This document provides instructions for using the Aptage Risk Burndown tool to plan and manage the risk of a project. It describes setting up the initial project plan workbook with details on epics, stories, and velocity estimates. Running the tool produces a likelihood chart and risk burndown chart. Updating the workbook with sprint results and running the tool again helps evaluate progress. The document demonstrates using scenarios to explore risk under different assumptions like removing content or increasing velocity.
Financial Data Access with SQL, Excel & VBAKAKAROTO ...
This document provides an overview of RExcel, which is an interface between R and Excel that allows users to transfer data between the two programs, run R commands and functions from within Excel, and create Excel applications using R functionality. It discusses RExcel components and modes of operation, installation and configuration, getting started tips, and the RCommander GUI. Basic data transfer and the scratchpad mode for executing R code in Excel are also introduced.
The document outlines specifications for 6 reports. Report 1 displays divisions, counties, and clients in a relational source with groups on division and country. Report 2 shows labor costs by county as a pie chart using a specified cube and measure. Report 3 displays overhead costs by category for Q3 and Q4 2005 from a cube with any increases in red. Report 4 is similar but allows selecting an overhead category and quarter. Report 5 shows overhead by category and quarter from a cube, allowing sorting. Report 6 displays labor costs for an employee's jobs within a date range from a cube, grouping by name and date.
Part B CS8391 Data Structures Part B Questions compiled from R2008 & R2013 to help the students of Affiliated Colleges appearing for Ann University Examination
The document summarizes updates made to the SPPARKS software. SPPARKS is a kinetic Monte Carlo simulation program. Over the summer, the researchers implemented a new curvature diagnostic and completed the Potts gradient application. The curvature diagnostic calculates grain curvature from simulation data. The Potts gradient application models grain growth under temperature or mobility gradients. Testing showed the applications worked as intended. Documentation was also added to the SPPARKS website to describe the new functionality.
This release updates BREEZE Incident Analyst software version 1.3, including updates to modeling algorithms and libraries. Key updates include:
- Increased maximum image size and improved map processing efficiency
- Adopted EPA's worst-case weather conditions as AFTOX model's default
- Updates to dispersion models AFTOX, DEGADIS, and INPUFF to improve accuracy
- Added jet fire at any angle and pool fire time period to fire models
- Replaced explosion model and added ground effects
Doing Analytics Right - Selecting AnalyticsTasktop
This webinar lays out the principles and key concerns for selecting analytics.
It covers:
* The proper purpose of analytics,
* enabling feedback loops to attain your goals such as efficiency and predictability, and
* how to avoid doing more harm than good.
In particular, we will cover: the dimensions of analytics, the key driving principles, analytics maturity, adapting the analytics to your mix of development efforts, and integrating analytics across the levels of the organization.
Homework 5
due Wednesday, March 8, 5pm
Submit zipped .m files on Canvas and printed published file in 182 George St box #15 or #16
You are encouraged to work with other students on this assignment but you are expected to write
and work on your own answers. You don’t need to provide the name of students you worked with.
You can find information about usage and syntax of any built-in Matlab function by typing
help xfunctionnamey
This document summarizes the key slides and findings from a paper on benchmarking processes for glass production. Slide 2 establishes the goal of finding an agile methodology to shorten the glass production cycle. Slide 3 establishes benchmarking metrics derived from previous project proofs. Slides 6-8 detail statistical analysis conducted and the derivation of a build equation. Slide 11 begins the benchmarking comparison of time and motion metrics. Slide 12 derives additional benchmarking metrics. Slide 13 proofs the benchmarked rates. Slide 17 derives an econometric benchmark. The document establishes the benchmarking process undertaken and metrics derived for further analysis.
Financial Data Access with SQL, Excel & VBAKAKAROTO ...
This document provides an overview of RExcel, which is an interface between R and Excel that allows users to transfer data between the two programs, run R commands and functions from within Excel, and create Excel applications using R functionality. It discusses RExcel components and modes of operation, installation and configuration, getting started tips, and the RCommander GUI. Basic data transfer and the scratchpad mode for executing R code in Excel are also introduced.
The document outlines specifications for 6 reports. Report 1 displays divisions, counties, and clients in a relational source with groups on division and country. Report 2 shows labor costs by county as a pie chart using a specified cube and measure. Report 3 displays overhead costs by category for Q3 and Q4 2005 from a cube with any increases in red. Report 4 is similar but allows selecting an overhead category and quarter. Report 5 shows overhead by category and quarter from a cube, allowing sorting. Report 6 displays labor costs for an employee's jobs within a date range from a cube, grouping by name and date.
Part B CS8391 Data Structures Part B Questions compiled from R2008 & R2013 to help the students of Affiliated Colleges appearing for Ann University Examination
The document summarizes updates made to the SPPARKS software. SPPARKS is a kinetic Monte Carlo simulation program. Over the summer, the researchers implemented a new curvature diagnostic and completed the Potts gradient application. The curvature diagnostic calculates grain curvature from simulation data. The Potts gradient application models grain growth under temperature or mobility gradients. Testing showed the applications worked as intended. Documentation was also added to the SPPARKS website to describe the new functionality.
This release updates BREEZE Incident Analyst software version 1.3, including updates to modeling algorithms and libraries. Key updates include:
- Increased maximum image size and improved map processing efficiency
- Adopted EPA's worst-case weather conditions as AFTOX model's default
- Updates to dispersion models AFTOX, DEGADIS, and INPUFF to improve accuracy
- Added jet fire at any angle and pool fire time period to fire models
- Replaced explosion model and added ground effects
Doing Analytics Right - Selecting AnalyticsTasktop
This webinar lays out the principles and key concerns for selecting analytics.
It covers:
* The proper purpose of analytics,
* enabling feedback loops to attain your goals such as efficiency and predictability, and
* how to avoid doing more harm than good.
