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MLlectureMethod.ppt

1. Cross-validation is commonly used to evaluate machine learning algorithms and estimate their performance on new data. It involves partitioning the dataset into training and test sets and measuring the accuracy on the held-out test sets.
2. Tuning sets are often used to select hyperparameters like the number of hidden units. Performance on the tuning set is used to estimate future performance on new examples.
3. Statistical tests like paired t-tests are used to determine if differences in performance between algorithms on test sets are statistically significant.

Page 1 of 18Part A Multiple Choice (1–11)______1. Using.docx

Page 1 of 18
Part A: Multiple Choice (1–11)
______1. Using the “eyeball” method, the regression line = 2+2x has been fitted to the data points (x = 2, y = 1), (x = 3, y = 8), and (x = 4, y = 7). The sum of the squared residuals will be
a. 7 b. 19 c. 34 d. 8
______2. A computer statistical package has included the following quantities in its output: SST = 50, SSR = 35, and SSE = 15. How much of the variation in y is explained by the regression equation?
a. 49% b. 70% c. 35% d. 15%
______3. In testing the significance of b, the null hypothesis is generally that
a. β = b b. β 0 c. β = 0 d. β = r
______4. Testing whether the slope of the population regression line could be zero is equivalent to testing whether the population _____________ could be zero.
a. standard error of estimate c. y-intercept
b. prediction interval d. coefficient of correlation
______5. A multiple regression equation includes 4 independent variables, and the coefficient of multiple determination is 0.64. How much of the variation in y is explained by the regression equation?
a. 80% b. 16% c. 32% d. 64%
______6. A multiple regression analysis results in the following values for the sum-of-squares terms: SST = 50.0, SSR = 35.0, and SSE = 15.0. The coefficient of multiple determination will be
a. = 0.35 b. = 0.30 c. = 0.70 d. = 0.50
______7. In testing the overall significance of a multiple regression equation in which there are three independent variables, the null hypothesis is
a. :
b. :
c. :
d. :
______8. In a multiple regression analysis involving 25 data points and 4 independent variables, the sum-of-squares terms are calculated as SSR = 120, SSE = 80, and SST = 200. In testing the overall significance of the regression equation, the calculated value of the test statistic will be
a. F = 1.5 c. F = 5.5
b. F = 2.5 d. F = 7.5
______9. For a set of 15 data points, a computer statistical package has found the multiple regression equation to be = -23 + 20+ 5 + 25 and has listed the t-ratio for testing the significance of each partial regression coefficient. Using the 0.05 level in testing whether = 20 differs significantly from zero, the critical t values will be
a. t = -1.960 and t= +1.960
b. t = -2.132 and t = +2.132
c. t = -2.201 and t = +2.201
d. t = -1.796 and t = +1.796
______10. Computer analyses typically provide a p-Value for each partial regression coefficient. In the case of , this is the probability that
a. = 0
b. =
c. the absolute value of could be this large if = 0
d. the absolute value of could be this large if 1
______11. In the multiple regression equation, = 20,000 + 0.05+ 4500 , is the estimated household income, is the amount of life insurance held by the head of the household, and is a dummy variable ( = 1 if the family owns mutual funds, 0 if it doesn’t). The interpretation of = 4500 is that
a. owing mutual funds increases the estimated income by $4500
b. the average value of a mut.

Chapter10 Revised

The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean and proportion. It also demonstrates identifying the null and alternative hypotheses and interpreting the results of hypothesis tests, including calculating p-values.

Chapter10 Revised

The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean using a sample mean and standard deviation. It also demonstrates how to identify the null and alternative hypotheses, determine if a test is right-tailed, left-tailed, or two-tailed, and calculate p-values to conclude whether to reject or fail to reject the null hypothesis based on a significance level of 0.05. Examples include testing claims about population proportions and means.

Chapter10 Revised

The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean using a sample mean and standard deviation. It also demonstrates how to identify the null and alternative hypotheses, determine if a test is right-tailed, left-tailed, or two-tailed, and calculate p-values to conclude whether to reject or fail to reject the null hypothesis based on a significance level of 0.05. Examples include testing claims about population proportions and means.

working with python

Linear regression and logistic regression are two machine learning algorithms that can be implemented in Python. Linear regression is used for predictive analysis to find relationships between variables, while logistic regression is used for classification with binary dependent variables. Support vector machines (SVMs) are another algorithm that finds the optimal hyperplane to separate data points and maximize the margin between the classes. Key terms discussed include cost functions, gradient descent, confusion matrices, and ROC curves. Code examples are provided to demonstrate implementing linear regression, logistic regression, and SVM in Python using scikit-learn.

