This document contains notes from an algebra class. It summarizes that:
1) All new algebra students are responsible for their own grades, including online and notebook assignments. Students should ask friends or the teacher for details.
2) For the previous quarter, every student who completed less than half of their classwork and online assignments averaged below 50% on tests. Completing assignments prepares students for tests, which make up a large part of the grade.
3) Upcoming topics include graphing systems of equations, systems of equations with elimination, and systems of equations with substitution.
The document provides a daily agenda and notes for a math class that includes:
- Khan Academy practice and an exponent test on Tuesday
- Reviewing class work on scientific notation and exponents
- Notes on problems involving scientific notation, exponents, and monomials including finding degrees, multiplying and dividing monomials, and writing expressions in scientific and standard form
- A reminder to show work when using a calculator and that copied answers are often incorrect
The document discusses how traditional math curriculums fail to teach students how to apply mathematical concepts to real-world contexts. It argues that the root cause is a lack of emphasis on contextual or applied math education. The solution proposed is for math courses to integrate real-life problems and applications to help students understand how mathematical tools can clarify practical situations.
The document discusses constraint satisfaction problems (CSPs). It defines a CSP as having variables with domains of possible values and constraints limiting the values variables can take. A solution assigns values to all variables while satisfying constraints. The document outlines backtracking search and constraint propagation techniques for solving CSPs, including variable and value ordering heuristics, forward checking, and arc consistency. Arc consistency is more effective than forward checking at detecting inconsistencies and pruning the search space. The document provides examples of CSP formulations for map coloring, Sudoku, and N-Queens problems.
"Yeah But How Do I Translate That to a Percentage?" -- STA Convention 2022.pdfChris Hunter
The document discusses standards-based assessment and answers common questions about the transition from traditional grading to standards-based assessment. Some key points include:
- Standards-based assessment focuses on demonstrating evidence of learning standards rather than accumulating points, and compares student learning to proficiency levels rather than other students.
- The reasons for changing include making assessment more accurate, fair, and relevant to learning, and shifting student focus from grades to learning.
- Assessment should evaluate specific delineated learning standards rather than broad topics. Descriptors define each level of the proficiency scale from emerging to extending.
- Evidence of learning can come from products, observations, and conversations, rather than single events like tests. Tracking data over
This document discusses constraint satisfaction problems (CSPs) and algorithms for solving them. It begins with an example scheduling CSP involving class scheduling with various constraints. It then defines the components of a CSP - variables, domains, and constraints. Backtracking search is presented as a basic algorithm for solving CSPs by systematically trying value assignments. The document notes that the variable and value ordering used in backtracking can affect both the runtime and the solution found. It then introduces constraint propagation techniques like arc consistency (AC-3) that prune inconsistent values from domains to reduce the search space before backtracking search. The computational complexity of AC-3 is analyzed as O(DC^2) where D is the maximum domain size and C is the
Special webinar on tips for perfect score in sat mathCareerGOD
Math is the language of logic and is therefore tested in all the major examinations where SAT is no exception.
Scoring well in Math can do wonders to your career and college candidature. Conversely, any complacency in Math affect your score and thus prove dangerous.
In this webinar, “Tips for perfect Math score in SAT and SAT- Math subject test” from the 5-day webinar series ‘Experts’ Speak: Demystifying US Admissions’, seasoned math trainers and subject experts with decades of experience in the industry share important insights on maximising your Math scores and minimising mistakes to lose out on Math scores.
Visit www.careergod.com for more info.
The document provides an agenda and notes for a math class that includes:
- Warm-up exercises on fractions, absolute value, and algebra
- Upcoming assignments and tests on Khan Academy, absolute value, and a star math test
- Information on submitting past due work for partial credit
- Lessons on adding/subtracting fractions and an introduction to absolute value including the definition, using absolute value bars, and evaluating expressions with absolute value.
This document contains notes from an algebra class. It summarizes that:
1) All new algebra students are responsible for their own grades, including online and notebook assignments. Students should ask friends or the teacher for details.
2) For the previous quarter, every student who completed less than half of their classwork and online assignments averaged below 50% on tests. Completing assignments prepares students for tests, which make up a large part of the grade.
3) Upcoming topics include graphing systems of equations, systems of equations with elimination, and systems of equations with substitution.