In particular, we will cover: the dimensions of analytics, the key driving principles, analytics maturity, adapting the analytics to your mix of development efforts, and integrating analytics across the levels of the organization.
Homework 5
due Wednesday, March 8, 5pm
Submit zipped .m files on Canvas and printed published file in 182 George St box #15 or #16
You are encouraged to work with other students on this assignment but you are expected to write
and work on your own answers. You don’t need to provide the name of students you worked with.
You can find information about usage and syntax of any built-in Matlab function by typing
help xfunctionnamey
This document summarizes the key slides and findings from a paper on benchmarking processes for glass production. Slide 2 establishes the goal of finding an agile methodology to shorten the glass production cycle. Slide 3 establishes benchmarking metrics derived from previous project proofs. Slides 6-8 detail statistical analysis conducted and the derivation of a build equation. Slide 11 begins the benchmarking comparison of time and motion metrics. Slide 12 derives additional benchmarking metrics. Slide 13 proofs the benchmarked rates. Slide 17 derives an econometric benchmark. The document establishes the benchmarking process undertaken and metrics derived for further analysis.
Using a stacked bar chart in Excel, you can create a Gantt chart to plan and track projects over time. The steps are: 1) Enter task data including descriptions, start dates, and durations in columns; 2) Create a stacked bar chart from the data and manually set the category and data series labels; 3) Format the chart axes to display tasks in order from top to bottom spanning the earliest to latest dates. Hide the start date data series to resemble a Gantt chart. Adjusting the project schedule will automatically update the chart.
This document provides tips for using a graphing calculator on the AP Calculus exam, including:
1) Graphing the derivative to find relative extrema and zeros, rather than integrating by hand.
2) Setting appropriate window settings when graphing to focus on the relevant domain.
3) Using calculator notation for functions, derivatives, and integrals only when specifically asked, and showing work using proper calculus notation otherwise.
Objectives Assignment 09 Applications of Stacks COS.docxdunhamadell
The document provides instructions for Assignment 09, which involves implementing four functions that use a stack data structure:
1. doParenthesisMatch() checks if a string of parentheses is properly matched and returns a boolean.
2. decodeIDSequence() decodes a string of 'I's and 'D's into a minimum number string without repeated digits.
3. insertItemOnSortedStack() inserts an item into a sorted stack.
4. sortStack() sorts an unsorted stack recursively.
Students are provided header and implementation files for a Stack ADT and tests, and must implement the functions in the given files while following style guidelines. The assignment evaluates correct implementation of the functions and stack usage,
WEB APPENDIX 5ACALCULATING BETA COEFFICIENTS5A-1The .docxmelbruce90096
This document provides instructions for calculating beta coefficients using different calculators and Excel. It explains that beta reflects a stock's expected volatility compared to the market. Historical returns for a stock and the market are used to construct a scatter plot and regression line. The slope of this line is the stock's beta coefficient. The document demonstrates calculating beta using a TI, HP, and Sharp calculator, as well as Excel. It emphasizes that beta is an ex ante measure and past performance does not guarantee future results.
This document provides instructions for using Mathematica to model and 3D print a chess pawn piece. It describes marking up a sketch of a pawn with x and y axes and identifying the points and equations that define each linear and circular section. These functions can then be plotted individually or revolved to create a 3D model that accurately represents the pawn shape and could be 3D printed. Going further, the document mentions using additional function types and a fitting function to create an even more precise 3D model of the pawn.
Labs/Lab5/Lab5_Excel_SH.htmlLab 5: SpreadsheetsLearning Outcomes and IntroductionTask 1: Powers of 2, Powers of 10 Task 2: Importing and Sorting DataTask 3: Graphing DataTask 4: FunctionsSubmission
Learning Outcomes and Introduction
During this process, you will be able to: Demonstrate your ability to layout and format a spreadsheetDemonstrate the use of relative vs. absolute references in spreadsheetsDemonstrate the use of functions in ExcelDemonstrate the use of IF and VLOOKUP in Excel
Task 1:Powers of 2, Powers of 10 (20 marks)Instructions
There is a reasonably close relationship between the powers of two and the powers of ten: 210 is a little more than 103, that is, 1024 is close to 1000. Similarly, 220 is more than 106
and the ratio is 1.049. The approximation is pretty good for a long distance though eventually it breaks down. Your task is to make a spreadsheet that shows
how good the approximation is and find the place where the ratio first becomes greater than 2.
Start your spreadsheet program (such as Excel)
Enter Data:
Put the numbers 0, 1, 2, ...,40 into column A.Put into column B a formula that will compute 2 raised to the power 10 times the value in column A. Put into column C a formula that will compute 10 raised to the power 3 times the value in column A.Put into column D a formula that will compute the ratio of B over C, that is, the ratio of how good or bad the
approximation is.Set the cell format for column D to display exactly two digits after the decimal point.
Prepare a Chart:
Select the correct range to create a chart that shows the ratio changing for the 40 rows.Use the chart wizard ("Insert>Chart>Column" or this icon ) to create a graph that shows the ratio.Move the chart so that is beside your data as shown in the picture below.
Add an appropriate chart title and remove the " legend"
Save Worksheet:
In this lab, you will be using a new sheet for each part, each with its own name. For task1, double-click on the tab that
says Sheet1
Type the name Power2 in its place.Save the spreadsheet in a file called lab5_Firstname_Lastname under the folder COMP152\Lab5
Side Note: the spreadsheet application you are using will add the correct filename extension)
Do this with as little typing and as much use of Excel's extension feature as possible; you can probably do it by typing no more
than two or three rows and then extending them. Your table should look like this when done, except that it will have more rows, more data in the graph,
and a highlighted row towards the end:
Note: In the example below, numbers are displayed as "floating point". You do not have to
format that way, most of us prefer more common looking number formats (comma style?).