Ali, Redescending M-estimator

Detail notes on Autocorrelation, including all mathematical work.
Muhammad Ali
Lecturer in Statistics
Higher Education, Department, KPK, Pakistan.

Simple lin regress_inference

This document provides an overview of simple linear regression analysis. It discusses estimating regression coefficients using the least squares method, interpreting the regression equation, assessing model fit using measures like the standard error of the estimate and coefficient of determination, testing hypotheses about regression coefficients, and using the regression model to make predictions.

Big Data Analysis

Big Data analysis involves building predictive models from high-dimensional data using techniques like variable selection, cross-validation, and regularization to avoid overfitting. The document discusses an example analyzing web browsing data to predict online spending, highlighting challenges with large numbers of variables. It also covers summarizing high-dimensional data through dimension reduction and model building for prediction versus causal inference.

Assignment #9First, we recall some definitions that will be help.docx

Assignment #9
First, we recall some definitions that will be helpful in answering questions 1-3
A population parameter is a single value that describes a population characteristic (such as center, spread, location etc.)
EXAMPLES:
· The proportion
p
of adults in the United States who worry about money
· The mean lifetime
m
of a certain brand of computer hard disks
· The lower quartile
1
q
of a population of incomes
· The standard deviation
s
of the nicotine content per cigarette produced by a certain manufacture.
In real life, population parameters are usually unknown. An important objective of statistical inference is to use information obtained from random sample or samples (depending on the design of the study) to estimate parameters and to test claims made about them.
A statistic is a number computed form the sample data only. The resulting sample value must be independent of the population parameters.
Statistics are used as numerical estimates of population parameters.
Example: A random sample of 1500 national adults shows that 33% of Americans worry about money. The margin of error is +/- 3 percentage points.
Statistics have variation. Different random samples of size
n
from the same population will usually yield different values of the same statistic. This is called sampling variability.
The sampling distribution of a statistic is the distribution of the values taken by the statistic over all possible random samples of the same size from a given population.
What do we look for in a sampling distrinution?
Bias: A statistic is unbiased if its sampling distribution has a mean that is equal to the true value of the parameter being estimated by that statistic.
Variability: How much variation is there in the sampling distribution?
The goal of this assignment is to simulate the sampling distribution of some statistics.
Question 1:
An urn contains 50 beads. The beads are identical in shape and have one of two colors: blue and orange. We would like to estimate the proportion
p
of blue beads. We select without replacement a sample of 10 beads. The relevant statistics is the sample proportion
p
ˆ
of blue beads (i.e., the number of blue beads in the sample divided by 10.
For the purpose of the simulation exercise, we will assume that the box contains exactly 15 blue beads or, equivalently, the proportion of blue beads is
30
.
0
=
p
.
i) Select 100 samples of size 10 from the box.
ii) Compute the sample proportion
p
ˆ
of blue beads for each of the 100 samples found in (i).
iii) Make a histogram of the values of
p
ˆ
found in (ii) (that is the approximate sampling distribution of
p
ˆ
.)
iv) Find the summary statistics of the 100 values of
p
ˆ
.
v) Base yourself on the histogram and the summary statistics to describe the approximate sampling distribution of
p
ˆ
.
vi) Is
p
ˆ
an unbiased estimator of
p
? Hint: Evaluate the difference between
30
.
0
=
p
and the mean value. ...

1. Outline the differences between Hoarding power and Encouraging..docx

1. Outline the differences between Hoarding power and Encouraging.
2. Explain about the power of Congruency in Leadership.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrCopy Employee Data set to this page.822.10.962233290915.81FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 1522.60.984233280814.91FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3522.60.984232390415.30FA37230.999232295216.20FAThe column labels in the table mean:1023.11.003233080714.71FAID – Employee sample number Salary – Salary in thousands 2323.11.004233665613.30FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)1123.31.01223411001914.81FASERvice – Years of serviceGender: 0 = male, 1 = female 2623.51.020232295216.20FAMidpoint – salary grade midpoint Raise – percent of last raise3123.61.028232960413.91FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)3623.61.026232775314.30FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint4023.81.034232490206.30MA14241.04523329012161FA4224.21.0512332100815.71FA1924.31.055233285104.61MA25251.0872341704040MA3226.50.855312595405.60MB227.70.895315280703.90MB3428.60.923312680204.91MB3933.91.094312790615.50FB2034.11.1013144701614.80FB1834.51.1133131801115.60FB335.11.132313075513.61FB1341.11.0274030100214.70FC741.31.0324032100815.71FC1642.21.054404490405.70MC4145.81.144402580504.30MC2746.91.172403580703.91MC548.21.0044836901605.71MD3049.31.0274845901804.30MD2456.31.173483075913.80FD4556.91.185483695815.21FD4757.21.003573795505.51ME3357.51.008573590905.51ME4581.01857421001605.51ME3858.81.0325745951104.50ME5059.61.0465738801204.60ME4660.21.0575739752003.91ME2260.31.257484865613.81FD161.61.081573485805.70ME4461.81.0855745901605.21ME49631.1055741952106.60ME1763.71.1185727553131FE1264.71.1355752952204.50ME4869.51.2195734901115.31FE973.91.103674910010041MF4375.61.1286742952015.50FF2976.31.139675295505.40MF2177.21.1526743951306.31MF678.11.1656736701204.51MF2878.31.169674495914.40FF
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descript ...

