processes like vacuum distillation with clay treatment or thin film evaporation to recycle waste oil. Oorja has developed a process using high vacuum distillation and thin film evaporation to remove water, diesel and light fuel fractions. The remaining lubricating oil fraction is further purified through clay free and deodorizing treatments to produce recycled lube oil meeting international standards.
Similar to Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?
This document provides an overview of plotting and image processing capabilities in Matlab. It discusses how to generate basic scatter plots and customize axis properties. It also explains how digital images are constructed as arrays and can be displayed, rotated, and converted to grayscale using commands like plot, surf, image, and imagesc. The document demonstrates plotting multiple lines and images on the same figure. It describes how image processing techniques like Sobel filtering can be used to detect edges in an image.
This document discusses exploring and visualizing data in Microsoft Excel. It covers topics such as creating charts, sorting and filtering data, statistical analysis methods for summarizing data, and using PivotTables and PivotCharts. Examples demonstrate how to construct frequency distributions, calculate percentiles and quartiles, filter records, and create cross-tabulations and charts from a structured data set.
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
Using Microsoft Excel for Weibull Analysis by William DornerMelvin Carter
I placed the original Quality Digest article (1/1/1999) in Word to clarify the equations used to perform analysis on a data set have Weibull distribution characteristics.
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docxagnesdcarey33086
SAMPLE QUESTION:
Exercise 1: Consider the function
f (x,C)=
sin(C x)
Cx
(a) Create a vector x with 100 elements from -3*pi to 3*pi. Write f as an inline or anonymous function
and generate the vectors y1 = f(x,C1), y2 = f(x,C2) and y3 = f(x,C3), where C1 = 1, C2 = 2 and
C3 = 3. Make sure you suppress the output of x and y's vectors. Plot the function f (for the three
C's above), name the axis, give a title to the plot and include a legend to identify the plots. Add a
grid to the plot.
(b) Without using inline or anonymous functions write a function+function structure m-file that does
the same job as in part (a)
SAMPLE LAB WRITEUP:
MAT 275 MATLAB LAB 1 NAME: __________________________
LAB DAY and TIME:______________
Instructor: _______________________
Exercise 1
(a)
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
f= @(x,C) sin(C*x)./(C*x) % C will be just a constant, no need for ".*"
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % supressing the y's
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
Command window output:
f =
@(x,C)sin(C*x)./(C*x)
C1 =
1
C2 =
2
C3 =
3
(b)
M-file of structure function+function
function ex1
x = linspace(-3*pi,3*pi); % generating x vector - default value for number
% of pts linspace is 100
C1 = 1, C2 = 2, C3 = 3 % Using commans to separate commands
y1 = f(x,C1); y2 = f(x,C2); y3 = f(x,C3); % function f is defined below
plot(x,y1,'b.-', x,y2,'ro-', x,y3,'ks-') % using different markers for
% black and white plots
xlabel('x'), ylabel('y') % labeling the axis
title('f(x,C) = sin(Cx)/(Cx)') % adding a title
legend('C = 1','C = 2','C = 3') % adding a legend
grid on
end
function y = f(x,C)
y = sin(C*x)./(C*x);
end
Command window output:
C1 =
1
C2 =
2
C3 =
3
Joe Bob
Mon lab: 4:30-6:50
Lab 3
Exercise 1
(a) Create function M-file for banded LU factorization
function [L,U] = luband(A,p)
% LUBAND Banded LU factorization
% Adaptation to LUFACT
% Input:
% A diagonally dominant square matrix
% Output:
% L,U unit lower triangular and upper triangular such that LU=A
n = length(A);
L = eye(n); % ones on diagonal
% Gaussian Elimination
for j = 1:n-1
a = min(j+p.
Statistics is both the science of uncertainty and the technology.docxrafaelaj1
Statistics is both the science of uncertainty and the technology of extracting information from data.
A statistic is a summary measure of data.
Descriptive statistics are methods that describe and summarize data.
Microsoft Excel supports statistical analysis in two ways:
1. Statistical functions
2. Analysis Toolpak add-in
Statistical Methods for Summarizing Data
A frequency distribution is a table that shows the number of observations in each of several nonoverlapping groups.