The document provides a daily agenda and notes for a math class that includes:
- Khan Academy practice and an exponent test on Tuesday
- Reviewing class work on scientific notation and exponents
- Notes on problems involving scientific notation, exponents, and monomials including finding degrees, multiplying and dividing monomials, and writing expressions in scientific and standard form
- A reminder to show work when using a calculator and that copied answers are often incorrect
The document discusses how traditional math curriculums fail to teach students how to apply mathematical concepts to real-world contexts. It argues that the root cause is a lack of emphasis on contextual or applied math education. The solution proposed is for math courses to integrate real-life problems and applications to help students understand how mathematical tools can clarify practical situations.
The document discusses constraint satisfaction problems (CSPs). It defines a CSP as having variables with domains of possible values and constraints limiting the values variables can take. A solution assigns values to all variables while satisfying constraints. The document outlines backtracking search and constraint propagation techniques for solving CSPs, including variable and value ordering heuristics, forward checking, and arc consistency. Arc consistency is more effective than forward checking at detecting inconsistencies and pruning the search space. The document provides examples of CSP formulations for map coloring, Sudoku, and N-Queens problems.
"Yeah But How Do I Translate That to a Percentage?" -- STA Convention 2022.pdfChris Hunter
The document discusses standards-based assessment and answers common questions about the transition from traditional grading to standards-based assessment. Some key points include:
- Standards-based assessment focuses on demonstrating evidence of learning standards rather than accumulating points, and compares student learning to proficiency levels rather than other students.
- The reasons for changing include making assessment more accurate, fair, and relevant to learning, and shifting student focus from grades to learning.
- Assessment should evaluate specific delineated learning standards rather than broad topics. Descriptors define each level of the proficiency scale from emerging to extending.
- Evidence of learning can come from products, observations, and conversations, rather than single events like tests. Tracking data over
This document discusses constraint satisfaction problems (CSPs) and algorithms for solving them. It begins with an example scheduling CSP involving class scheduling with various constraints. It then defines the components of a CSP - variables, domains, and constraints. Backtracking search is presented as a basic algorithm for solving CSPs by systematically trying value assignments. The document notes that the variable and value ordering used in backtracking can affect both the runtime and the solution found. It then introduces constraint propagation techniques like arc consistency (AC-3) that prune inconsistent values from domains to reduce the search space before backtracking search. The computational complexity of AC-3 is analyzed as O(DC^2) where D is the maximum domain size and C is the
Special webinar on tips for perfect score in sat mathCareerGOD
Math is the language of logic and is therefore tested in all the major examinations where SAT is no exception.
Scoring well in Math can do wonders to your career and college candidature. Conversely, any complacency in Math affect your score and thus prove dangerous.
In this webinar, “Tips for perfect Math score in SAT and SAT- Math subject test” from the 5-day webinar series ‘Experts’ Speak: Demystifying US Admissions’, seasoned math trainers and subject experts with decades of experience in the industry share important insights on maximising your Math scores and minimising mistakes to lose out on Math scores.
Visit www.careergod.com for more info.
The document provides an agenda and notes for a math class that includes:
- Warm-up exercises on fractions, absolute value, and algebra
- Upcoming assignments and tests on Khan Academy, absolute value, and a star math test
- Information on submitting past due work for partial credit
- Lessons on adding/subtracting fractions and an introduction to absolute value including the definition, using absolute value bars, and evaluating expressions with absolute value.
This document provides information about standard-based assessment in mathematics. It discusses what standard-based assessment is, including that it measures student performance according to predetermined educational standards rather than comparing students. It also discusses the components of standard-based assessment, including learning standards, formative and summative assessment, and examples of assessment tools. Performance tasks are presented as an example of authentic assessment.
Linear regression is a statistical method used to analyze and understand the relationship between two or more variables. It predicts a numeric target variable based on one or more independent variables. Single linear regression uses one independent variable to predict the dependent variable based on a linear equation. The document provides examples of calculating linear regression coefficients and making predictions using the linear regression equation. It also discusses evaluating linear regression models using metrics like MAE, MSE, and RMSE.