Project2

This document summarizes the derivation of the EM algorithm for parameter estimation in a mixed normal model. It begins by presenting the log-likelihood function and derives update equations for the mean (μk) and covariance (Σk) parameters of each normal component. An experimental design is then described to statistically analyze the performance of the EM algorithm under different conditions. The results show that the EM estimates are most accurate when the normal components have distinct means and covariances, and when more training data is available. Interactions between factors are also examined.

Data Analysis Assignment Help

I am Paul G. I am a Data Analysis Assignment Expert at excelhomeworkhelp.com. I hold a Master's in Statistics, from Queensland, Australia. I have been helping students with their assignments for the past 10 years. I solved assignments related to Data Analysis.
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Evaluation and optimization of variables using response surface methodology

By using the attached file, you will be able to design and optimize your product using Design Experts software.

Monte carlo-simulation

1) The Monte Carlo method is used to determine the expected value of random variables by running multiple simulations or trials. 2) In this example, a Monte Carlo simulation is conducted in Microsoft Excel to calculate the expected total cost of a project with 6 activities that each have a range of possible costs. 3) The simulation involves generating random costs for each activity based on the minimum and maximum values, calculating a total cost, and repeating this process 362 times to estimate the expected project cost within 2% error.

Simple Regression Years with Midwest and Shelf Space Winter .docx

Simple Regression Years with Midwest and Shelf Space Winter 2016 Page 1
Lecture Notes for Simple Linear Regression
Problem Definition: Midwest Insurance wants to develop a model able to predict sales
according to time with the company.
Results for: MIDWEST.MTW
Data Display
Row Sales Years with Midwest xy y2 x2
1 487 3 1461 237169 9
2 445 5 2225 198025 25
3 272 2 544 73984 4
4 641 8 5128 410881 64
5 187 2 374 34969 4
6 440 6 2640 193600 36
7 346 7 2422 119716 49
8 238 1 238 56644 1
9 312 4 1248 97344 16
10 269 2 538 72361 4
11 655 9 5895 429025 81
12 563 6 3378 316969 36
y=4855 x=55 xy=26,091 y
2
=2,240,687 x
2
=329
(x)
2
= 3025
(y)
2
= 23571025
Scatterplot of Midwest Data
Graphs>Scatterplot
Years with Midwest
S
a
le
s
9876543210
700
600
500
400
300
200
Scatterplot of Sales vs Years with Midwest
Evaluate the bivariate graph to determine whether a linear relationship exists and the
nature of the relationship. What happens to y as x increases? What type of relationship do
you see?
Simple Regression Years with Midwest and Shelf Space Winter 2016 Page 2
Dialog box for developing correlation coefficient
Explore Linearity of Relationship for significance using t distribution
Pearson Product Moment
Correlation Coefficient
Stat>Basic Stat>Correlation
Correlations: Sales, Years with Midwest – Minitab readout
Pearson correlation of Sales and Years with Midwest = 0.833
P-Value = 0.001
Formula for computing correlation coefficient
2222
yynxxn
yxxyn
r
Hypothesis for t test for significant correlation
H0: =0
H1: ≠0
Decision Rule: Pvalue and critical ratio/critical value technique
Critical Ratio of t
t=
r
r
n
1
2
2
Conclusion:
Interpretation:
Simple Regression Years with Midwest and Shelf Space Winter 2016 Page 3
Simple linear regression assumes that the relationship between the dependent, y
and independent variable, x can be approximated by a straight line.
Population or Deterministic Model – For each x there is an exact value for y.
y = 0 + 1(x) +
y - value of independent variable
(x) - value of independent variable
0 - Value of population y intercept
1 - Slope of population regression line
- Epsilon represents the difference between y and y’. Epsilon also accounts for the independent
variables that affect y but are not in the model. (The .