No matter what format and number of decimal places you choose to display - the spreadsheet
software is actually using floating point in the background to ensure maximum accur ...
This document provides an overview of events and presentations at the 2010 International Microwave Symposium (IMS) featuring AWR Corporation. It lists the schedule of 6 microapps presentations to take place on May 25th, 2010, covering topics like multi-chip module design challenges, nonlinear co-simulation, system-level component models, and power amplifier design techniques. It also advertises AWR's online design environment for generating customized transistor datasheets.
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).
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.
This document provides instructions for a GIS exercise involving spatial analysis of elevation and precipitation data. The goals are to calculate average watershed elevation and precipitation for subwatersheds of the San Marcos River basin. Slope, aspect, flow direction and hydrologic slope will first be calculated from a sample digital elevation model to demonstrate spatial analysis tools in ArcGIS. A ModelBuilder model is then created to automate these calculations. Finally, the model is applied to real elevation data for the San Marcos basin watersheds to calculate average elevation and interpolate precipitation from station data to estimate watershed precipitation volumes and runoff ratios.
RS Trainings: is a brand and providing quality online and offline trainings for students in world wide. Rs Trainings providing Best DataScience online training in Hyderabad
This document provides instructions for the first assignment in CS106B. It includes 5 problems to help students get familiar with C++ programming. The first problem has students fix bugs in a program that generates a hash code for a name. For the second problem, students write a program to display a histogram of exam scores. The third problem simulates coin flipping until getting 3 consecutive heads. The fourth problem implements Pascal's triangle recursively. The fifth problem converts integers to strings recursively. Students are asked to email their name, hash code, background, and feedback to the instructor.
I am Gozan. M. I am a Programming Exam Helper at liveexamhelper.com. I hold a master' Degree in Programming from the University of Denver, USA. I have been helping students with their exams for the past 9 years. You can hire me to take your exam in Programming. Visit liveexamhelper.com or email info@liveexamhelper.com. You can also call on +1 678 648 4277 for any assistance with the Programming Exam.
In Section 1 on the Data page, complete each column of the spreads.docxsleeperharwell
In Section 1 on the Data page, complete each column of the spreadsheet to arrive at the desired calculations. Use Excel formulas to demonstrate that you can perform the calculations in Excel. Remember, a cell address is the combination of a column and a row. For example, C11 refers to Column C, Row 11 in a spreadsheet.
Reminder: Occasionally in Excel, you will create an unintentional circular reference. This means that within a formula in a cell, you directly or indirectly referred to (back to) the cell. For example, while entering a formula in A3, you enter =A1+A2+A3. This is not correct and will result in an error. Excel allows you to remove or allow these references.
Hint: Another helpful feature in Excel is Paste Special. Mastering this feature allows you to copy and paste all elements of a cell, or just select elements like the formula, the value or the formatting.
"Names" are a way to define cells and ranges in your spreadsheet and can be used in formulas. For review and refresh, see the resources for Create Complex Formulas and Work with Functions.
Ready to Begin?
1. To calculate
hourly rate, you will use the annual hourly rate already computed in Excel, which is 2080. This is the number most often used in annual salary calculations based on full time, 40 hours per week, 52 weeks per year. In E11 (or the first cell in the
Hrly Rate column), create a formula that calculates the hourly rate for each employee by referencing the employee’s salary in Column D, divided by the value of annual hours, 2080. To do this, you will create a simple formula:
=D11/2080. Complete the calculations for the remainder of Column E. If you don’t want to do this cell by cell, you can create a new formula that will let you use that same formula all the way to the end of the column. It would look like this:
=$D$11:$D$382/2080.
2. In Column F, calculate the
number of years worked for each employee by creating a formula that incorporates the date in cell F9 and demonstrates your understanding of relative and absolute cells in Excel. For this, you will need a formula that can compute absolute values to determine years of service. You could do this longhand, but it would take a long time. So, try the
YEARFRAC formula, which computes the number of years (and even rounds). Once you start the formula in Excel, the element will appear to guide you. You need to know the “ending” date (F9) and the hiring date (B11). The formula looks like this:
=YEARFRAC($F$9,B11), and the $ will repeat the formula calculation down the column as before if you grab the edge of the cell and drag it to the bottom of the column.
3. To determine if an employee is
vested or not In Column I, use an
IF statement to flag with a "Yes" any employees who have been employed 10 years or more. Here is how an IF statement works:
=IF(X is greater (or less th.
This document describes Project 4 for CMPSC 201, which involves writing a C++ program to perform linear regression on a set of (x,y) coordinate data to find the line of best fit. The program must read in the data from a file, calculate statistics like the mean, standard deviation, and correlation coefficient, then use those values to compute the slope and y-intercept of the regression line. It also provides specifications on the required functions and expected inputs/outputs of the program.
This document provides an overview of basic operations in Mathematica, including:
1) Mathematica notebooks contain cells for text, commands, and graphics. Functions are defined using capitalized keywords and arguments in brackets.
2) Basic calculations can be performed using standard operators like addition and multiplication. Variables can be assigned values.
3) Plots of functions can be generated and manipulated using sliders. Calculus operations like derivatives and integrals are supported.
4) Data can be imported from files and exported for use in other programs. Basic plotting and analysis of data is demonstrated.
PHYS 221Lab 1 - Acceleration Due to GravityPlease work in g.docxmattjtoni51554
PHYS 221 Lab 1 - Acceleration Due to Gravity
Please work in groups of three. Please submit one lab report per person via Canvas.
In this laboratory we will measure the acceleration due to gravity by studying the motion of a cart accelerating down an inclined plane.
Background
Suppose we start with a level track and then tip it, as shown in Figure 1 below. Let L be the distance between two fixed points on a ramp, selected to be as far apart as possible, on the track. Let h be the difference in the vertical height above the table of these two points.
