It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
Regression Analysis -Meaning, Uses, Properties Difference between Regression and Correlation and Methods of Studying Regression are included in the ppt (only Theory part)
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
Biostats coorelation vs rREGRESSION.DIFFERENCE BETWEEN CORRELATION AND REGRES...Payaamvohra1
CORRELATION
REGRESSION
BIOSTATISTICS
SEMESTER 8
M PHARMACY
CORRELATION VS REGRESSION
REGRESSION ANALYSIS
LINEAR AND MULTIPLE REGREISSIO
CORRELATION COEFFICIENT
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
It includes various cases and practice problems related to Binomial, Poisson & Normal Distributions. Detailed information on where tp use which probability.
This presentation includes detailed information on Hypothesis testing for large and small samples, for two sample means. Briefed computational procedure with various case studies.
This presentation includes topics related to sampling and its distributions, estimates related to large samples and small samples using Z test and T test respectively. Also when to use Finite Population Multiplier is explained in detail.
It includes introduction to quantitative techniques; Meaning, Importance applications and Limitations of statistics. Primary vs Secondary Data and their collection methods, Different graphs and their examples. Classification of data, types of data/series etc.
This presentation describes Matrices and Determinants in detail including all the relevant definitions with examples, various concepts and the practice problems.
It includes concepts of logarithms including properties and log tables. The methodology to find out values using log tables and anti log tables is also mentioned in a detailed manner. Moreover, questions related to logarithms are mentioned for practice.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2. REGRESSION
Regression Analysis measures the nature and
extent of the relationship between two or more
variables, thus enables us to make predictions.
Regression is the measure of the average
relationship between two or more variables.
BirinderSingh,AssistantProfessor,PCTE
3. UTILITY OF REGRESSION
Degree & Nature of relationship
Estimation of relationship
Prediction
Useful in Economic & Business Research
BirinderSingh,AssistantProfessor,PCTE
4. DIFFERENCE BETWEEN CORRELATION &
REGRESSION
Degree & Nature of Relationship
Correlation is a measure of degree of relationship
between X & Y
Regression studies the nature of relationship
between the variables so that one may be able to
predict the value of one variable on the basis of
another.
Cause & Effect Relationship
Correlation does not always assume cause and effect
relationship between two variables.
Regression clearly expresses the cause and effect
relationship between two variables. The independent
variable is the cause and dependent variable is effect.
BirinderSingh,AssistantProfessor,PCTE
5. DIFFERENCE BETWEEN CORRELATION &
REGRESSION
Prediction
Correlation doesn’t help in making predictions
Regression enable us to make predictions using
regression line
Symmetric
Correlation coefficients are symmetrical i.e. rxy = ryx.
Regression coefficients are not symmetrical i.e. bxy ≠ byx.
Origin & Scale
Correlation is independent of the change of origin and
scale
Regression coefficient is independent of change of origin
but not of scale
BirinderSingh,AssistantProfessor,PCTE
6. TYPES OF REGRESSION ANALYSIS
Simple & Multiple Regression
Linear & Non Linear Regression
Partial & Total Regression
BirinderSingh,AssistantProfessor,PCTE
8. REGRESSION LINES
The regression line shows the average relationship
between two variables. It is also called Line of Best Fit.
If two variables X & Y are given, then there are two
regression lines:
Regression Line of X on Y
Regression Line of Y on X
Nature of Regression Lines
If r = ±1, then the two regression lines are coincident.
If r = 0, then the two regression lines intersect each other at
90°.
The nearer the regression lines are to each other, the greater
will be the degree of correlation.
If regression lines rise from left to right upward, then
correlation is positive.
BirinderSingh,AssistantProfessor,PCTE
9. REGRESSION EQUATIONS
Regression Equations are the algebraic
formulation of regression lines.
There are two regression equations:
Regression Equation of Y on X
Y = a + bX
Y – 𝑌 = 𝑏𝑦𝑥 (𝑋 − 𝑋)
Y – 𝑌 = 𝑟.
σ 𝑦
σ 𝑥
(𝑋 − 𝑋)
Regression Equation of X on Y
X = a + bY
X – 𝑋 = 𝑏𝑥𝑦 (𝑌 − 𝑌)
X – 𝑋 = 𝑟.
σ 𝑥
σ 𝑦
(𝑌 − 𝑌)
BirinderSingh,AssistantProfessor,PCTE
10. REGRESSION COEFFICIENTS
Regression coefficient measures the average
change in the value of one variable for a unit
change in the value of another variable.