1 Computer Assignment 3 --- Hypothesis tests about m.docx

1
Computer Assignment 3 --- Hypothesis tests about means
Statistics, Fall 2014, Singleton
Microsoft Excel has two data analysis tools that are very useful for hypothesis testing about
population means as done in the textbook, chapters 9 and 10. These are
1) t-Test: Two-Sample Assuming Equal Variances
2) t-Test: Two-Sample Assuming Unequal Variances
These tools utilize the two sets of equations in Chapter 10 of the text, namely (1) on page 368
assuming equal variances, and (2) on page 369 assuming unequal variances.
Number 1 tool (equal variances) is strictly for Chapter 10 problems with two samples, each
from its own population, and you are attempting to compare the two population means,
and by hypothesis tests involving .
Number 2 tool (unequal variances) can be used for Chapter 9 or Chapter 10 problems. In
previous online classes taught by Dr Revere, she had students use a combination of various Excel
functions to do Chapter 9 t-tests about . It seems to me that using the unequal-variance tool is
easier. On page 353 of the text there is an almost-correct description of how to use the Chapter-
10-style t-test for Chapter 9. In using a Chapter 10 test for Chapter 9, instead of comparing 2
sets of data, the second set of data is replaced with a fixed hypothesized value (which has zero
variation).
Steps are (for Ch 9 problems)
---Enter a column of your n values of experimental data to be tested.
---Enter another column of at least two of the hypothesized value being tested against (if the null
hypothesis has =1.84 or ≤1.84 or ≥1.84 enter at a column of at least two 1.84’s (contrary to
what the book says, you do not have to use n 1.84’s).
---With menus open the “t-Test: Two-Sample Assuming Unequal Variances” analysis tool.
---Select the “Variable 1 Range” to contain the experimental data.
---Select the “Variable 2 Range” to contain at least two identical values of the number in the
null hypothesis (see the example below which has 1.84). (The book says enter n of them.)
2
---Enter 0 for the “Hypothesized Mean Difference”
---Set the “Labels” checkbox using the rules you obeyed for data in Computer Assignment 1.
In many of the problems the data are set up NOT to use labels.
---Make sure you set “Alpha” to your specified value. Also typing it on the worksheet is helpful
as a reminder.
---The output is going to be compact and I suggest that you set “Output Range” to a cell near the
data.
---Click OK
When you do a Chapter 9 problem this way you are comparing your sample data, which has a
variance that is calculated by Excel, with your hypothesized value, which has a variance of zero
since you entered 2 or more identical values for it. Your “Hypothesized Mean Difference”
must always be zero. Of course this requires the “t-Test: Two-Sample Assuming Unequal
Variances” tool.
Why does this work?
If s22 and are both zero in the equat ...

Probability Assignment Help

I am Stacy W. I am a Probability Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, University of McGill, Canada
I have been helping students with their homework for the past 8 years. I solve assignments related to Probability.
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You can also call on +1 678 648 4277 for any assistance with Probability Assignments.

Fpe 90min-all

This document summarizes a research paper that proposes a new method for estimating the probability of informed trading (PIN) to address biases caused by floating point exceptions (FPEs). When estimating PIN using maximum likelihood estimation, large trade volumes can trigger FPEs that narrow the feasible parameter space and underestimate PIN. The authors develop an alternative likelihood formulation and conduct simulations showing their method produces less biased PIN estimates. Empirical tests on US stock market data from 2007 also indicate the new method better explains the relation between PIN and bid-ask spreads.

BIIntro.ppt

This document provides an introduction to business intelligence and data analytics. It discusses key concepts such as data sources, data warehouses, data marts, data mining, and data analytics. It also covers topics like univariate analysis, measures of dispersion, heterogeneity measures, confidence intervals, cross validation, and ROC curves. The document aims to introduce fundamental techniques and metrics used in business intelligence and data mining.