Learning & Teaching GCSE MathematicsColleen Young
This document provides teaching resources and ideas for GCSE Mathematics. It includes information on specification changes, assessment objectives, teaching guidance from exam boards, and problem solving strategies. Sample exam questions, topic tests, and diagnostic questions are provided. Additional resources on areas like extension materials, revision activities, and developing recall are also referenced.
Reinforcement learning allows an agent to learn from interaction with an uncertain environment to achieve a goal. There are three main methods to solve reinforcement learning problems: dynamic programming which requires a complete model of the environment; Monte Carlo methods which learn from sample episodes without a model; and temporal-difference learning like Sarsa and Q-learning which combine ideas from dynamic programming and Monte Carlo to learn directly from experience in an online manner. Designing good state representations, features, and rewards is important for applying these methods to real-world problems.
This document discusses constraint satisfaction problems (CSPs). It defines CSPs as problems with variables that must satisfy constraints. CSPs can represent many real-world problems and are solved through constraint satisfaction methods. The document outlines CSP components like variables, domains, and constraints. It also describes representing problems as CSPs, solving CSPs through backtracking search, and the role of heuristics like minimum remaining values in improving the search process.
This document outlines the topics that will be covered on a math test for an academic physics course. It lists various grade 10 math concepts that students are expected to understand, such as the Pythagorean theorem, trigonometric ratios, solving linear and quadratic equations, graphing, slopes, and angles. The test will count towards students' term marks, so mastery of these prerequisite math skills is necessary to succeed in the grade 11 physics course. Students are advised to study by doing practice problems and reviewing old tests and textbook sections.
This document provides an introduction and overview for a course on machine learning. It outlines the course structure, assignments, and expectations. The course will cover topics including linear regression, classification, model selection, and dimensionality reduction. It will teach students how to analyze data, preprocess it, extract features, train models, and evaluate model performance. The goal is for students to understand core machine learning algorithms and concepts. Required materials include an introduction to statistical learning textbook.
This document provides a review for an upcoming math test covering several topics:
- Review of addition, subtraction, equations, and an introduction to complex fractions
- Sample problems are provided to review solving quadratic equations algebraically and graphically, as well as rational equations
- Instructions are given for tomorrow's test and how current grades will be calculated, with a proposal that students can pass the quarter by scoring a minimum of 75% on the remaining tests.
Relational algebra allows querying relational databases using a set of operators. Key operators include selection (σ) to filter tuples, projection (π) to select attributes, and join (×) to combine relations. More complex queries can be built by combining multiple operators. The division operator (/) is used to find tuples that have a relationship to all tuples in another relation. While not directly supported, division queries can be computed from other relational algebra operations and their complements.
This document provides information about an upcoming math test for an academic physics course, including the topics that will be covered and how students can study. The topics are concepts from grade 10 math, including the Pythagorean theorem, trigonometric ratios, solving linear and quadratic equations. Understanding these mathematical concepts is required for success in the physics course. This test will count towards students' term marks. Students are advised to review prior tests, practice problems, and know the required formulas in order to prepare.
The document summarizes analysis of math concepts tested on 4 released SAT math tests:
1) The concepts were grouped into categories from the SAT (Heart of Algebra, Problem Solving & Data Analysis, Passport to Advanced Math, Additional Topics).
2) The most commonly tested concepts were identified, with the top 11 making up 68% of questions. The top 18 concepts made up 88% of questions.
3) Heart of Algebra concepts accounted for 32% of questions, with Problem Solving & Data Analysis making up 25% and Passport to Advanced Math 28%.
4) The analysis can help students focus their study on the most likely concepts to improve their scores, depending on available
This document provides an overview of the topics that will be covered in a discrete mathematics course for computer science. It includes:
- Administrative details like the instructor's contact information, textbook, exam dates, and policies on cheating and late homework.
- The course objectives which are to learn basic discrete mathematics tools and techniques like propositional logic, set theory, counting, and induction, as well as rigorous mathematical reasoning and proof writing.
- Advice for students on how to study effectively and keep up in the course by practicing problems and examples.
- An outline of the topics to be covered, beginning with propositional logic - including truth tables, logical connectives, tautologies, and translating sentences -
1. The document discusses two methods for solving mathematical problems: Polya's method and the Singapore method.
2. Polya's method involves 4 steps: understanding the problem, designing a solution strategy, executing the strategy, and reviewing/answering. The Singapore method involves 8 steps like reading the problem and drawing a unit bar diagram.