Categorical variables naturally define the groups in a frequency distribution.
To construct a frequency distribution, we need only count the number of observations that appear in each category.
This can be done using the Excel COUNTIF function.
Frequency Distributions for Categorical Data
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
List the item names in a column on the spreadsheet.
Use the function =COUNTIF($D$4:$D$97,cell_reference), where cell_reference is the cell containing the item name
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
Construct a column chart to visualize the frequencies.
Relative frequency is the fraction, or proportion, of the total.
If a data set has n observations, the relative frequency of category i is:
We often multiply the relative frequencies by 100 to express them as percentages.
A relative frequency distribution is a tabular summary of the relative frequencies of all categories.
Relative Frequency Distributions
Example 3.17: Constructing a Relative Frequency Distribution for Items in the Purchase Orders Database
First, sum the frequencies to find the total number (note that the sum of the frequencies must be the same as the total number of observations, n).
Then divide the frequency of each category by this value.
For numerical data that consist of a small number of discrete values, we may construct a frequency distribution similar to the way we did for categorical data; that is, we simply use COUNTIF to count the frequencies of each discrete value.
Frequency Distributions for Numerical Data
In the Purchase Orders data, the A/P terms are all whole numbers 15, 25, 30, and 45.
Example 3.18: Frequency and Relative Frequency Distribution for A/P Terms
A graphical depiction of a frequency distribution for numerical data in the form of a column chart is called a histogram.
Frequency distributions and histograms can be created using the Analysis Toolpak in Excel.
Click the Data Analysis tools button in the Analysis group under the Data tab in the Excel menu bar and select Histogram from the list.
Excel Histogram Tool
Specify the Input Range corresponding to the data. If you include the column header, then also check the Labels box so Excel knows that the range contains a label. The Bin Range defines the groups (Excel calls these “bins”) used for the frequency distribution.
Histogra.
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docxYASHU40
ENGR 102B: Microsoft Excel Proficiency Levels
Please have your instructor or TA initial each level as you complete it. If you need additional help, ask the TAs or use the help guide within Excel.
Once you master Excel Levels I through IV, you can note Excel as a skill on your resume!
Please see D2L Content for this week for your Excel Homework assignment (individual), which is due via D2L Dropbox by the due date specified in the D2L News for your section.
If you use a Mac, please be sure to submit your homework in a format that the grader and instructor can open on a PC.
Level I: Basic Functions Initials _______
1. Calculating an Average: Calculate the arithmetic average of the 5 values listed below. Enter the values in cells A2 through A6. Place a descriptive label in cell A1.
3.6, 3.8, 3.5, 3.7, 3.6
First, calculate the average the long way, by summing the values and dividing by 5:
You will enter the following formula into a blank cell to accomplish this:
=(A2+A3+A4+A5+A6)/5
Second, calculate the average using Excel’s AVERAGE( ) function by entering the following formula in a cell:
=AVERAGE(cellrange)
Replace the “cellrange” with the actual addresses in your spreadsheet of the range of cells holding the five values (i.e., for this problem, the cell range is A2:A6).
2. Determining Velocities (in kph): Some friends at the University of Calgary are coming south for spring break. Help them avoid a speeding ticket by completing a velocity conversion worksheet that calculates the conversion from mph to kph in increments of 10 from 10 to 100. A conversion factor you will need is 0.62 miles/km; you will need this factor to convert from miles/hour to km/hour. Place the conversion factor in its own cell and then reference it in your conversion calculations using absolute cell referencing (e.g., $C$2). Refer to the CBT video on Absolute and Relative Cell Referencing from the “Preparation for the Excel Workshop” assignment if you don’t remember how to do this.
Level II: Advanced Functions Initials _______
1. Projectile Motion I: (See following page for Fig. 1 Excel chart) A projectile is launched at the angle 35o from the horizontal with a velocity equal to 30 m/s. Neglecting air resistance and assuming a horizontal surface, determine how far away from the launch site the projectile will land.