MLlectureMethod.ppt

MLlectureMethod.ppt

Page 1 of 18Part A Multiple Choice (1–11)______1. Using.docx

Page 1 of 18Part A Multiple Choice (1–11)______1. Using.docx

Chapter10 Revised

Chapter10 Revised

Chapter10 Revised

Chapter10 Revised

Chapter10 Revised

Chapter10 Revised

working with python

working with python

Ali, Redescending M-estimator

Ali, Redescending M-estimator

Simple lin regress_inference

Simple lin regress_inference

Big Data Analysis

Big Data Analysis

Assignment #9First, we recall some definitions that will be help.docx

Assignment #9First, we recall some definitions that will be help.docx

1. Outline the differences between Hoarding power and Encouraging..docx

1. Outline the differences between Hoarding power and Encouraging..docx

Project2

Project2

Data Analysis Assignment Help

Data Analysis Assignment Help

Evaluation and optimization of variables using response surface methodology

Evaluation and optimization of variables using response surface methodology

Monte carlo-simulation

Monte carlo-simulation

Simple Regression Years with Midwest and Shelf Space Winter .docx

Simple Regression Years with Midwest and Shelf Space Winter .docx

1 Computer Assignment 3 --- Hypothesis tests about m.docx

1 Computer Assignment 3 --- Hypothesis tests about m.docx

Probability Assignment Help

Probability Assignment Help

Fpe 90min-all

Fpe 90min-all

BIIntro.ppt

BIIntro.ppt

Excel Homework Help

The Excel Homework Help team comprises highly experienced academic writers who possess the expertise to craft original and unique content for all sorts of Excel homework. Our professionals will meticulously prepare a well-structured academic paper that meets all your requirements without any trace of plagiarism. You can easily access various types of Excel homework writing services from the convenience of your home.
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Statistics Homework Help

I am Hannah Dennis. Currently associated with excelhomeworkhelp.com as Excel homework helper. After completing my master's from Kean University, USA. I was in search of an opportunity that expands my area of knowledge hence I decided to help students with their homework. I have written several excel homework till date to help students overcome numerous difficulties they face.

Quantitative Methods Assignment Help

I am Cecily, K. I am a Quantitative Methods Assignment Expert at excelhomeworkhelp.com. I hold a Master's in Statistics, from Adelaide, Australia. I have been helping students with their assignments for the past 10 years. I solved assignments related to Quantitative Methods.
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Multiple Linear Regression Homework Help

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Mathematical Statistics Assignment Help

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Mathematical Statistics Homework Help

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Probabilistic Systems Analysis Homework Help

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Stochastic Processes Homework Help

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Stochastic Processes Homework Help

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Excel Homework Help

Excel Homework Help

Statistics Homework Help

Statistics Homework Help

Quantitative Methods Assignment Help

Quantitative Methods Assignment Help

Quantitative Analysis Homework Help

Quantitative Analysis Homework Help

Multiple Linear Regression Homework Help

Multiple Linear Regression Homework Help

Mathematical Statistics Assignment Help

Mathematical Statistics Assignment Help

Mathematical Statistics Assignment Help

Mathematical Statistics Assignment Help

Mathematical Statistics Homework Help

Mathematical Statistics Homework Help

Probabilistic Systems Analysis Homework Help

Probabilistic Systems Analysis Homework Help

Excel Homework Help

Excel Homework Help

Probabilistic Systems Analysis Homework Help

Probabilistic Systems Analysis Homework Help

Stochastic Processes Homework Help

Stochastic Processes Homework Help

Stochastic Processes Homework Help

Stochastic Processes Homework Help

Stochastic Processes Homework Help

Stochastic Processes Homework Help

How To Update One2many Field From OnChange of Field in Odoo 17

There can be chances when we need to update a One2many field when we change the value of any other fields in the form view of a record. In Odoo, we can do this. Let’s go with an example.

BRIGADA ESKWELA OPENING PROGRAM KICK OFF.pptx

BRIGADA ESKWELA OPENING PROGRAM

Introduction to Banking System in India.ppt

Bank – Banking – Banking System in India – Origin of Bank-Classification of Banks –Types of Customers RBI Functions- Commercial Banks – Functions

How to Manage Early Receipt Printing in Odoo 17 POS

This slide will represent how to manage the early receipt printing option in Odoo 17 POS. Early receipts offer transparency and clarity for each customer regarding their individual order. Also printing receipts as orders are placed, we can potentially expedite the checkout process when the bill is settled.

New Features in Odoo 17 Sign - Odoo 17 Slides

The Sign module available in the Odoo ERP platform is exclusively designed for sending, signing, and approving documents digitally. The intuitive interface of the module with the drag and drop fields helps us to upload our pdf easily and effectively. In this slide, let’s discuss the new features in the sign module in odoo 17.

Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...

