Monthly Archives: June 2023

For this project, you create a column chart to illustrate April–September revenue for Classic Gardens and Landscapes. You also build a pie chart sheet to graph the proportion that each category contributes to total revenue.

File Needed: ClassicGardens-03.xlsx (Available from the Start File link.)

Completed Project File Name: [your name]-ClassicGardens-03.xlsx

SKILLS COVERED IN THIS PROJECT

• Create a chart object.
• Size and position a chart object.
• Edit and format chart elements.
• Edit the source data for a chart.
• Build a pie chart sheet.
• Use texture as fill.
• Add and format data labels in a chart.

This image appears when a project instruction has changed to accommodate an update to Microsoft 365 Apps. If the instruction does not match your version of Office, try using the alternate instruction instead.

1. OPEN THE CLASSICGARDENS-03.XLSX START FILE. IF THE WORKBOOK OPENS IN PROTECTED VIEW, CLICK THE ENABLE EDITING BUTTON SO YOU CAN MODIFY IT. THE FILE WILL BE RENAMED AUTOMATICALLY TO INCLUDE YOUR NAME. CHANGE THE PROJECT FILE NAME IF DIRECTED TO DO SO BY YOUR INSTRUCTOR, AND SAVE IT.

NOTE: If group titles are not visible on your Ribbon in Excel for Mac, click the Excel menu and select Preferences to open the Excel Preferences dialog box. Click the View button and check the Group Titles check box under In Ribbon, Show. Close the Excel Preferences dialog box.

1. CREATE A CLUSTERED COLUMN CHART OBJECT FOR CELLS A4:G9.
2. MOVE THE CHART OBJECT SO THAT ITS TOP-LEFT CORNER IS AT CELL A12. SIZE THE BOTTOM OF THE CHART TO REACH CELL H30.
3. EDIT THE CHART TITLE TO DISPLAY CGL MAJOR SALES REVENUE AND PRESS RETURN. ON THE SECOND LINE, TYPE SECOND AND THIRD QUARTERS.
4. APPLY CHART STYLE 14 TO THE CHART.
5. FORMAT THE FIRST LINE AND THE SECOND LINE OF THE CHART TITLE TO A FONT SIZE OF 20 PT.
6. APPLY A 1 PT BLACK, TEXT 1 (SECOND COLUMN) OUTLINE TO THE CHART AREA.
7. VERIFY THAT THE CHART IS STILL SELECTED. IN THE HIGHLIGHTED RANGE IN THE WORKSHEET, DRAG THE RESIZE POINTER TO REMOVE THE DESIGN CONSULTING DATA SERIES FROM THE CHART (FIGURE 3-71).Figure 3-71 Resize the chart by dragging the resize pointer
8. CREATE A 3-D PIE CHART SHEET FOR CELLS A4:A9 AND CELLS H4:H9. MOVE THE CHART TO ITS OWN SHEET NAMED REVENUE BREAKDOWN. HINT: USE THE MOVE CHART BUTTON [CHART DESIGN TAB, LOCATION GROUP].
9. EDIT THE CHART TITLE TO DISPLAY REVENUE BY CATEGORY. CHANGE THE FONT SIZE TO 32.
10. SELECT THE LEGEND AND CHANGE THE FONT SIZE TO 12.
11. APPLY THE WOVEN MAT TEXTURE FILL (FIRST ROW, FOURTH COLUMN) TO THE PATIO AND FURNITURE SLICE.
12. Select the pie shape and add data labels to display in the center of each slice.
    1. DISPLAY THE FORMAT DATA LABELS TASK PANE, CHOOSE THE ACCOUNTING FORMAT [LABEL OPTIONS BUTTON, NUMBER GROUP], AND SET 0 DECIMAL PLACES.
    2. CHANGE THE DATA LABEL FONT SIZE TO 14 PT AND APPLY BOLD [HOME TAB, FONT GROUP].
13. DESELECT THE CHART.
14. SAVE AND CLOSE THE WORKBOOK (FIGURE 3-72).Figure 3-72 Excel 3-4 completed worksheet and chart
15. UPLOAD AND SAVE YOUR PROJECT FILE.
16. SUBMIT PROJECT FOR GRADING.

Create the pseudocode design that creates and prints an array of sales amounts, determines the sum of the sales amounts, the average of the sales amounts, and find the highest of the sales amounts. The design should be modular as described below. There should be 5 modules in the design. NOTE the use of SIZE and SIZE -1 in the text readings on arrays. Remember that arrays are 0 based. If an array has 5 elements (as below), they are positions 0, 1, 2, 3, and 4
The main module should
• declare the array of 5 Real sales amounts and give each element a literal value as part of the declaration statement
• call a module to print out the values in the array
• call a module calcSum() to calculate and return the sum of the sales amounts and pass the array to it as an argument
• print a line with the sum returned from calcSum()
• call a module findAverage() to calculate and return the avg of the sales amounts and pass the SIZE and sum as arguments
• print a line with the average returned from findAverage()
• call a module named findAndPrintHigh() and pass the array to it as an argument
The calcSum() module should
• accept the array as a parameter
• declare any variables necessary to compute the sum of the array
• return the sum
The findAverage() module should
• accept the sum and the SIZE as parameters
• declare any variables necessary to compute the average
• return the average
The findAndPrintHigh() module should
• accept the array as a parameter
• use the length function of the array when looping through it
• declare any variables necessary
• determine the highest value in the array
• determine the position of the highest value in the array
• print a line with the highest value and the position of that value
 