3. An example problem is presented and solved using both methods. While the Singapore method is simpler, Polya's method allows for more in-depth analysis and understanding of problems at different difficulty levels.
mathematical notations and functions, algorithmic notations, control structures, complexity of algorithms, other asymptotic notations for complexity of algorithms, subalgorithms, variables, data types.
This document discusses theorem proving and its applications. It covers several topics:
1. Theorem proving can be useful for mathematics, hardware verification, safety-critical systems, automated planning, and cryptanalysis. Early implementations included proving evenness of sums.
2. Propositional logic is a simple but important logic that is decidable and can model many problems. Techniques for checking tautologies include truth tables, natural deduction, and converting to SAT problems solved by SAT solvers.
3. Modern SAT solvers use techniques like conflict-driven clause learning and non-chronological backtracking to efficiently handle large problems from applications like electronic design automation.
This document discusses theorem proving in propositional and first-order logic. It covers topics like:
- Propositional logic, including syntax, semantics, and techniques like truth tables, natural deduction, resolution, and DPLL algorithms.
- Converting propositional formulas to conjunctive normal form.
- Applications of theorem proving like hardware and software verification.
- First-order logic, including its syntax of terms, formulas, and semantics based on interpretations and variable assignments in a domain.
The document provides an overview of fundamental concepts and techniques in automated theorem proving at the propositional and first-order levels. It also mentions applications and references for further study.
Constraint Satisfaction Problems (CSPs) involve variables with domains of possible values and constraints specifying allowed value combinations. The document discusses CSP definitions and representations, examples like Sudoku and graph coloring, and solutions being complete consistent assignments. It also covers backtracking search for CSPs, heuristics like minimum remaining values and least constraining value, and forward checking for pruning inconsistent values earlier.
This document discusses methods for calculating the reliability of quantitative measures. It describes four types of reliability: split-half, test-retest, parallel forms, and inter-rater. Split-half reliability determines how consistently a measure assesses the intended construct by splitting a test in half. The Kuder-Richardson formula 20 (KR-20) is used to calculate split-half reliability for dichotomous items, while Cronbach's alpha or the Spearman-Brown formula are used for Likert scales. The document provides an example of calculating KR-20 by splitting a 10-item spelling test administered to 15 children into halves.
GRE is different for different people, sometimes your score may be less in your terms but maybe the right one for the university that you want. But, sometimes, your score might be really good for you and not so for the university. Just like any practical decision, here too, the choice boils down to the basics: what is the right thing to do? If your score is not good enough for the university, then go ahead retake the GRE.
Retaking or not retaking the GRE is a crucial decision, and hence please do consider it with utmost care and importance. It might be the case where the situation and the demands of your aspiration might propel you to take the test again.
To know more, please refer to this in-depth article:
https://www.manyagroup.com/blog/re-taking-the-gre-5-important-things-to-consider/
The Graduate Record Examinations (GRE), also called the GRE General Test is a standardized test that is taken by graduates, business schools, and law school applicants.
The test measures verbal reasoning, quantitative reasoning, critical thinking, and analytical writing skills that are not related to any specific field of study.
This e-book provides information on the GRE and the ways to get the best out of you as you prepare for the GRE.
Know more about Manya's GRE Test Prep Program - https://www.manyagroup.com/gre/
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This document provides information about standard-based assessment in mathematics. It discusses what standard-based assessment is, including that it measures student performance according to predetermined educational standards rather than comparing students. It also discusses the components of standard-based assessment, including learning standards, formative and summative assessment, and examples of assessment tools. Performance tasks are presented as an example of authentic assessment.
Linear regression is a statistical method used to analyze and understand the relationship between two or more variables. It predicts a numeric target variable based on one or more independent variables. Single linear regression uses one independent variable to predict the dependent variable based on a linear equation. The document provides examples of calculating linear regression coefficients and making predictions using the linear regression equation. It also discusses evaluating linear regression models using metrics like MAE, MSE, and RMSE.