To answer this problem, you will need:
1. Excel’s trigonometry functions to handle the 35o angle, and
2. Equations relating distance to velocity and acceleration
When velocity is constant, as in the horizontal motion of our particle (since we’re neglecting air resistance), the distance traveled is simply the initial horizontal velocity times the time of flight:
(Equation 1)
What keeps the projectile from flying forever is gravity. Since the gravitational acceleration is constant, the vertical distance traveled becomes
(Equation 2)
Because the projectile ends up back on the ground, the final value of y is zero (a hor ...
More instructions for the lab write-up1) You are not obli.docxgilpinleeanna
More instructions for the lab write-up:
1) You are not obligated to use the 'diary' function. It was presented only for you convenience. You
should be copying and pasting your code, plots, and results into some sort of "Word" type editor that
will allow you to import graphs and such. Make sure you always include the commands to generate
what is been asked and include the outputs (from command window and plots), unless the problem
says to suppress it.
2) Edit this document: there should be no code or MATLAB commands that do not pertain to the
exercises you are presenting in your final submission. For each exercise, only the relevant code that
performs the task should be included. Do not include error messages. So once you have determined
either the command line instructions or the appropriate script file that will perform the task you are
given for the exercise, you should only include that and the associated output. Copy/paste these into
your final submission document followed by the output (including plots) that these MATLAB
instructions generate.
3) All code, output and plots for an exercise are to be grouped together. Do not put them in appendix, at
the end of the writeup, etc. In particular, put any mfiles you write BEFORE you first call them.
Each exercise, as well as the part of the exercises, is to be clearly demarked. Do not blend them all
together into some sort of composition style paper, complimentary to this: do NOT double space.
You can have spacing that makes your lab report look nice, but do not double space sections of text
as you would in a literature paper.
4) You can suppress much of the MATLAB output. If you need to create a vector, "x = 0:0.1:10" for
example, for use, there is no need to include this as output in your writeup. Just make sure you
include whatever result you are asked to show. Plots also do not have to be a full, or even half page.
They just have to be large enough that the relevant structure can be seen.
5) Before you put down any code, plots, etc. answer whatever questions that the exercise asks first.
You will follow this with the results of your work that support your answer.
SAMPLE QUESTION:
Exercise 1: Consider the function
f (x,C)=
sin(C x)
Cx
(a) Create a vector x with 100 elements from -3*pi to 3*pi. Write f as an inline or anonymous function
and generate the vectors y1 = f(x,C1), y2 = f(x,C2) and y3 = f(x,C3), where C1 = 1, C2 = 2 and
C3 = 3. Make sure you suppress the output of x and y's vectors. Plot the function f (for the three
C's above), name the axis, give a title to the plot and include a legend to identify the plots. Add a
grid to the plot.
(b) Without using inline or anonymous functions write a function+function structure m-file that does
the same job as in part (a)
SAMPLE LAB WRITEUP:
MAT 275 MATLAB LAB 1 NAME: ...
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docxdessiechisomjj4
Statistics Questions to Answer.doc.rtf
2
*Note: An Excel Workbook has also been uploaded. Within that workbook are 8 XLS files which are included in 8 separate tabs. These files will be needed to answer most of the questions.This work is due Friday, September 19th
Q1)Fill in the blanks (show your work).
Variable
N
Mean
Median
TrMean
StDev
haircut
171
23.17
17.00
21.14
18.20
sleep
171
6.6477
7.0000
6.6487
0.8396
age
171
27.421
27.000
27.098
3.646
Correlations:haircut,sleep, age
haircut
sleep
sleep
-0.117
age
0.062
(1)
Covariances:haircut,sleep, age
haircut
sleep
age
haircut
(2)_
sleep
-1.79232
0.70491
age
4.12314
-0.45372
13.29226
Blank 1 =
Blank 2 =
Q2)Is the following statement correct? Explain why or why not.
“A correlation of 0 implies that no relationship exists between the two variables under study.”
Q3)Does how long children remain at the lunch table help predict how much they eat? The data in file lunchtime.xls (File is in Tab#1 of Excel Workbook) gives information on 20 toddlers observed over several months at a nursery school. “Time” is the average number of minutes a child spent at the table when lunch was served. “Calories” is the average number of calories the child consumed during lunch, calculated from careful observation of what the child ate each day.