These represent the slope of regression line
There are two regression coefficients:
Regression coefficient of Y on X: byx = 𝑟.
σ 𝑦
σ 𝑥
Regression coefficient of X on Y: bxy = 𝑟.
σ 𝑥
σ 𝑦
BirinderSingh,AssistantProfessor,PCTE
11. PROPERTIES OF REGRESSION
COEFFICIENTS
Coefficient of correlation is the geometric mean of
the regression coefficients. i.e. r = 𝑏 𝑥𝑦 . 𝑏𝑦𝑥
Both the regression coefficients must have the
same algebraic sign.
Coefficient of correlation must have the same sign
as that of the regression coefficients.
Both the regression coefficients cannot be greater
than unity.
Arithmetic mean of two regression coefficients is
equal to or greater than the correlation
coefficient. i.e.
𝑏𝑥𝑦+𝑏𝑦𝑥
2
≥ r
Regression coefficient is independent of change of
origin but not of scale
BirinderSingh,AssistantProfessor,PCTE
13. REGRESSION EQUATIONS IN INDIVIDUAL
SERIES USING NORMAL EQUATIONS
This method is also called as Least Square Method.
Under this method, regression equations can be
calculated by solving two normal equations:
For regression equation Y on X: Y = a + bX
Σ𝑌 = 𝑁𝑎 + 𝑏Σ𝑋
Σ𝑋𝑌 = 𝑎Σ𝑋 + 𝑏Σ𝑋2
Another Method
byx =
𝑁 .Σ𝑋𝑌 − Σ𝑋.Σ𝑌
𝑁.Σ𝑋2 −(Σ𝑋)2
& a = 𝑌 − b𝑋
Here a is the Y – intercept, indicates the minimum
value of Y for X = 0
& b is the slope of the line, indicates the absolute
increase in Y for a unit increase in X.
BirinderSingh,AssistantProfessor,PCTE
14. PRACTICE PROBLEMS
Q1: Calculate the regression equation of X on Y using
method of least squares: X = 0.5 + 0.5Y
Q2: Given the following data:
N = 8, ƩX = 21, ƩX2 = 99, ƩY = 4, ƩY2 = 68, ƩXY = 36
Using the values, find:
o Regression Equation of Y on X Y = – 1.025 + 0.581X
o Regression Equation of X on Y X = 2.432 + 0.386Y
o Value of Y when X = 10 Y = 4.785
o Value of X when Y = 2.5 X = 3.397
BirinderSingh,AssistantProfessor,PCTE
X 1 2 3 4 5
Y 2 5 3 8 7
16. REGRESSION EQUATIONS USING
REGRESSION COEFFICIENTS
Methods
Using Actual
Values of
X & Y
Using deviations
from Actual
Means
Using deviations
from Assumed
Means
Using r, σx, σy
BirinderSingh,AssistantProfessor,PCTE
17. REGRESSION EQUATIONS USING REGRESSION
COEFFICIENTS (USING ACTUAL VALUES)
Regression Equation of Y on X
Y – 𝑌 = byx (X – 𝑋) where byx =
𝑁 .Σ𝑋𝑌 − Σ𝑋.Σ𝑌
𝑁.Σ𝑋2 −(Σ𝑋)2
Regression Equation of X on Y
X – 𝑋 = bxy (Y – 𝑌) where bxy =
𝑁 .Σ𝑋𝑌 − Σ𝑋.Σ𝑌
𝑁.Σ𝑌2 −(Σ𝑌)2
Q3: Calculate the regression equation of Y on X & X on Y
Y = 1.3X + 1.1, X = 0.5 + 0.5Y
BirinderSingh,AssistantProfessor,PCTE
18. REGRESSION EQUATIONS USING REGRESSION
COEFFICIENTS (USING DEVIATIONS FROM
ACTUAL VALUES)
Regression Equation of Y on X
Y – 𝑌 = byx (X – 𝑋) where byx =
Σ𝑥𝑦
Σ𝑥2
Regression Equation of X on Y
X – 𝑋 = bxy (Y – 𝑌) where bxy =
Σ𝑥𝑦
Σ𝑦2
Q4: Calculate the regression equation of Y on X & X on Y
using method of least squares: Y = 0.26X + 3.2, X = 4.75 + 0.45Y
BirinderSingh,AssistantProfessor,PCTE
X 2 4 6 8 10 12
Y 4 2 5 10 3 6
19. REGRESSION EQUATIONS USING REGRESSION
COEFFICIENTS (USING DEVIATIONS FROM
ASSUMED MEAN)
Regression Equation of Y on X
Y – 𝑌 = byx (X – 𝑋) where byx =
𝑁 .Σ𝑑𝑥𝑑𝑦 − Σ𝑑𝑥 Σ𝑑𝑦
𝑁.Σ𝑑𝑥2 −(Σ𝑑𝑥)2
Regression Equation of X on Y
X – 𝑋 = bxy (Y – 𝑌) where bxy =
𝑁 .Σ𝑑𝑥𝑑𝑦 − Σ𝑑𝑥 Σ𝑑𝑦
𝑁.Σ𝑑𝑦2 −(Σ𝑑𝑦)2
Q5: Calculate the regression equation of Y on X & X
Y = 1.212 X + 34.725
BirinderSingh,AssistantProfessor,PCTE
X 78 89 97 69 59 79 68 61
Y 125 137 156 112 107 136 124 108
21. REGRESSION EQUATIONS USING REGRESSION
COEFFICIENTS (USING STANDARD DEVIATIONS)
Regression Equation of Y on X
Y – 𝑌 = byx (X – 𝑋) where byx = 𝑟.