Learning & Teaching GCSE MathematicsColleen Young
This document provides teaching resources and ideas for GCSE Mathematics. It includes information on specification changes, assessment objectives, teaching guidance from exam boards, and problem solving strategies. Sample exam questions, topic tests, and diagnostic questions are provided. Additional resources on areas like extension materials, revision activities, and developing recall are also referenced.
Reinforcement learning allows an agent to learn from interaction with an uncertain environment to achieve a goal. There are three main methods to solve reinforcement learning problems: dynamic programming which requires a complete model of the environment; Monte Carlo methods which learn from sample episodes without a model; and temporal-difference learning like Sarsa and Q-learning which combine ideas from dynamic programming and Monte Carlo to learn directly from experience in an online manner. Designing good state representations, features, and rewards is important for applying these methods to real-world problems.
This document discusses constraint satisfaction problems (CSPs). It defines CSPs as problems with variables that must satisfy constraints. CSPs can represent many real-world problems and are solved through constraint satisfaction methods. The document outlines CSP components like variables, domains, and constraints. It also describes representing problems as CSPs, solving CSPs through backtracking search, and the role of heuristics like minimum remaining values in improving the search process.
This document outlines the topics that will be covered on a math test for an academic physics course. It lists various grade 10 math concepts that students are expected to understand, such as the Pythagorean theorem, trigonometric ratios, solving linear and quadratic equations, graphing, slopes, and angles. The test will count towards students' term marks, so mastery of these prerequisite math skills is necessary to succeed in the grade 11 physics course. Students are advised to study by doing practice problems and reviewing old tests and textbook sections.
This document provides an introduction and overview for a course on machine learning. It outlines the course structure, assignments, and expectations. The course will cover topics including linear regression, classification, model selection, and dimensionality reduction. It will teach students how to analyze data, preprocess it, extract features, train models, and evaluate model performance. The goal is for students to understand core machine learning algorithms and concepts. Required materials include an introduction to statistical learning textbook.
This document provides a review for an upcoming math test covering several topics:
- Review of addition, subtraction, equations, and an introduction to complex fractions
- Sample problems are provided to review solving quadratic equations algebraically and graphically, as well as rational equations
- Instructions are given for tomorrow's test and how current grades will be calculated, with a proposal that students can pass the quarter by scoring a minimum of 75% on the remaining tests.
Relational algebra allows querying relational databases using a set of operators. Key operators include selection (σ) to filter tuples, projection (π) to select attributes, and join (×) to combine relations. More complex queries can be built by combining multiple operators. The division operator (/) is used to find tuples that have a relationship to all tuples in another relation. While not directly supported, division queries can be computed from other relational algebra operations and their complements.
This document provides information about an upcoming math test for an academic physics course, including the topics that will be covered and how students can study. The topics are concepts from grade 10 math, including the Pythagorean theorem, trigonometric ratios, solving linear and quadratic equations. Understanding these mathematical concepts is required for success in the physics course. This test will count towards students' term marks. Students are advised to review prior tests, practice problems, and know the required formulas in order to prepare.
The document summarizes analysis of math concepts tested on 4 released SAT math tests:
1) The concepts were grouped into categories from the SAT (Heart of Algebra, Problem Solving & Data Analysis, Passport to Advanced Math, Additional Topics).
2) The most commonly tested concepts were identified, with the top 11 making up 68% of questions. The top 18 concepts made up 88% of questions.
3) Heart of Algebra concepts accounted for 32% of questions, with Problem Solving & Data Analysis making up 25% and Passport to Advanced Math 28%.
4) The analysis can help students focus their study on the most likely concepts to improve their scores, depending on available
This document provides an overview of the topics that will be covered in a discrete mathematics course for computer science. It includes:
- Administrative details like the instructor's contact information, textbook, exam dates, and policies on cheating and late homework.
- The course objectives which are to learn basic discrete mathematics tools and techniques like propositional logic, set theory, counting, and induction, as well as rigorous mathematical reasoning and proof writing.
- Advice for students on how to study effectively and keep up in the course by practicing problems and examples.
- An outline of the topics to be covered, beginning with propositional logic - including truth tables, logical connectives, tautologies, and translating sentences -
1. The document discusses two methods for solving mathematical problems: Polya's method and the Singapore method.
2. Polya's method involves 4 steps: understanding the problem, designing a solution strategy, executing the strategy, and reviewing/answering. The Singapore method involves 8 steps like reading the problem and drawing a unit bar diagram.