Figure 1 - Schematic of a cart on an inclined plane. The magnitude of the acceleration of the cart down the ramp can be considered a component of the gravitational acceleration: a = g sinθ
Then we have an incline of angle given by Equation 1:
. (1)
The acceleration of gravity, g, acts vertically downward, so the component of parallel to the incline – which is the acceleration of our cart – is given by Equation 2:
(2)
We see in Equation 2 that a graph of acceleration a as a function of sinθ should be linear with slope g. We will take data to plot such a graph and from its slope determine the value of g.
Setup
Gather the following materials:
· 2 m ramp
· Meter stick
· Lab Stand
· Ramp clamp
· Plastic Box with ULI, AC Adapter, and USB Cable
· Motion Sensor
· Magnetic Bumper
1. Connect the ULI to the computer via the USB cable and connect the AC adapter. Open Logger Pro 3.8.7.
2. Attach the ramp clamp to the lab stand and attach one end of the ramp.
3. Elevate one end of the track slightly using the vertical rod. Choose a value of h so that the angle of inclination stays less than about 8 degrees. (Use Equation 1 to verify).
· You can choose any two points along the track to serve as your L, but they must be the same two points for all your runs!
· Measure h by measuring the difference in the two heights of your two points.
4. Connect a motion sensor to the ULI and mount it on the elevated end of the track. The low end of the track should have a magnetic bumper installed on it (magnets face upward along the track).
Procedure
1. Choose at least five values of height h, to vary over the range 1-8 degrees.
2. Record each value of h chosen, and then obtain a graph of velocity versus time for that value.
3. You have two options for collecting velocity data from the cart:
· Release from the elevated end of the track and let it accelerate to the lower end.
· Push the cart from the lower end of the track up the incline. Record data during its entire motion back to its starting point. This will take slightly more finesse, but the data will be better.
The motion sensor will not record accurate data for a cart closer than 40 cm (the limit of its near range). Do not let the cart collide with the end of the track!
4. Determine the acceleration for the cart by using the Linear Fit tool and highlighting the appropriate region of the velocity graph. Record the .
APPENDIX A A Spreadsheet Model for Efficient Diversificatio.docxaryan532920
APPENDIX A: A Spreadsheet Model for Efficient Diversification
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244 P A R T I I Portfolio Theory and Practice
b. Because the stocks are identical, efficient portfolios are equally weighted. To obtain a standard
deviation of 43%, we need to solve for n:
432 5
602
n
1 .5 3
602(n 2 1)
n
1,849n 5 3,600 1 1,800n 2 1,800
n 5
1,800
49
5 36.73
Thus we need 37 stocks and will come in with volatility slightly under the target.
c. As n gets very large, the variance of an efficient (equally weighted) portfolio diminishes,
leaving only the variance that comes from the covariances among stocks, that is
sP 5 "r 3 s
2 5 ".5 3 602 5 42.43%
Note that with 25 stocks we came within .84% of the systematic risk, that is, the nonsystematic
risk of a portfolio of 25 stocks is only .84%. With 37 stocks the standard deviation is 43%, of
which nonsystematic risk is .57%.
d. If the risk-free is 10%, then the risk premium on any size portfolio is 15 2 10 5 5%. The
standard deviation of a well-diversified portfolio is (practically) 42.43%; hence the slope of
the CAL is
S 5 5/42.43 5 .1178
Several software packages can be used to generate the efficient frontier. We will dem-
onstrate the method using Microsoft Excel. Excel is far from the best program for this
purpose and is limited in the number of assets it can handle, but working through a simple
portfolio optimizer in Excel can illustrate concretely the nature of the calculations used in
more sophisticated “black-box” programs. You will find that even in Excel, the computa-
tion of the efficient frontier is fairly easy.
We apply the Markowitz portfolio optimization program to a practical problem of inter-
national diversification. We take the perspective of a portfolio manager serving U.S. clients,
who wishes to construct for the next year an optimal risky portfolio of large stocks in the U.S
and six developed capital markets (Japan, Germany, U.K., France, Canada, and Australia).
First we describe the input list: forecasts of risk premiums and the covariance matrix. Next,
we describe Excel’s Solver, and finally we show the solution to the manager’s problem.
The Covariance Matrix
To capture recent risk parameters the manager compiles an array of 60 recent monthly
(annualized) rates of return, as well as the monthly T-bill rates for the same period.
The standard deviations of excess returns are shown in Spreadsheet 7A.1 (column C).
They range from 14.93% (U.K. large stocks) to 22.7% (Germany). For perspective on how
these parameters can change over time, standard deviations for the period 1991–2000 are
also shown (column B). In addition, we present the correlation coefficient between large
stocks in the six foreign markets with U.S. large stocks for the same two periods. Here we
see that correlations are higher in the more recent period, co ...
Using a stacked bar chart in Excel, you can create a Gantt chart to plan and track projects over time. The steps are: 1) Enter task data including descriptions, start dates, and durations in columns; 2) Create a stacked bar chart from the data and manually set the category and data series labels; 3) Format the chart axes to display tasks in order from top to bottom spanning the earliest to latest dates. Hide the start date data series to resemble a Gantt chart. Adjusting the project schedule will automatically update the chart.
This document provides tips for using a graphing calculator on the AP Calculus exam, including:
1) Graphing the derivative to find relative extrema and zeros, rather than integrating by hand.
2) Setting appropriate window settings when graphing to focus on the relevant domain.
3) Using calculator notation for functions, derivatives, and integrals only when specifically asked, and showing work using proper calculus notation otherwise.
Objectives Assignment 09 Applications of Stacks COS.docxdunhamadell
The document provides instructions for Assignment 09, which involves implementing four functions that use a stack data structure:
1. doParenthesisMatch() checks if a string of parentheses is properly matched and returns a boolean.