σ 𝑦
σ 𝑥
Regression Equation of X on Y
X – 𝑋 = bxy (Y – 𝑌) where bxy = 𝑟.
σ 𝑥
σ 𝑦
Q6: Estimate Y when X = 9 as per the following information:
Y = 15.88
BirinderSingh,AssistantProfessor,PCTE
X Y
Arithmetic Mean 5 12
Standard Deviation 2.6 3.6
Correlation Coefficient 0.7
22. PRACTICE PROBLEMS
Q7: If 𝑋 = 25, 𝑌 = 120, bxy = 2. Estimate the value of X
when Y = 130. X = 45
Q8: If σ 𝑥
2
= 9, σ 𝑦
2
= 1600, obtain bxy. bxy = 0.04
Q9: Given two regression equations:
3X + 4Y = 44
5X + 8Y = 80
Variance of X = 30.
Find 𝑋, 𝑌, r and σ 𝑦
8,5,– 0.91, 3.7
BirinderSingh,AssistantProfessor,PCTE
23. SHORTCUT METHOD OF CHECKING
REGRESSION EQUATIONS
Suppose two regression equations are as follows:
a1x + b1y + c1 = 0
a2x + b2y + c2 = 0
Case 1: If a1b2 ≤ a2b1 (in magnitude, ignoring negative), then
a1x + b1y + c1 = 0 is the regression of Y on X
a2x + b2y + c2 = 0 is the regression of X on Y
Case 2: If a1b2 > a2b1 (in magnitude, ignoring negative), then
a1x + b1y + c1 = 0 is the regression of X on Y
a2x + b2y + c2 = 0 is the regression of Y on X
BirinderSingh,AssistantProfessor,PCTE
24. STANDARD ERROR OF ESTIMATE
Standard error of estimate helps us to know that
to what extent the estimates are accurate.
It shows that to what extent the estimated values
by regression line are closer to actual values
For two regression lines, there are two standard
error of estimates:
Standard error of estimate of Y on X (Syx)
Standard error of estimate of X on Y (Sxy)
BirinderSingh,AssistantProfessor,PCTE
25. FORMULAE FOR SE (Y ON X)
Syx =
Σ 𝑌 −𝑌𝑐 2
𝑁
Y = Actual Values,
Yc = Estimated Values
Syx =
Σ𝑌2
−𝑎Σ𝑌 −𝑏Σ𝑋𝑌
𝑁
Here a & b are to be
obtained from normal equations
Syx = σy 1 − 𝑟2
BirinderSingh,AssistantProfessor,PCTE
26. PRACTICE PROBLEMS – SE
Q10: Find the Standard error of estimates if σx = 4.4,
σy = 2.2 & r = 0.8 Ans: 1.32, 2.64
Q11: Given: ƩX = 15, ƩY = 110, ƩXY = 400, ƩX2 = 250,
ƩY2 = 3200, N = 10. Calculate Syx Ans: 13.21
Q12: Compute regression equation Y on X. Hence, find Syx
Ans: Y = 11.9 – 0.65X, 0.79
BirinderSingh,AssistantProfessor,PCTE
X 6 2 10 4 8
Y 9 11 5 8 7