Findthecorrelationforthesedata.
Supposeweweretorecordtimeatthetableinhoursratherthaninminutes.Howwouldthecorrelationchange?Why?
Writeasentenceortwoexplainingwhatthiscorrelationmeansforthesedata.Remembertowriteaboutfoodconsumptionbytoddlersratherthanaboutcorrelationcoefficients.
Oneanalystconcluded,“Itisclearfromthiscorrelationthattoddlerswhospendmoretimeatthetableeatless.Evidentlysomethingaboutbeingatthetablecausesthemtolosetheirappetites.”Explainwhythisexplanationisnotanappropriateconclusionfromwhatweknowaboutthedata.
Q4) In file bach.xls (File is in Tab#2 of Excel Workbook) is state by state data (plus Washington, DC) on percentage of residents over he age of 25 who have at least a bachelor’s degree and median salary.
Whatisthecorrelationbetweenthesetwovariables?
Produceascatterplotofthedatawithpercentagewithbachelor’sdegreeontheXaxis.Noticetheoutlier?Whodoesthatpointbelongto?Can youthinkofanyreasonswhythislocationmighthaveahighpercentageofresidentswithabachelor’sdegreebutalowerthanexpectedmedianincome?
Removetheoutlierpointfoundin(b)andrecalculatethecorrelation.Howdothetwocorrelationvaluescompare?Whatdoesthisillustrateaboutcorrelation?
Q5) The mean rate of return and standard deviation of Stocks 1 and 2 are given below:
Stock 1 Stock 2
Mean 10 % 20 %
Standard deviation 20 % 30 %
Giventhatthecorrelationbetweenstocksis-1.0,findrisk(standarddeviation)and(mean)returnofaportfoliothatthat60%inStock1and40%instock2.
Giventhatthecorrelationbetweenstocksis0,findrisk(standarddeviation)and(mean)returnofaportfoliothatthat60%inStock1and40%instock2.
Giventhatthecorrelationbetweenstocksis1,findrisk(standarddeviation)and(mean)returnofaportfoliothatthat60%.
MAT 240 Random Sampling in Excel Tutorial This tutorial wiAbramMartino96
MAT 240 Random Sampling in Excel Tutorial
This tutorial will guide you though the steps necessary to collect a random sample of a data set to put on
a new sheet.
1. Open your data set in Excel. Be sure the Analysis toolpak is enabled. Steps for how to do this are
available on the Microsoft support site.
2. To find a random sample, you first need to insert the =rand() function an empty column next to
your data. In the example being shown, it is column G. To do this, select the target cell and type
in =rand() then press enter.
3. Double click the Fill handle (little square icon) at the bottom right side of the highlighted cell to
copy the formula through to the bottom of the data set. This will copy this formula to each row
of data.
4. Sort your new column to rearrange the data into a random order. To do this, select the data
within your column, then click the Sort & Filter button from the Home ribbon and choose Sort
https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4
Smallest to Largest.
5. A dialog box will open asking if you what you want to do. Select to Expand the selection and
click Sort.
6. Capture your sample size by selecting the amount of rows you are sampling. A sample of 50
would mean you should select the first 50 rows of data.
a. By selecting only the first cell of data in the first column and dragging down, Excel will
count the number of rows for you.
b. Once you have the correct number of rows, then drag to the right to highlight all the
data in the appropriate number of rows.
7. Cut and paste this selected data set onto a new sheet and you will have your random sample
separated from the main data set.
8. In the Descriptive statistics window, select input range field, then select all your numerical data
9. Then check the Summary Statistics box and click ok
10. You now should see a new sheet with just your descriptive statistics listed in a chart. Change the
titles of the columns to their respective names from your data: median listing price, median dollars
per square foot, median square feet. And remove any extraneous information that is not needed for
this project.
MAT 240 Random Sampling in Excel Tutorial
MAT 240 Scatterplots in Excel Tutorial
This tutorial will guide you though the steps necessary to create scatterplots using your data. It will also
walk you through inserting a linear trend line and inserting the regression equation and the R-squared
value on the chart.