2. decodeIDSequence() decodes a string of 'I's and 'D's into a minimum number string without repeated digits.
3. insertItemOnSortedStack() inserts an item into a sorted stack.
4. sortStack() sorts an unsorted stack recursively.
Students are provided header and implementation files for a Stack ADT and tests, and must implement the functions in the given files while following style guidelines. The assignment evaluates correct implementation of the functions and stack usage,
WEB APPENDIX 5ACALCULATING BETA COEFFICIENTS5A-1The .docxmelbruce90096
This document provides instructions for calculating beta coefficients using different calculators and Excel. It explains that beta reflects a stock's expected volatility compared to the market. Historical returns for a stock and the market are used to construct a scatter plot and regression line. The slope of this line is the stock's beta coefficient. The document demonstrates calculating beta using a TI, HP, and Sharp calculator, as well as Excel. It emphasizes that beta is an ex ante measure and past performance does not guarantee future results.
This document provides instructions for using Mathematica to model and 3D print a chess pawn piece. It describes marking up a sketch of a pawn with x and y axes and identifying the points and equations that define each linear and circular section. These functions can then be plotted individually or revolved to create a 3D model that accurately represents the pawn shape and could be 3D printed. Going further, the document mentions using additional function types and a fitting function to create an even more precise 3D model of the pawn.
Labs/Lab5/Lab5_Excel_SH.htmlLab 5: SpreadsheetsLearning Outcomes and IntroductionTask 1: Powers of 2, Powers of 10 Task 2: Importing and Sorting DataTask 3: Graphing DataTask 4: FunctionsSubmission
Learning Outcomes and Introduction
During this process, you will be able to: Demonstrate your ability to layout and format a spreadsheetDemonstrate the use of relative vs. absolute references in spreadsheetsDemonstrate the use of functions in ExcelDemonstrate the use of IF and VLOOKUP in Excel
Task 1:Powers of 2, Powers of 10 (20 marks)Instructions
There is a reasonably close relationship between the powers of two and the powers of ten: 210 is a little more than 103, that is, 1024 is close to 1000. Similarly, 220 is more than 106
and the ratio is 1.049. The approximation is pretty good for a long distance though eventually it breaks down. Your task is to make a spreadsheet that shows
how good the approximation is and find the place where the ratio first becomes greater than 2.
Start your spreadsheet program (such as Excel)
Enter Data:
Put the numbers 0, 1, 2, ...,40 into column A.Put into column B a formula that will compute 2 raised to the power 10 times the value in column A. Put into column C a formula that will compute 10 raised to the power 3 times the value in column A.Put into column D a formula that will compute the ratio of B over C, that is, the ratio of how good or bad the
approximation is.Set the cell format for column D to display exactly two digits after the decimal point.
Prepare a Chart:
Select the correct range to create a chart that shows the ratio changing for the 40 rows.Use the chart wizard ("Insert>Chart>Column" or this icon ) to create a graph that shows the ratio.Move the chart so that is beside your data as shown in the picture below.
Add an appropriate chart title and remove the " legend"
Save Worksheet:
In this lab, you will be using a new sheet for each part, each with its own name. For task1, double-click on the tab that
says Sheet1
Type the name Power2 in its place.Save the spreadsheet in a file called lab5_Firstname_Lastname under the folder COMP152\Lab5
Side Note: the spreadsheet application you are using will add the correct filename extension)
Do this with as little typing and as much use of Excel's extension feature as possible; you can probably do it by typing no more
than two or three rows and then extending them. Your table should look like this when done, except that it will have more rows, more data in the graph,
and a highlighted row towards the end:
Note: In the example below, numbers are displayed as "floating point". You do not have to
format that way, most of us prefer more common looking number formats (comma style?).
No matter what format and number of decimal places you choose to display - the spreadsheet
software is actually using floating point in the background to ensure maximum accur ...
This document provides an overview of events and presentations at the 2010 International Microwave Symposium (IMS) featuring AWR Corporation. It lists the schedule of 6 microapps presentations to take place on May 25th, 2010, covering topics like multi-chip module design challenges, nonlinear co-simulation, system-level component models, and power amplifier design techniques. It also advertises AWR's online design environment for generating customized transistor datasheets.
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).
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.
This document provides instructions for a GIS exercise involving spatial analysis of elevation and precipitation data. The goals are to calculate average watershed elevation and precipitation for subwatersheds of the San Marcos River basin. Slope, aspect, flow direction and hydrologic slope will first be calculated from a sample digital elevation model to demonstrate spatial analysis tools in ArcGIS. A ModelBuilder model is then created to automate these calculations. Finally, the model is applied to real elevation data for the San Marcos basin watersheds to calculate average elevation and interpolate precipitation from station data to estimate watershed precipitation volumes and runoff ratios.
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This document provides instructions for the first assignment in CS106B. It includes 5 problems to help students get familiar with C++ programming. The first problem has students fix bugs in a program that generates a hash code for a name. For the second problem, students write a program to display a histogram of exam scores. The third problem simulates coin flipping until getting 3 consecutive heads. The fourth problem implements Pascal's triangle recursively. The fifth problem converts integers to strings recursively. Students are asked to email their name, hash code, background, and feedback to the instructor.
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In Section 1 on the Data page, complete each column of the spreads.docxsleeperharwell
In Section 1 on the Data page, complete each column of the spreadsheet to arrive at the desired calculations. Use Excel formulas to demonstrate that you can perform the calculations in Excel. Remember, a cell address is the combination of a column and a row. For example, C11 refers to Column C, Row 11 in a spreadsheet.
Reminder: Occasionally in Excel, you will create an unintentional circular reference. This means that within a formula in a cell, you directly or indirectly referred to (back to) the cell. For example, while entering a formula in A3, you enter =A1+A2+A3. This is not correct and will result in an error. Excel allows you to remove or allow these references.