Add a 2nd array ( a parallel array) to the main module of part A with the names of 5 salesmen. Rewrite the findAndPrintHigh() module passing both arrays to it. Include in the line which prints the highest sales in the array, the name of the salesman who sold that high sales amount

 Exp19_Excel_Ch08_ML2_Reading_Scores

Project Description:

As an elementary school principal, you are concerned about students’ reading comprehension. After conducting a study, you want to calculate some general statistics and then conduct a one-way analysis of variance (ANOVA). Doing so will enable you to compare three sample group means and evaluate the variances within each group compared to the variances among the three groups.

Start   Excel. Download and open the file named Exp19_Excel_Ch08_ML2_HW_ReadingScores.xlsx. Grader has automatically added   your last name to the beginning of the filename.

In the range F2:H2, insert   functions that will calculate the average for each of the groups (Control,   CBT Only, Hybrid).

In the range F3:H3, insert   functions that will calculate the median for each of the groups.

In the range F4:H4, insert   functions that will calculate the most repetitive value for each of the   groups.

In the range F5:H5, insert   functions that will calculate the highest value for each of the groups.

In the range F6:H6, insert   functions that will calculate the lowest value for each of the groups.

In the range F7:H7, insert   functions that will calculate the variance for each of the groups. Use the   function that includes the .S descriptor.

In the range F8:H8, insert   functions that will calculate the standard deviation for each of the groups.   Use the function that includes the .S descriptor.

Format the values in the range   F2:H8 with the Comma Style with three decimal places.

Use the Data Analysis tools to   perform an analysis of variance using the range A2:C25 as the input range and   E11 as the output range.

Format the averages and   variances in the SUMMARY table with the Comma Style with three decimal   places.

Save and close   EXP19_Excel_CH08_ML2_HW _ReadingScores.xlsx. Exit Excel. Submit the file as   directed.

For this Discussion Board, please complete the following:

You have been asked to design a database for CTU, which includes three entities (tables): Students, Faculty, and Courses.

Note: A course refers to a particular course section. For example, the current CS251 you are taking is one course instance (e.g., CS251-01), and another CS251 section is another course instance (e.g., CS251-02). It should also have session information (e.g., 2101A).

Discuss the following:

• Identify 2–3 attributes for each entity, and describe the relationships between the entities.
• To help you with the discussion above, provide a summary using the following template table: (Please see the attached photo for the template table)
• Why is the selection of entities and attributes so important during the design phase of a database?

 I am attaching the code as well. All i need is just an .exe file. If you can send me an exe file i can pay you. 

DECISION TREES for Risk Assessment

One of the great advantages of decision trees is their interpretability. The rules learnt for classification are easy for a person to follow, unlike the opaque “black box” of many other methods, such as neural networks. We demonstrate the utility of this using a German credit data set. You can read a description of this dataset at the UCI site. The task is to predict whether a loan approval is good or bad credit risk based on 20 attributes. We’ve simplified the data set somewhat, particularly making attribute names and values more meaningful.

1. Download the credit_Dataset.arff dataset and load it to Weka.

2. (5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the Visualise tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of age and duration. Do you notice anything unusual? You can click on any data point to display all it’s values.

3. (5 Points) In the previous point you should have found a data point, which seems to be corrupted, as some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.

4. (10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the Preprocess tab click on Choose in the Filter pane. Select filters > unsupervised > instance > RemoveWithValues. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click Ok to set the parameters and Apply to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.

5. (20 Points) On the Classify tab, select the Percentage split test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select classifiers > trees > J48 and click Start. J48 is the Weka implementation of the C4.5 algorithm, which uses the normalized information gain criterion to build a decision tree for classification.

6. (20 Points) After training the classifier, the full decision tree is output for your perusal; you may need to scroll up for this. The tree may also be viewed in graphical form by right-clicking in the Result list and selecting Visualize tree; unfortunately this format is very cluttered for large trees. Such a tree accentuates one of the strengths of decision tree algorithms: they produce classifiers which are understandable to humans. This can be an important asset in real life applications (people are seldom prepared to do what a computer program tells them if there is no clear explanation). Observe the output of the classifier and try to answer the following questions:

o How would you assess the performance of the classifier? Is the Percentage of Correctly Classified Instances a sufficient measure in this case? Why? Hint: check the number of good and bad cases in the test sample, using the confusion matrix. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. For example let us define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2 by 2 contingency table or confusion matrix. One benefit of a confusion matrix is that it is easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).

o Looking at the decision tree itself, are the rules it applies sensible? Are there any branches which appear absurd? At what depth of the tree? What does this suggest?
Hint: Check the rules applied after following the paths: (a) CheckingAccount = <0, Foreign = yes, Duration >11, Job = skilled, OtherDebtors = none, Duration <= 30 and (b) CheckingAccount = <0, Foreign = yes, Duration >11, Job = unskilled.

o How does the decision tree deal with classification in the case where there are zero instances in the training set corresponding to that particular path in the tree (e.g. those leaf nodes that have (0:0))?