1. Open your data set in Excel.
2. Select all the data for the two variables you are targeting. (example: median listing price & Median
square feet)
a. Tip: holding down the CTRL button while selecting your data will allow you to select two
columns of data that are not next to each other
3. On the Insert tab select Recommended Charts button
4. This will bring up the insert chart dialog box prompting you to ...
MAT 240 Random Sampling in Excel Tutorial This tutorial wiAbramMartino96
MAT 240 Random Sampling in Excel Tutorial
This tutorial will guide you though the steps necessary to collect a random sample of a data set to put on
a new sheet.
1. Open your data set in Excel. Be sure the Analysis toolpak is enabled. Steps for how to do this are
available on the Microsoft support site.
2. To find a random sample, you first need to insert the =rand() function an empty column next to
your data. In the example being shown, it is column G. To do this, select the target cell and type
in =rand() then press enter.
3. Double click the Fill handle (little square icon) at the bottom right side of the highlighted cell to
copy the formula through to the bottom of the data set. This will copy this formula to each row
of data.
4. Sort your new column to rearrange the data into a random order. To do this, select the data
within your column, then click the Sort & Filter button from the Home ribbon and choose Sort
https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4
Smallest to Largest.
5. A dialog box will open asking if you what you want to do. Select to Expand the selection and
click Sort.
6. Capture your sample size by selecting the amount of rows you are sampling. A sample of 50
would mean you should select the first 50 rows of data.
a. By selecting only the first cell of data in the first column and dragging down, Excel will
count the number of rows for you.
b. Once you have the correct number of rows, then drag to the right to highlight all the
data in the appropriate number of rows.
7. Cut and paste this selected data set onto a new sheet and you will have your random sample
separated from the main data set.
8. In the Descriptive statistics window, select input range field, then select all your numerical data
9. Then check the Summary Statistics box and click ok
10. You now should see a new sheet with just your descriptive statistics listed in a chart. Change the
titles of the columns to their respective names from your data: median listing price, median dollars
per square foot, median square feet. And remove any extraneous information that is not needed for
this project.
MAT 240 Random Sampling in Excel Tutorial
MAT 240 Scatterplots in Excel Tutorial
This tutorial will guide you though the steps necessary to create scatterplots using your data. It will also
walk you through inserting a linear trend line and inserting the regression equation and the R-squared
value on the chart.
1. Open your data set in Excel.
2. Select all the data for the two variables you are targeting. (example: median listing price & Median
square feet)
a. Tip: holding down the CTRL button while selecting your data will allow you to select two
columns of data that are not next to each other
3. On the Insert tab select Recommended Charts button
4. This will bring up the insert chart dialog box prompting you to ...
This document provides information on various quality control tools including check sheets, Pareto diagrams, cause and effect diagrams, histograms, stratification, scatter diagrams, and control charts. It explains how to construct and interpret each tool and how they can be used to gather and analyze data to identify problems, determine causes, and evaluate solutions. The tools help quality professionals make data-driven decisions to improve processes and prevent issues.
- The class outline covers regression analysis, including determining the R-squared value and interpreting regression output from Excel.
- Regression models the relationship between a dependent variable (sales) and independent variables (price and other factors) using estimated coefficients.
- The R-squared value measures the explanatory power of the regression model, with higher values indicating more of the variation in the dependent variable is explained by the independent variables.
- Excel can be used to perform the regression analysis and output statistics including coefficients, F-statistics from the ANOVA table, and p-values to interpret the significance of each coefficient.
This document provides instructions for using a graphing calculator to perform linear regression on a dataset and find the line of best fit. It describes entering paired x and y data values into separate lists, using the LinReg(ax+b) function to determine the regression equation, optionally creating a scatter plot of the original data and regression line, and using the line equation to forecast values. As an example, it analyzes a dataset of alternative-fueled vehicles in the US from 1997 to predict the number in 2014.
