Cobolbasics
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The document discusses COBOL data structures and how to define group items, elementary items, and move data between them. It also covers accepting and displaying data using COBOL verbs like MOVE, DISPLAY, and ACCEPT.
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Kudler has plenty of room to increase sales while controlling costs. The first place they should start is renegotiating the cost associated with rent in the locations where they already have stores. Since they already occupy the stores and show good payment history, they have leverage in these negotiations. They are currently paying $63,000, if they can reduce that rent by 5%, they would be saving $3,150 which is more than enough to cover their bad debts. They can also drive cost out of the business by paying back debt that has higher interest rates. Currently, Kudler is paying over $63,000 in interest while showing net income of $676,795. Some of that income should be leveraged to reduce the cost of interest. Kudler can utilize these cost controls without compromising the increase in sales.
TABLE 6
Sarah Stamm
Andrew Smith
George Denillo
Kevin Henry
Jerry Hill
7-3 What does this algorithm do?Adding 2 to the current number 10 times.
Assumptions: The user provides a positive integer.
Input:A number from user.
Output:Some result.
1. Input number from user.
2. Set count to 1.
3. While (count <= 10)
a. Set current # to # + 2
b. Set count to count + 1
4. Display current # to the screen
7-13
Create an algorithm to describe new balance after 10 transactions at new restaurant.
Assumptions: Balance never goes negative.
Step 1: Get initial balance
Step 2: Ask if there are more transactions. If not, go to step 6.
Step 3: Get transaction.
Step 4: Set balance to balance + transaction.
Step 5: Go to step 2.
Step 6: Display overall balance.
9-5
What would this SQL statement output?
Select * FROM “LOCATION”
Output: All the fields in the location table would be selected.
9-8
Which customer(s) live in Georgia (GA)? List by name.
Table name is customer, check c_state attribute to locate matching state, returns c_id # 5 (Sheila Lewis)
TABLE #5
(Emma Towe, Dieter Archer, Brian House, Davis Ruppert, Ross Bowman, Yahya Hefnawi)
MODULE 8
(8-4) Q1: What will this for loop output?
Var counter;
For(counter=7; counter >=7; counter--);
{
Document.write(“current count: “+ counter+” < br/>”);
{
Document.write(“loop stopped”);
(8-9) Q2: Create a javascript program that will convert Celsius to Fahrenheit or vice versa.
MODULE 9
(9-5) Q3: List the building code and the capacity in the location table using SQL developer.
(9-8) Q4: Create a list of all the students that live in Clermont or Carrabelle from SQL developer.
Display the S_first, S_last, and S_city
A1: Starts at 7 then decrements till -7.
A2:
var degrees;
var label;
degrees = prompt ("Please enter a temperature in degrees");
label = prompt ("Please enter 'C' for Celsius or 'F' for Fahrenheit");
if (label=="C") {
answer = ((degrees * 9/5) + 32);
document.write(degrees +" degrees C is equal to " + answer + " F");
}
else{
answer = ((degrees - 32) * 5/9);
document.write(degrees +" degrees F is equal to " + answer + " C");
}
A3: SELECT bldg_code, capacity FROM location
A4: ...
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1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docx
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
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This document discusses chapter 34 of Liang's Introduction to Java Programming textbook. The chapter covers Java database programming and relational database concepts. It defines database systems and components, explains the relational data model including tables, keys, and constraints. It also provides examples of SQL commands and JDBC for interacting with databases in Java applications.
databases3b
This document discusses database concepts like fields, attributes, data types, primary keys and validation rules. It provides examples of designing databases to store student information and sales data. Entity-relationship diagrams are explained to represent relationships between entities like one-to-one, one-to-many and many-to-many. Examples are given of entity-relationship diagrams for a hospital database and police database.
Database Management System Review
The document discusses database management systems and database concepts. It defines what a database is and reasons for using databases. It covers different data models including the relational, object-oriented relational, and semi-structured models. It also discusses defining database schemas in SQL, various relational operations like selection and projection, and different types of joins. Finally, it covers topics related to database normalization including anomalies that can occur without normalization and the first normal form.
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Cover PageComplete and copy the following to Word for your cover p.docx
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Kudler has plenty of room to increase sales while controlling cost.docx
Kudler has plenty of room to increase sales while controlling cost.docx
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1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docx
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