Hint: Another helpful feature in Excel is Paste Special. Mastering this feature allows you to copy and paste all elements of a cell, or just select elements like the formula, the value or the formatting.
"Names" are a way to define cells and ranges in your spreadsheet and can be used in formulas. For review and refresh, see the resources for Create Complex Formulas and Work with Functions.
Ready to Begin?
1. To calculate
hourly rate, you will use the annual hourly rate already computed in Excel, which is 2080. This is the number most often used in annual salary calculations based on full time, 40 hours per week, 52 weeks per year. In E11 (or the first cell in the
Hrly Rate column), create a formula that calculates the hourly rate for each employee by referencing the employee’s salary in Column D, divided by the value of annual hours, 2080. To do this, you will create a simple formula:
=D11/2080. Complete the calculations for the remainder of Column E. If you don’t want to do this cell by cell, you can create a new formula that will let you use that same formula all the way to the end of the column. It would look like this:
=$D$11:$D$382/2080.
2. In Column F, calculate the
number of years worked for each employee by creating a formula that incorporates the date in cell F9 and demonstrates your understanding of relative and absolute cells in Excel. For this, you will need a formula that can compute absolute values to determine years of service. You could do this longhand, but it would take a long time. So, try the
YEARFRAC formula, which computes the number of years (and even rounds). Once you start the formula in Excel, the element will appear to guide you. You need to know the “ending” date (F9) and the hiring date (B11). The formula looks like this:
=YEARFRAC($F$9,B11), and the $ will repeat the formula calculation down the column as before if you grab the edge of the cell and drag it to the bottom of the column.
3. To determine if an employee is
vested or not In Column I, use an
IF statement to flag with a "Yes" any employees who have been employed 10 years or more. Here is how an IF statement works:
=IF(X is greater (or less th.
This document describes Project 4 for CMPSC 201, which involves writing a C++ program to perform linear regression on a set of (x,y) coordinate data to find the line of best fit. The program must read in the data from a file, calculate statistics like the mean, standard deviation, and correlation coefficient, then use those values to compute the slope and y-intercept of the regression line. It also provides specifications on the required functions and expected inputs/outputs of the program.
This document provides an overview of basic operations in Mathematica, including:
1) Mathematica notebooks contain cells for text, commands, and graphics. Functions are defined using capitalized keywords and arguments in brackets.
2) Basic calculations can be performed using standard operators like addition and multiplication. Variables can be assigned values.
3) Plots of functions can be generated and manipulated using sliders. Calculus operations like derivatives and integrals are supported.
4) Data can be imported from files and exported for use in other programs. Basic plotting and analysis of data is demonstrated.
PHYS 221Lab 1 - Acceleration Due to GravityPlease work in g.docxmattjtoni51554
PHYS 221 Lab 1 - Acceleration Due to Gravity
Please work in groups of three. Please submit one lab report per person via Canvas.
In this laboratory we will measure the acceleration due to gravity by studying the motion of a cart accelerating down an inclined plane.
Background
Suppose we start with a level track and then tip it, as shown in Figure 1 below. Let L be the distance between two fixed points on a ramp, selected to be as far apart as possible, on the track. Let h be the difference in the vertical height above the table of these two points.
Figure 1 - Schematic of a cart on an inclined plane. The magnitude of the acceleration of the cart down the ramp can be considered a component of the gravitational acceleration: a = g sinθ
Then we have an incline of angle given by Equation 1:
. (1)
The acceleration of gravity, g, acts vertically downward, so the component of parallel to the incline – which is the acceleration of our cart – is given by Equation 2:
(2)
We see in Equation 2 that a graph of acceleration a as a function of sinθ should be linear with slope g. We will take data to plot such a graph and from its slope determine the value of g.
Setup
Gather the following materials:
· 2 m ramp
· Meter stick
· Lab Stand
· Ramp clamp
· Plastic Box with ULI, AC Adapter, and USB Cable
· Motion Sensor
· Magnetic Bumper
1. Connect the ULI to the computer via the USB cable and connect the AC adapter. Open Logger Pro 3.8.7.
2. Attach the ramp clamp to the lab stand and attach one end of the ramp.
3. Elevate one end of the track slightly using the vertical rod. Choose a value of h so that the angle of inclination stays less than about 8 degrees. (Use Equation 1 to verify).
· You can choose any two points along the track to serve as your L, but they must be the same two points for all your runs!
· Measure h by measuring the difference in the two heights of your two points.
4. Connect a motion sensor to the ULI and mount it on the elevated end of the track. The low end of the track should have a magnetic bumper installed on it (magnets face upward along the track).
Procedure
1. Choose at least five values of height h, to vary over the range 1-8 degrees.
2. Record each value of h chosen, and then obtain a graph of velocity versus time for that value.
3. You have two options for collecting velocity data from the cart:
· Release from the elevated end of the track and let it accelerate to the lower end.
· Push the cart from the lower end of the track up the incline. Record data during its entire motion back to its starting point. This will take slightly more finesse, but the data will be better.
The motion sensor will not record accurate data for a cart closer than 40 cm (the limit of its near range). Do not let the cart collide with the end of the track!
4. Determine the acceleration for the cart by using the Linear Fit tool and highlighting the appropriate region of the velocity graph. Record the .
