Economics Honours , Statistics, Mathematics Tuition Coaching Class

Economics Honours , Statistics, Mathematics Tuition Coaching Class

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Need tuition for maths
Now I am waiting Mathematics tomorrow
Jay Shree Ram
kau bolte parbe... j 11 12 er j economics ache seta science arts and commerce er khetre alada na ek?
Required faculties for a well renowned institute of North - West Delhi

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For subjects: MME, SME, Micro/Macro Eco, Econometrics

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How to use binomial applications in economics
Binary data occur when you can place an observation into only two categories. Learn how to use the binomial, geometric, negative binomial, and the hyper-geometric distributions to glean more information from your binary data.

Maximize the Value of Your Binary Data with the Binomial and Other Probability Distributions

By Jim Frost / Penn State University
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Please share and subscribe to Statistics By Jim. Jim Frost has over 20 years experience in Statistics Research and Consulting.

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03/05/2022

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02/05/2022

Statistics || Question Answer Solution

ISS Statistics
Statistics - I paper of Indian Statistical Services is of objective type with 80 questions and 200 marks. The duration of the paper is 2 hours. The syllabus has four sections - Probability, Statistical Methods, Numerical Analysis and Computer Application & Data processing.

1. Probability
Classical and axiomatic definitions of Probability and consequences. Law of total probability, Conditional probability, Bayes' theorem and applications. Discrete and continuous random variables. Distribution functions and their properties.

Standard discrete and continuous probability distributions - Bernoulli, Uniform, Binomial, Poisson, Geometric, Rectangular, Exponential, Normal, Cauchy, Hyper geometric, Multinomial, Laplace, Negative binomial, Beta, Gamma, Lognormal. Random vectors, Joint and marginal distributions, conditional distributions, Distributions of functions of random variables. Modes of convergences of sequences of random variables - in distribution, in probability, with probability one and in mean square. Mathematical expectation and conditional expectation. Characteristic function, moment and probability generating functions, Inversion, uniqueness and continuity theorems. Borel 0-1 law, Kolmogorov's 0-1 law. Tchebycheff's and Kolmogorov's inequalities. Laws of large numbers and central limit theorems for independent variables.

2. Statistical Methods
Collection, compilation and presentation of data, charts, diagrams and histogram. Frequency distribution. Measures of location, dispersion, skewness and kurtosis. Bivariate and multivariate data. Association and contingency. Curve fitting and orthogonal polynomials. Bivariate normal distribution. Regression-linear, polynomial. Distribution of the correlation coefficient, Partial and multiple correlation, Intraclass correlation, Correlation ratio.

Standard errors and large sample test. Sampling distributions of sample mean, sample variance, t, chi-square and F; tests of significance based on them, Small sample tests.

Non-parametric tests-Goodness of fit, sign, median, run, Wilcoxon, Mann-Whitney, Wald-Wolfowitz and Kolmogorov-Smirnov. Order statistics - minimum, maximum, range and median. Concept of Asymptotic relative efficiency.

3. Numerical Analysis
Finite differences of different orders: ∆, E and D operators, factorial representation of a polynomial, separation of symbols, sub-division of intervals, differences of zero.

Concept of interpolation and extrapolation: Newton Gregory's forward and backward interpolation formulae for equal intervals, divided differences and their properties, Newton's formula for divided difference, Lagrange's formula for unequal intervals, central difference formula due to Gauss, Sterling and Bessel, concept of error terms in interpolation formula.

Inverse interpolation: Different methods of inverse interpolation.

Numerical differentiation: Trapezoidal, Simpson's one-third and three-eight rule and Waddles rule.

Summation of Series: Whose general term (i) is the first difference of a function (ii) is in geometric progression.

Numerical solutions of differential equations: Euler's Method, Milne's Method, Picard's Method and Runge-Kutta Method.

4. Computer Application and Data Processing
Basics of Computer: Operations of a computer, Different units of a computer system like central processing unit, memory unit, arithmetic and logical unit, input unit, output unit etc., Hardware including different types of input, output and peripheral devices, Software, system and application software, number systems, Operating systems, packages and utilities, Low and High level languages, Compiler, Assembler, Memory - RAM, ROM, unit of computer memory (bits, bytes etc.), Network - LAN, WAN, internet, intranet, basics of computer security, virus, antivirus, firewall, spyware, malware etc.

Basics of Programming: Algorithm, Flowchart, Data, Information, Database, overview of different programming languages, frontend and backend of a project, variables, control structures, arrays and their usages, functions, modules, loops, conditional statements, exceptions, debugging and related concepts.



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02/05/2022

Substitution or Income Effect

Income Effect vs. Substitution Effect: What's the Difference?
Income Effect vs. Substitution Effect: An Overview
The income effect expresses the impact of increased purchasing power on consumption, while the substitution effect describes how consumption is impacted by changing relative income and prices.

These economic concepts concern changes in the market and how they impact consumption patterns for consumer goods and services.

Different goods and services are affected by these changes in the market in different ways. Some products, called inferior goods, generally are purchased less whenever incomes increase. Consumer spending on normal goods typically increases with higher purchasing power, which is in contrast with inferior goods.

KEY TAKEAWAYS
The income effect is the change in the consumption of goods by consumers based on their income (purchasing power).
The substitution effect happens when consumers replace cheaper items with more expensive ones due to price changes or when their financial conditions improve, and vice-versa.
The income effect can be both direct (when it is directly related to a change in income) or indirect (when consumers must make buying decisions not directly related to their incomes).
A small reduction in price may make an expensive product more attractive to consumers, which can also lead to the substitution effect.
Companies experience the substitution effect, too, e.g., when they outsource production to take advantage of cheaper labor costs.

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02/05/2022

Roy's Identity

Roy's identity (named for French economist Rene Roy) is a major result in microeconomics having applications in consumer choice and the theory of the firm. The lemma relates the ordinary demand function to the derivatives of the indirect utility function.

Derivation of Roy's identity

Roy's identity reformulates Shephard's lemma in order to get a Marshallian demand function for an individual and a good (i) from some indirect utility function.

The first step is to consider the trivial identity obtained by substituting the expenditure function for wealth or income (m)in the indirect utility function (psi (m, p), at a utility of u):

:psi ( e(p, u), p) = u

This says that the indirect utility function evaluated in such a way that minimizes the cost for achieving a certain utility given a set of prices (a vector p) is equal to that utility when evaluated at those prices.

Taking the derivative of both sides of this equation with respect to the price of a single good p_i (with the utility level held constant) gives:

:frac{ partial psi [e(u,p),p] }{partial m} frac{partial e(u,p)}{partial p_i} + frac{partial psi [e(u,p),p] }{partial p_i} = 0.

Rearranging gives the desired result:

:frac{partial e(u,p)}{partial p_i}=-frac{frac{partial psi [e(u,p),p] }{partial p_i{frac{partial psi [e(u,p),p] }{partial m=x_i(m,p)

Application

This gives a method of deriving the Marshallian demand function of a good for some consumer from the indirect utility function of that consumer. It is also fundamental in deriving the Slutsky equation.



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