Using microsoft excel for weibull analysisMelvin Carter
A simple introduction to reliability analysis of components. Though this lacks explanations of the calculated steps it shows how simple analysis can be. Note that it only addresses the Weibull distribution. It does share how to look elsewhere if the Weibull shape parameter is not near the ideal three(3).
If you are looking for business statistics homework help, Statisticshelpdesk is your rightest destination. Our experts are capable of solving all grades of business statistics homework with best 100% accuracy and originality. We charge reasonable.
In this tutorial, we discuss how to do a regression analysis in Excel. I will teach you how to activate the regression analysis feature, what are the functions and methods we can use to do a regression analysis in Excel and most importantly, how to interpret the regression analysis results. Source: https://tinytutes.com/tutorials/regression-analysis-in-excel/
This document provides an overview of MATLAB including:
1. How to perform operations interactively or using script files
2. Entering commands and expressions using the command window
3. Examples of arithmetic, precedence rules, and assignment
4. Common mathematical functions and operations on arrays and matrices
5. Saving, loading, and managing variables and files in MATLAB sessions
Exploratory data analysis is an approach consisting of tools that help you understand your data easily. These tools can be used with minimal knowledge of statistics.
EDA tools are presented here by The School of Continuous Improvement with the main purpose of anyone wanting to use these tools to be able to use them.
I am Simon M. I am an Environmental Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Environmental Engineering, Glasgow University, UK. I have been helping students with their assignments for the past 8 years. I solve assignments related to Environmental Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Environmental Engineering Assignments.
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A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
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The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
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Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?
1. Dual vertical axes chart scaling algorithm
comparison with Excel’s
May 2016
Confidential
Questions/Comments?
Please contact Jennifer Lin
at graphician1122@gmail.com
1
2. ®
100
36
100
84
36
52
68
84
100
36
52
68
84
100
Q1 Q2
A B
100
36
100
84
75
80
85
90
95
100
105
0
20
40
60
80
100
120
Q1 Q2
A B
Executive Summary
2
Graphician is pleased to present one of our 5 US-patented algorithms.
An algorithm to truthfully present the intelligence of data graphically on dual vertical axes chart.
An algorithm can be easily incorporated into conventional tableted data applications such as Excel.
-16-64
Excel algorithm mispresents a decrease from 100 to 36 and
a decrease from 100 to 84 is the same.
Graphician algorithmExcel dual vertical axes chart
-16
-64
Graphician algorithm shows a decrease from 100 to 36 is
more than a decrease from 100 to 84.
3. ®
-91
100
94
90
92
94
96
98
100
102
-150
-100
-50
0
50
100
150
Q1 Q2
A B
What algorithm Excel adopts for dual axes chart now?
3
Excel single vertical axis chart Excel dual vertical axes chart
-6-191
Excel adopts the same algorithm for single vertical axis chart when setting the scales of dual vertical axes chart,
thus the elongations of both axes are not coordinated to be the same.
100100
-91
-150
-100
-50
0
50
100
150
Q1 Q2
A
5. ®
No negative base value
Commonly used “Base Value” method misleads too
5
Graphician algorithm is the only solution which can correctly present the interaction/relationship between the data sets in all
kinds of situations on chart.
Base Value
Period A B
Q1 100 20
Q2 20 100
Change -80 +80
100%
20%
100%
500%
0%
100%
200%
300%
400%
500%
600%
Q1 Q2
A B
Line A’s decrease should be equal to
Line B’s increase
100
2020
100
20
40
60
80
100
20
40
60
80
100
Q1 Q2
A B
With negative base value Base Value
100%
-80%
100%
-300%
-400%
-300%
-200%
-100%
0%
100%
200%
Q1 Q2
A B
Line A’s decrease should be more
than Line B’s decrease
100
-80
-20
-100 -100
-80
-60
-40
-20
0
20
40
60
80
-80
-60
-40
-20
0
20
40
60
80
100
Q1 Q2
A B
Period A B
Q1 100 -20
Q2 -80 -100
Change -180 -80
Graphician
Graphician
6. ®
6
Misled by chart (case 1):
What drove the increase of sales?