APPENDIX A A Spreadsheet Model for Efficient Diversificatio.docxaryan532920
APPENDIX A: A Spreadsheet Model for Efficient Diversification
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244 P A R T I I Portfolio Theory and Practice
b. Because the stocks are identical, efficient portfolios are equally weighted. To obtain a standard
deviation of 43%, we need to solve for n:
432 5
602
n
1 .5 3
602(n 2 1)
n
1,849n 5 3,600 1 1,800n 2 1,800
n 5
1,800
49
5 36.73
Thus we need 37 stocks and will come in with volatility slightly under the target.
c. As n gets very large, the variance of an efficient (equally weighted) portfolio diminishes,
leaving only the variance that comes from the covariances among stocks, that is
sP 5 "r 3 s
2 5 ".5 3 602 5 42.43%
Note that with 25 stocks we came within .84% of the systematic risk, that is, the nonsystematic
risk of a portfolio of 25 stocks is only .84%. With 37 stocks the standard deviation is 43%, of
which nonsystematic risk is .57%.
d. If the risk-free is 10%, then the risk premium on any size portfolio is 15 2 10 5 5%. The
standard deviation of a well-diversified portfolio is (practically) 42.43%; hence the slope of
the CAL is
S 5 5/42.43 5 .1178
Several software packages can be used to generate the efficient frontier. We will dem-
onstrate the method using Microsoft Excel. Excel is far from the best program for this
purpose and is limited in the number of assets it can handle, but working through a simple
portfolio optimizer in Excel can illustrate concretely the nature of the calculations used in
more sophisticated “black-box” programs. You will find that even in Excel, the computa-
tion of the efficient frontier is fairly easy.
We apply the Markowitz portfolio optimization program to a practical problem of inter-
national diversification. We take the perspective of a portfolio manager serving U.S. clients,
who wishes to construct for the next year an optimal risky portfolio of large stocks in the U.S
and six developed capital markets (Japan, Germany, U.K., France, Canada, and Australia).
First we describe the input list: forecasts of risk premiums and the covariance matrix. Next,
we describe Excel’s Solver, and finally we show the solution to the manager’s problem.
The Covariance Matrix
To capture recent risk parameters the manager compiles an array of 60 recent monthly
(annualized) rates of return, as well as the monthly T-bill rates for the same period.
The standard deviations of excess returns are shown in Spreadsheet 7A.1 (column C).
They range from 14.93% (U.K. large stocks) to 22.7% (Germany). For perspective on how
these parameters can change over time, standard deviations for the period 1991–2000 are
also shown (column B). In addition, we present the correlation coefficient between large
stocks in the six foreign markets with U.S. large stocks for the same two periods. Here we
see that correlations are higher in the more recent period, co ...
APPENDIX A A Spreadsheet Model for Efficient Diversificatio.docx
RBD-Demo
1. Risk Burndown Demo
Background
You have just started a new project with a new team. This is an experienced team, and you expect their
velocity will be somewhere about 50 story points per sprint but maybe as low as 40, or as high as 60.
This team is about to embark of on a 10-sprint plan to deliver five epics, E1, E2 …, E5. Each sprint is 2
weeks. Let’s call the project Rel.
This team is reasonably aggressive and willing to accept an initial 30% uncertainty of delivery.
THE APTAGE Workbook
To start using Aptage, you need to create the input workbook to upload. Start with the Aptage RBD
template, RBBTemplate.xlsx. In this workbook, you will find two sheets: Plan and Items.
The Plan sheet is shown in Figure 1.
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Figure 1. The Plan sheet.
Fill in the project’s values, and then save the workbook with a name such as Rel1.xlxs.
Figure 2 Release Plan Data.
Note: we will be using Estimated Velocity, represented by E. That data is entered in the Items sheet.
Over time, we will replace the estimate with learned velocity, which will be explained more fully below.
The risk burndown tool evaluates the risk of delivery at the beginning of each sprint.
We start planning at sprint 1.
Now, save the workbook as Rel1.xlsx (or whatever name you like, but keep the ‘1’).
We now move to the Item sheet.
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Figure 3 The Item sheet.
Each row represents the data for either a STORY or an EPIC.
On this sheet:
Column A is the Item ID. This can be any string.
Columns B, C, D, together, is a sizing triplet. Values in B, C, or D can be any number, so that D
C B.
Column E contains the name of the Iteration in which the item was completed. This is used for
ongoing velocity estimation.
Column F contains an ‘x’ for those items still in plan and not yet done.
Column G contains a priority field. These can be an integer with 1 being the highest priority.
Figure 4 shows an example of a populated Item sheet.
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Figure 4 The populated Item sheet.
Item G is the initial velocity ‘guess’ The E in column E - the velocity estimation data - links to the E in
row 6 of the Plan sheet.
Note first there is a mixture of stories and epics. E1 is decomposed into stories E1S1, …, E1S5. The
remaining epics, E2 through E5, have not yet been decomposed and, so, have large sizings. Even
though they are in the initial plan, the E1 stories have appropriately smaller sizings. Aptage Risk
Burndown will use the stories P1 through P15 to estimate the team velocity. Note that the epics and
the epic stories are all included in the plan.
Now save the workbook in ../RBD Demo/input folder.
Initial Run
Now, open a terminal and cd to the ../RBD Demo/code directory. Enter ‘python RBD.py’. The code will
ask for the name of the worksheet. Just enter the name without the .xlsx extension. The code returns
a message validating that it has found and opened the workbook - and that the data has been
validated. This is followed by a suggested set of stories for the next (in this case, first) sprint. These
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stories are chosen so that if they are completed, the needed velocity will be maintained and
prioritized by uncertainty reduction and priority.
Figure 5 Running RBD.
A window showing the probability over time of completing the effort will also appear.
Figure 6 The Aptage likelihood chart.
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Now, close the graphics window to complete the process. Never fear, the same diagram is saved to
the output folder as Rel1.png
In this case, the x-axis is weeks, and the y axis is the probability of completing the release in that
number of weeks given the size and velocity estimate. In this diagram, the area of the red part of the
diagram is the probability of missing the hoped-for date. Note that this plan does not meet the initial
criteria of 30% uncertainty. So, some content management is in order. Go back into the Item sheet
and deselect Epic E5. Then, re-run the tool.
Figure 7 The Rel-2 item sheet, with Epic E5 deselected.
Running the tool again gives us the likelihood chart in Figure 8.