7. (20 Points) Now, explore the effect of the confidenceFactor option. You can find this by clicking on the Classifer name (to the right of the Choose button on the Classify tab). On the Classifier options window, click on the More button to find out what the confidence factor controls. Try the values 0.1, 0.2, 0.3 and 0.5. What is the performance of the classifier at each case? Did you expect this given your observations in the previous questions? Why do you think this happens?

8. (20 Points) Suppose that it is worse to classify a customer as good when they are bad, than it is to classify a customer as bad when they are good. Which value would you pick for the confidence factor? Which performance measure would you base your decision on?

9.  (20 Points)Finally we will create a random decision forest and compare the performance of this classifier to that of the decision tree and the decision stump. The random decision forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class’s output by individual trees. Again set the test option Percentage split to 90%. Select classifiers > trees > RandomForest and hit Start. Again, observe the output. How high can you get the performance of the classifier by changing the number of trees (numTrees) parameter? How does the random decision forest compare performance wise to the decision tree and decision stump?

write a 5 pages (not including cover page or refrence page) APA compliant paper discussing the use of  “Cluster Analysis”.  I will expect to see a disucssion of what Cluster Analysisi is good for and what it is not good for,

Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study. 

Requirements:

ABSOLUTELY ZERO PLAGIARISM

Provide a 500 word (or 2 pages double spaced) minimum reflection.

Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.

Share a personal connection that identifies specific knowledge and theories from this course.

Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment. 

I Need help with chapter 3 in the dissertation paper for quantitative methodology.

  Sample Output

Original Polyhedra







------------------------------------------------------







[Sphere] (2, 2, 2)->Radius: 1 Diameter: 2







[Cylinder] (2, 2, 2)->Radius: 1 Height: 2







[Sphere] (8, 8, 8)->Radius: 4 Diameter: 8







[Cylinder] (6, 6, 2)->Radius: 3 Height: 2







[Composite] (14, 14, 14)->3 polyhedra







  [Sphere] (6, 6, 6)->Radius: 3 Diameter: 6







  [Sphere] (10, 10, 10)->Radius: 5 Diameter: 10







  [Sphere] (14, 14, 14)->Radius: 7 Diameter: 14















[Composite] (10, 10, 10)->2 polyhedra







  [Cylinder] (4, 4, 1)->Radius: 2 Height: 1







  [Sphere] (10, 10, 10)->Radius: 5 Diameter: 10















[Sphere] (6, 6, 6)->Radius: 3 Diameter: 6























Scaled Polyhedra (Clones)







------------------------------------------------------







[Sphere] (4, 4, 4)->Radius: 2 Diameter: 4







[Cylinder] (4, 4, 4)->Radius: 2 Height: 4







[Sphere] (16, 16, 16)->Radius: 8 Diameter: 16







[Cylinder] (12, 12, 4)->Radius: 6 Height: 4







[Composite] (28, 28, 28)->3 polyhedra







  [Sphere] (12, 12, 12)->Radius: 6 Diameter: 12







  [Sphere] (20, 20, 20)->Radius: 10 Diameter: 20







  [Sphere] (28, 28, 28)->Radius: 14 Diameter: 28















[Composite] (20, 20, 20)->2 polyhedra







  [Cylinder] (8, 8, 2)->Radius: 4 Height: 2







  [Sphere] (20, 20, 20)->Radius: 10 Diameter: 20















[Sphere] (12, 12, 12)->Radius: 6 Diameter: 12

 your task is 

1. Acquire complete versions of Cylinder.h and Cylinder.cpp from Part 1. You may use my Part 1 solution, or your own solution.

1. Complete add, display, and scale functions for Composite.
2. Complete the Big-3 for Composite
   • Note that the Composite::allPolyhedra attribute, is a vector of pointers (std::vector).
   • I was feeling generous and provided you the Composite::operator= method (I used the copy-and-swap idiom). This leaves you with the Copy Constructor and Destructor (2 of the Big-3).

If you implemented everything in Composite correctly you will see:

PASSED->testDefaultConstructor



PASSED->testAdd



PASSED->testClone



PASSED->testRead



PASSED->testScale



PASSED->testDisplay

 If you see FAILED you must revisit revisit the corresponding function(s).  

Many students forget to scale the bounding boxes. This can be done using the BoundingBox::scale or Polyhedron::scale functions.