Title: Relational Database Management System Concepts(RDBMS)
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : DATA INTEGRITY, CREATING AND MAINTAINING A TABLE AND INDEX
Sub-Topic :
Data Integrity,Types of Integrity, Integrity Constraints, Primary Key, Foreign key, unique key, self referential integrity,
creating and maintain a table, Modifying a table, alter a table, Deleting a table
Create an Index, Alter Index, Drop Index, Function based index, obtaining information about index, Difference between ROWID and ROWNUM
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
Feedback and Contact Information:
Your feedback is valuable! For any queries or suggestions, please contact muruganjit@agacollege.in

C# Interview Questions PDF By ScholarHat.pdf

C# Interview Questions PDF

RDBMS Lecture Notes Unit4 chapter12 VIEW

Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : VIEW
Sub-Topic :
View Definition, Advantages and disadvantages, View Creation Syntax, View creation based on single table, view creation based on multiple table, Deleting View and View the definition of view
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
Previous Slides Link:
1. Data Integrity, Index, TAble Creation and maintenance https://www.slideshare.net/slideshow/lecture_notes_unit4_chapter_8_9_10_rdbms-for-the-students-affiliated-by-alagappa-university/270123800
2. Sequences : https://www.slideshare.net/slideshow/sequnces-lecture_notes_unit4_chapter11_sequence/270134792
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.

(T.L.E.) Agriculture: Essentials of Gardening

(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏.𝟎)-𝐅𝐢𝐧𝐚𝐥𝐬
Lesson Outcome:
-Students will understand the basics of gardening, including the importance of soil, water, and sunlight for plant growth. They will learn to identify and use essential gardening tools, plant seeds, and seedlings properly, and manage common garden pests using eco-friendly methods.

"DANH SÁCH THÍ SINH XÉT TUYỂN SỚM ĐỦ ĐIỀU KIỆN TRÚNG TUYỂN ĐẠI HỌC CHÍNH QUY ...

"DANH SÁCH THÍ SINH XÉT TUYỂN SỚM ĐỦ ĐIỀU KIỆN TRÚNG TUYỂN ĐẠI HỌC CHÍNH QUY NĂM 2024
KHỐI NGÀNH NGOÀI SƯ PHẠM"

Odoo 17 Events - Attendees List Scanning

Use the attendee list QR codes to register attendees quickly. Each attendee will have a QR code, which we can easily scan to register for an event. You will get the attendee list from the “Attendees” menu under “Reporting” menu.

modul ajar kelas x bahasa inggris 2024-2025

modul ajar kelas x 2024-2025

How to Manage Access Rights & User Types in Odoo 17

In Odoo, who have access to the database they are called users. There are different types of users in odoo and they have different accesses into the database. Access rights are permissions that can be set for the individual or group of users. This slide will show How to Manage Access Rights & User Types in Odoo 17.

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi Book Summary

Mail Server Configuration Using App passwords in Odoo 17

In Odoo 17, we can securely configure an email server to send and receive emails within the application. This is useful for features like sending quotations, invoices, and notifications via email. If our email service provider (e.g., Gmail, Outlook) supports app passwords, we can use them to authenticate our Odoo instance with the email server.

How to Empty a One2Many Field in Odoo 17

This slide discusses how to delete or clear records in an Odoo 17 one2many field. We'll achieve this by adding a button named "Delete Records." Clicking this button will delete all associated one2many records.

Allopathic M1 Srudent Orientation Powerpoint

Allopathic Medical M1 Orientation

Debts of Gratitude - Esselde and Informal Letter.pptx

Debts of Gratitude - Esselde and Informal Letter.pptx

How to Create & Publish a Blog in Odoo 17 Website

A blog is a platform for sharing articles and information. In Odoo 17, we can effortlessly create and publish our own blogs using the blog menu. This presentation provides a comprehensive guide to creating and publishing a blog on your Odoo 17 website.

How to Manage Line Discount in Odoo 17 POS

This slide will cover the management of line discounts in Odoo 17 POS. Using the Line discount approach, we can apply discount for individual product lines.

How To Update One2many Field From OnChange of Field in Odoo 17

How To Update One2many Field From OnChange of Field in Odoo 17

BRIGADA ESKWELA OPENING PROGRAM KICK OFF.pptx

BRIGADA ESKWELA OPENING PROGRAM KICK OFF.pptx

Introduction to Banking System in India.ppt

Introduction to Banking System in India.ppt

How to Manage Early Receipt Printing in Odoo 17 POS

How to Manage Early Receipt Printing in Odoo 17 POS

New Features in Odoo 17 Sign - Odoo 17 Slides

New Features in Odoo 17 Sign - Odoo 17 Slides

Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...

Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...