Period Selling Price Units Sold Sales
Q1 84 9,762 820,000
Q2 100 10,000 1,000,000
Excel
Excel algorithm presents as the selling price and units sold both increased, but there was no much increase on sales.
820,000
1,000,000
84
100
75
80
85
90
95
100
105
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Q1 Q2
Sales Selling Price
Excel
820,000
1,000,000
9,762
10,000
9,600
9,650
9,700
9,750
9,800
9,850
9,900
9,950
10,000
10,050
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Q1 Q2
Sales Units Sold
7. ®
7
Misled by chart (case 1):
What drove the increase of sales? (cont.)
Period Selling Price Units Sold Sales
Q1 84 9,762 820,000
Q2 100 10,000 1,000,000
Graphician
820,000
1,000,000
9,762
10,000
8,200
8,560
8,920
9,280
9,640
10,000
820,000
856,000
892,000
928,000
964,000
1,000,000
Q1 Q2
Sales Units Sold
Graphician
820,000
1,000,000
84
100
82.0
85.6
89.2
92.8
96.4
100.0
820,000
856,000
892,000
928,000
964,000
1,000,000
Q1 Q2
Sales Selling Price
Graphician algorithm presents the fact that the main driver of increased sales is the increased selling price.
9. ®
Misled by chart (case 2):
What drove the growth of number of employed labor?
9
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
110,000
115,000
120,000
125,000
130,000
135,000
140,000
145,000
150,000
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Employed in all industries
Employed in non-agricultural industries
Excel Graphician auto scaling algorithm
119,651
124,511
129,371
134,232
139,092
143,952
121,392
126,323
131,254
136,185
141,116
146,047
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Employed in all industries
Employed in non-agricultural industries
Graphician
18.7%
20.3%
18.7% 20.3%
Graphician shows the fact that increase of employed in all
industries was mainly contributed by increase of
employed in non-agricultural industries in modern society.
Excel shows there was few relationship between the 2
data sets.
Source: U.S. Bureau of Labor Statistics
10. ®
Misled by chart (case 3):
Which stock performed better?
10
Source: Stock price data base
30.53
31.76
32.98
34.21
35.43
36.66
24.15
25.12
26.09
27.06
28.03
29.00
3/23/2004
4/23/2004
5/23/2004
6/23/2004
7/23/2004
8/23/2004
9/23/2004
10/23/2004
Microsoft Dell
31.00
32.00
33.00
34.00
35.00
36.00
37.00
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
3/23/2004
4/23/2004
5/23/2004
6/23/2004
7/23/2004
8/23/2004
9/23/2004
10/23/2004
Microsoft Dell
Excel Graphician auto scaling algorithmGraphician
20.1%
11.1%
20.1%
11.1%
Graphician shows the movement of the 2 stocks in same
elongation and the fact that Microsoft’s share price
performed better than Dell’s.
Microsoft’s share price grew 20.1% while Dell’s grew only
11.1%. However, the chart indicated that Dell’s price
movement was much larger than Microsoft’s.
11. ®
100
36
1000
840
360
520
680
840
1000
36
52
68
84
100
Q1 Q2
A B
100
36
1000
840
750
800
850
900
950
1000
1050
0
20
40
60
80
100
120
Q1 Q2
A B
Key steps of the algorithm
11
A B
Q1 100 1000
Q2 36 840
Original E-value
0.64
= (100-36)/100
0.16
= (1000-840)/1000
Upper limit
of the axis
100
= A’s Max
1000
= B’s Max
Lower limit
of the axis
36
= A’s Min
360
= B’s Max ×
A’s Min/A’s Max
New E-value N/A
0.64
= (1000-360)/1000
Note: (1) Which of the upper and lower limit should be unchanged and how to calculate the other limit is disclosed in the flowchart next page.
Calculate the E-value of each sequence (A: 0.64; B:0.16)
Set upper and lower limits of the axis with larger E-value (A: 0.64)
as its Max & Min (100 & 36)
Set one of the upper and lower limit of the axis with smaller E-
value (B: 0.16) unchanged (1000) (1)
Calculate the other limit of the axis with smaller E-value (360).