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Figure 8 The Rel1-2 plan likelihood chart.
Since we are running the tool prior to starting the project, the burndown chart does not appear.
Next Run
Now, suppose the team completes the first iteration with all suggested stories and it wants to see if it
is on track. It needs to build a new input workbook, based on the first book.
To do this, open the workbook. Note there is a hidden sheet in the book that contain data for future
plotting; so, do not start from scratch. We will save the workbook as demo2.
In prepping for the next sprint, Epics E3 and E5 have been decomposed into stories. Each of these
has been given sizing triplets. There is no need for the story sizes to add up in any way to the initial
epic sizing, as the team has gained more insight during the decomposition.
To reflect all this:
Add the new stories with the sizings, and mark them in plan.
Remove the in-plan marker from the E3 and E5 items, now that its stories are in plan.
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Add the S1 marker to column E for the completed stories.
Now the item sheet looks like:
Figure 9 Item sheet prior to sprint 2.
We also need to update the Plan sheet. All that is needed is to replace the E with ‘S1’ to the list in cell
B6 to reflect the team history, and change B2 to 2. See Figure 10.
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Figure 10 The Updated Plan Sheet.
Save the spreadsheet as Rel2.xlsx, and run the code again. This time, enter the updated spreadsheet
into the tool. I will enter ‘Rel2’.
The tool returns suggested stories for the next sprint (E2S3, E2S6, E3S3, E2S1) and this likelihood
diagram:
Figure 11 The likelihood diagram after one sprint.
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Note the risk of missing the delivery has been slightly reduced to 27%. Closing this window, we also
get the first risk burndown chart (Figure 12).
Figure 12 The risk burndown chart after the first iteration.
In a well-run Agile exercise, the uncertainty of delivery will decrease over the project lifecycle. The
risk burndown chart shows if this is in fact the case for the current effort.
Now suppose the next sprint does not go well. A couple of team members were not available, and the
team only completed E2S3. The updated Item sheet is found in Figure 13. After updated, the Item
sheet looks like this:
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Figure 14 The plan sheet for Rel3.
Note that we added S2 to the estimation data with a ‘,’ separator.
Again, we save the workbook - this time as ‘Rel3.xlsx’ and run the tool entering this workbook.
Figure 15 Rel3 Likelihood chart.
Note that even though one story was completed, the uncertainty of delivery has not decreased,
putting the overall project at risk. This is more evident in the risk burndown chart below.
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Figure 16 . The risk burndown after Sprint 2 moves into the red.
Given the sprint history, we see the project is now slightly in the red. The tool suggests the team
deliver stories E2S6, E3S3, E3S6 in the next sprint. However, they only deliver E2S6 and E3S3.
The sheets are updated and the workbook is saved as ‘Rel4.xlsx’. The tool now returns the following
charts:
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Figure 17 The Rel4 Likelihood chart.
Figure 18 The Risk Burndown after iteration 3.
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Risk Management and Scenarios
This project is clearly in trouble and needs to get on track and needs something in the project
execution to change:
You can use the Aptage Risk Burndown tool to explore some alternatives. You can create a variety
scenarios to project what the residual risk would be under different actions. For example, here are
three scenarios.
1. Removing epic 4 from the plan and completing the three stories;
2. Assuming leaving epic 4 in the plan, decomposing it and completing all the suggested stories;
3. Continuing at the same pace;
To test each of these, build three versions of the workbook: Rel5S1.xlsx, Rel5S2.xlsx, and Rel5S3.xlsx
modeling each of them. Also, to reflect the team history, we will base the velocity estimation on the
last three sprints. This is captured in cell B of the Plan sheet; the ‘S1’ is removed from the list (see
Figure 19).
Figure 19. The Plan sheet for the three scenarios.
The risk burndown chart for scenario 1 is Figure 20.
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Figure 20 The Risk burndown chart for scenario 1.
Note the risk of missing the delivery is under control in this scenario.
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Figure 21 The Risk Burndown chart for scenario 2.
In this case, the risk is still too high but leveling off. Proceeding with this plan means committing to
higher velocity to get the project on track.
As you can see in Figure 22, proceeding at the same pace is not a viable option.
Figure 22 The Risk Burndown for scenario 3.
Scenario 3 is clearly not an option. Choosing between the other scenarios depends on your
organization culture and priorities. If your organization is counting on the release and epic 4 can be
postponed, scenario 1 is the safe choice. However, if epic 4 is critical, then the team needs to be
allowed to be focused and not miss the further sprints.
Let’s suppose the team does go forward with scenario 2 but gets four more stories done: E3S6, E3S4,
and completes epic 2 (stories E2S4, E2S5).
The item sheet for Rel6 looks like this:
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Figure 23 The Rel6 item sheet.
Then the risk burndown chart now looks like:
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Figure 24 The Rel6 Risk Burndown chart.
Note that the project is on track but not home free. With the right focus and monitoring, this project
can deliver on-time.
Discussion
With the Aptage Risk Burndown tool, the team can plan and steer the program to success:
Plan a release according the estimated team velocity (based on previous history or team’s
best belief) and risk appetite.
Continually monitor and adjust the content and effort to account for the program
performance.
o Get early indication of possible failure.
o Consider and model different recovery approaches.
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Appendix: Aptage RBD Tool Environment
The Risk Burndown Tool
To run the Aptage RBD tool, you need to have access to a machine that has the following installed.
Python 3.4 or higher;
The numpy, scipy, matplotlib, and openpyxl modules;
The Aptage Dropbox demo directory, ‘RBD Demo’ with these three folders:
1. Code - the Aptage code files including RBD.py, the main executable;
2. Input – holding the MS Excel® worksheets;
3. Output – containing the output graphics generated by the Risk Burndown tool;
Also, to run demos, clear out the RBD_input folder copy the files from the canned directory to the
folder.