C# Interview Questions PDF By ScholarHat.pdf

C# Interview Questions PDF By ScholarHat.pdf

RDBMS Lecture Notes Unit4 chapter12 VIEW

RDBMS Lecture Notes Unit4 chapter12 VIEW

(T.L.E.) Agriculture: Essentials of Gardening

(T.L.E.) Agriculture: Essentials of Gardening

"DANH SÁCH THÍ SINH XÉT TUYỂN SỚM ĐỦ ĐIỀU KIỆN TRÚNG TUYỂN ĐẠI HỌC CHÍNH QUY ...

"DANH SÁCH THÍ SINH XÉT TUYỂN SỚM ĐỦ ĐIỀU KIỆN TRÚNG TUYỂN ĐẠI HỌC CHÍNH QUY ...

Odoo 17 Events - Attendees List Scanning

Odoo 17 Events - Attendees List Scanning

modul ajar kelas x bahasa inggris 2024-2025

modul ajar kelas x bahasa inggris 2024-2025

How to Manage Access Rights & User Types in Odoo 17

How to Manage Access Rights & User Types in Odoo 17

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

Mail Server Configuration Using App passwords in Odoo 17

Mail Server Configuration Using App passwords in Odoo 17

How to Empty a One2Many Field in Odoo 17

How to Empty a One2Many Field in Odoo 17

Allopathic M1 Srudent Orientation Powerpoint

Allopathic M1 Srudent Orientation Powerpoint

Debts of Gratitude - Esselde and Informal Letter.pptx

Debts of Gratitude - Esselde and Informal Letter.pptx

How to Create & Publish a Blog in Odoo 17 Website

How to Create & Publish a Blog in Odoo 17 Website

How to Manage Line Discount in Odoo 17 POS

How to Manage Line Discount in Odoo 17 POS

- 1. For any Homework related queries, Call us at:- +1 678 648 4277 You can mail us at:- info@excelhomeworkhelp.com or reach us at:- www.excelhomeworkhelp.com
- 2. 1. CAPM Model The CAPM model was fit to model the excess returns of Exxon-Mobil (Y) as a linear function of the excess returns of the market (X) as represented by the S&P 500 Index. Yi = α + βXi + Ei where the Ei are assumed to be uncorrelated, with zero mean and constant variance σ2 . Using a recent 500-day analysis period the following output was generated in R: statisticshomeworkhelper.com excelhomeworkhelp.com
- 3. print(summary(lmfit0)) Call: lm(formula = r.daily.symbol0.0[index.window] ~ r.daily.SP500.0[index.window], x = TRUE, y = TRUE) Residuals: Min 1Q Median 3Q Max 0.038885 -0.004415 0.000187 0.004445 0.026748 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0004805 0.0003360 -1.43 0.153 r.daily.SP500.0[index.window] 0.9190652 0.0454380 20.23 <2e-16 Residual standard error: 0.007489 on 498 degrees of freedom Multiple R-squared: 0.451, Adjusted R-squared: 0.4499 F-statistic: 409.1 on 1 and 498 DF, p-value: < 2.2e-16 statisticshomeworkhelper.com excelhomeworkhelp.com
- 4. (a). Explain the meaning of the residual standard error. Solution: The residual standard error is an estimate of the standard devi ation of the error term in the regression model. It is given by It measures the standard deviation of the difference between the actual and fitted value of the dependent variable. (b). What does “498 degrees of freedom” mean? Solution: The degrees of freedom equals (n − p) where n = 500 is the number of sample values and p = 2 is the number of regression parameters being estimated. (c). What is the correlation between Y (Stock Excess Return) and X (Market Excess Return)? Solution: The correlation is (we know it is positive because of the positive slope coefficient 0.919) statisticshomeworkhelper.com excelhomeworkhelp.com
- 5. (d). Using this output, can you test whether the alpha of Exxon Mobil is zero (consistent with asset pricing in an efficient market). H0 : α = 0 at the significance level α = .05? If so, conduct the test, explain any assumptions which are necessary, and state the result of the test? Solution: Yes, apply a t-test of H0: intercept equals 0. R computes this in the coefficients table and the statistic value is −1.43 with a (two-sided) p-value of 0.153. For a nominal significance level of .05 for the test (twosided), the null hypothesis is not rejected because the p-value is higher than the significance level. The assumptions necessary to conduct the test are that the error terms in the regression are i.i.d. normal variables with mean zero and constant variance σ2 > 0. If the normal distribution doesn’t apply, then so long as the error distribution has mean zero and constant variance, the test is approximately approximately correct and equivalent to using a z-test for the parameter/estimate and the CLT.) (e). Using this output, can you test whether the β of Exxon Mobil is less than 1, i.e., is Exxon Mobil less risky than the market: H0 : β = 1 versus HA : β < 1. If so, what is your test statistic; what is the approximate P -value of the test (clearly state any assumptions you make)? Would you reject H0 in favor of HA? statisticshomeworkhelper.com excelhomeworkhelp.com