B’ new E-value (0.64) equals to A’s original E-value (0.64) (1)
4 key steps
1
2
3
4
1
2 3
4
1
2
4
2
2
3
4
-16%
-64%-16%-64%
Excel algorithm Graphician algorithm
12. ®
Step 3 & 4 of the algorithm:
Which of the upper and lower limit should be changed and how?
│Max value of 1st data set │
≥
│Min value of 1st data set │
│Max value of 2nd data set │
≥
│Min value of 2nd data set │
Adjust lower limit
of 2nd data set’s axis
= 2nd data set max value
× 1st data set mini value
÷ 1st data set max value
│Max value of 2nd data set │
≥
│Min value of 2nd data set │
Yes
Yes Yes
No
No No
Adjust upper limit
of 2nd data set’s axis
= 2nd data set mini value
× 1st data set mini value
÷ 1st data set max value
Adjust lower limit
of 2nd data set’s axis
= 2nd data set max value
× 1st data set max value
÷ 1st data set mini value
Adjust upper limit
of 2nd data set’s axis
= 2nd data set mini value
× 1st data set max value
÷ 1st data set mini value
i. 1st data set refers to the data set
with larger E-Value
ii. 2nd data set refers to the data set
with smaller E-Value
Upper limit of 2nd data
set’s axis unchanged
= 2nd data set max value
Upper limit of 2nd data
set’s axis unchanged
= 2nd data set max value
Lower limit of 2nd data
set’s axis unchanged
= 2nd data set mini value
Lower limit of 2nd data
set’s axis unchanged
= 2nd data set mini value
12
13. ®
Step 3 & 4 of the algorithm (cont.):
Yes
Yes 100
36
1000
840
360
520
680
840
1000
36
52
68
84
100
Q1 Q2
A B
A B
Q1 100 1000
Q2 36 840
Original E-value
0.64
= (100-36)/100
0.16
= (1000-840)/1000
Upper limit
of the axis
100
= A’s Max
1000
= B’s Max
Lower limit
of the axis
36
= A’s Min
360
= B’s Max ×
A’s Min/A’s Max
New E-value N/A
0.64
= (1000-360)/1000
1
2 3
4
1
2
4
2
2
3
4
-16%
-64%
Graphician algorithm
│100│≥│36│
│1000│≥│840│
Adjust lower limit
of 2nd data set’s axis
= 1000 × 36 ÷ 100
=360
Upper limit of 2nd data
set’s axis unchanged
= 1000
i. Here 1st data set is data set A
ii. Here 2nd data set is data set B
13
15. ®
Demonstration of all kinds of situations
15
We define:
a1 = Max value of sequence (A);
an = Min value of sequence (A)
To prove that the patented algorithm can present the true interaction of data with the same elongation ratio under all kinds of
situations, we will demonstrate one example for each situation.
Though the algorithm can be applied to charts with multiple vertical axes, to simplify the demonstration, we assume there
are only two sets of sequences: sequence (A) and sequence (B). Each sequence has only two data, 1st data and 2nd data.
Note: The case of “Max = Min” is not included as there is special treatment as disclosed in the patent.
There are total 16 (=4*4) combinations crossed sequence (A) and (B).
We define:
b1 = Max value of sequence (B);
bn = Min value of sequence (B)
A1: a1 ≥ 0 an ≥ 0 │a1│>│an│
A2: a1 > 0 an < 0 │a1│>│an│
A3: a1 ≥ 0 an < 0 │a1│<│an│
A4: a1 < 0 an < 0 │a1│<│an│
B1: b1 ≥ 0 bn ≥ 0 │b1│>│bn│
B2: b1 > 0 bn < 0 │b1│>│bn│
B3: b1 ≥ 0 bn < 0 │b1│<│bn│
B4: b1 < 0 bn < 0 │b1│<│bn│
For any sequence of data, the range of the Max value and
the Min value can only be one of the 4 situations:
1: Max ≥ 0 Min ≥ 0 │Max│>│Min│
2: Max > 0 Min < 0 │Max│>│Min│
3: Max ≥ 0 Min < 0 │Max│<│Min│
4: Max < 0 Min< 0 │Max│<│Min│