- 6. Solution: Yes, we apply a one-sided t-test using the statistic: Under the null hypothesis T has a t-distribution with 498 degrees of free dom. This distribution is essentially the N(0, 1) distribution since the degrees of freedom is so high. The p-value of this statistic (one-sided) is less than 0.05 because P (Z< −1.645) = 0.05 for a Z ∼ N(0, 1) so P (T< −1.7841) ≈ P (Z< −1.7841) which is smaller. 5. For the following batch of numbers: 5, 8, 9, 9, 11, 13, 15, 19, 19, 20, 29 (a). Make a stem-and-leaf plot of the batch. (b). Plot the ECDF (empirical cumulative distribution function) of the batch. (c). Draw the Boxplot of the batch. Solution: x=c(5,8,9,9,11,13,15,19,19,20,29) stem(x) The decimal point is 1 digit(s) to the right of the | statisticshomeworkhelper.com excelhomeworkhelp.com
- 7. 0 | 5899 1 | 13 1 | 599 2|0 2|9 > plot(ecdf(x)) statisticshomeworkhelper.com excelhomeworkhelp.com
- 9. Note that the center of the box is at the median (19), the bottom is at the 25-th percentile and top is at the 75-th percentile. The inter-quartile range is (19- 9)=10, so any value more than 1.5 × 9 = 13.5 units above or below the box will be plotted as outliers. There are no such outliers. 6. Suppose X1,...,Xn are n values sampled at random from a fixed distri bution: Xi = θ + Ei where θ is a location parameter and the Ei are i.i.d. random variables with mean zero and median zero. (a). Give explicit definitions of 3 different estimators of the location pa rameter θ. (b). For each estimator in (a), explain under what conditions it would be expected to be better than the other two. Solution: (a). Consider the sample mean, the sample median, and the 10%-Trimmed mean. statisticshomeworkhelper.com excelhomeworkhelp.com
- 10. θT rimmedMean = average of {Xi} after excluding the highest 10% and the lowest 10% values. (b). We expect the sample mean to be the best when the data are a random sample from the same normal distribution. In this case it is the MLE and will have lower variability than any other estimate. We expect the same median to be the best when the data are a random sample from the bilateral exponential distribution. In this case it is the MLE and will hve lower variability, asymptotically than any other esti mate. Also, the median is robust against gross outliers in the data result ing from the possibility of sampling distribution including a contamination component. We expect the trimmed mean to be best when the chance of gross errors in the data are such that no more than 10% of the highest and 10% of the lowest could be such gross errors/outliers. For this estimate to be better than the median, it must be that the information in the mean of the re maining values (80% untrimmed) is more than the median. This would be the case if 80% of the data values came from a normal distribution/model. arise from a normal distribution with statisticshomeworkhelper.com excelhomeworkhelp.com
- 11. q _0.5 q_0.75 q_0.9 q_0.95 q_0.99 q_0.999 N(0,1) 0.00 0.67 1.28 1.64 2.33 3.09 t (df=1) 0.00 1.00 3.08 6.31 31.82 318.31 t (df=2) 0.00 0.82 1.89 2.92 6.96 22.33 t (df=3) 0.00 0.76 1.64 2.35 4.54 10.21 t (df=4) 0.00 0.74 1.53 2.13 3.75 7.17 t (df=5) 0.00 0.73 1.48 2.02 3.36 5.89 t (df=6) 0.00 0.72 1.44 1.94 3.14 5.21 t (df=7) 0.00 0.71 1.41 1.89 3.00 4.79 t (df=8) 0.00 0.71 1.40 1.86 2.90 4.50 t (df=9) 0.00 0.70 1.38 1.83 2.82 4.30 t (df=10) 0.00 0.70 1.37 1.81 2.76 4.14 t (df=25) 0.00 0.68 1.32 1.71 2.49 3.45 t (df=50) 0.00 0.68 1.30 1.68 2.40 3.26 t (df=100) 0.00 0.68 1.29 1.66 2.36 3.17 t (df=500) 0.00 0.67 1.28 1.65 2.33 3.11 Percentiles of the Normal and t Distributions statisticshomeworkhelper.com excelhomeworkhelp.com