3. An example problem is presented and solved using both methods. While the Singapore method is simpler, Polya's method allows for more in-depth analysis and understanding of problems at different difficulty levels.
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This document discusses theorem proving and its applications. It covers several topics:
1. Theorem proving can be useful for mathematics, hardware verification, safety-critical systems, automated planning, and cryptanalysis. Early implementations included proving evenness of sums.
2. Propositional logic is a simple but important logic that is decidable and can model many problems. Techniques for checking tautologies include truth tables, natural deduction, and converting to SAT problems solved by SAT solvers.
3. Modern SAT solvers use techniques like conflict-driven clause learning and non-chronological backtracking to efficiently handle large problems from applications like electronic design automation.
This document discusses theorem proving in propositional and first-order logic. It covers topics like:
- Propositional logic, including syntax, semantics, and techniques like truth tables, natural deduction, resolution, and DPLL algorithms.
- Converting propositional formulas to conjunctive normal form.
- Applications of theorem proving like hardware and software verification.
- First-order logic, including its syntax of terms, formulas, and semantics based on interpretations and variable assignments in a domain.
The document provides an overview of fundamental concepts and techniques in automated theorem proving at the propositional and first-order levels. It also mentions applications and references for further study.
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This document discusses methods for calculating the reliability of quantitative measures. It describes four types of reliability: split-half, test-retest, parallel forms, and inter-rater. Split-half reliability determines how consistently a measure assesses the intended construct by splitting a test in half. The Kuder-Richardson formula 20 (KR-20) is used to calculate split-half reliability for dichotomous items, while Cronbach's alpha or the Spearman-Brown formula are used for Likert scales. The document provides an example of calculating KR-20 by splitting a 10-item spelling test administered to 15 children into halves.
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GRE is different for different people, sometimes your score may be less in your terms but maybe the right one for the university that you want. But, sometimes, your score might be really good for you and not so for the university. Just like any practical decision, here too, the choice boils down to the basics: what is the right thing to do? If your score is not good enough for the university, then go ahead retake the GRE.
Retaking or not retaking the GRE is a crucial decision, and hence please do consider it with utmost care and importance. It might be the case where the situation and the demands of your aspiration might propel you to take the test again.
To know more, please refer to this in-depth article:
https://www.manyagroup.com/blog/re-taking-the-gre-5-important-things-to-consider/
The Graduate Record Examinations (GRE), also called the GRE General Test is a standardized test that is taken by graduates, business schools, and law school applicants.
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This e-book provides information on the GRE and the ways to get the best out of you as you prepare for the GRE.
Know more about Manya's GRE Test Prep Program - https://www.manyagroup.com/gre/
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2. ‘Saraswati before Lakshmi’
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• Before the test day
• At the testing center
• After the test
• The Experimental Section Conundrum
3. ‘Saraswati before Lakshmi’ 3
Quick Revision – Global strategies
Do the easy ones
first
Don’t leave anything
unansweredPacing
Eliminate the wrong
ones
4. ‘Saraswati before Lakshmi’ 4
Quick Revision – Quant - Strategies
Bite sized pieces
Do Estimation
Calculator Use
Read the question
carefully When you see variables, assign a value and
proceed
Question asks about changes to values but
don’t provide the actual values
Plug in your number for the actual value
Make use of the numbers given in the answer
choices
Word Problems –
Basic Approach
5. ‘Saraswati before Lakshmi’ 5
• The use of calculator is permitted only for ‘yes calculator’ section.
• All variables and expressions used represent real numbers unless otherwise
indicated.
• Figures provided in this test are drawn to scale unless otherwise indicated.
• All figures are in a plane unless otherwise indicated.
• Unless Otherwise indicated, the domain of a given function f is the set of all
real numbers x for which f(x) is a real number
Quick Revision – Quant – Points given on test booklet
7. ‘Saraswati before Lakshmi’ 7
Quant Revision – Quant – Formulas not given on the booklet
When a quadratic is in the form ax2 + bx + c = 0,
Sum of the roots = -b/a and Product of the roots = c/a
Quadratic formula:
Discriminant, D = b2 – 4ac,
If D is positive, the quadratic has two real roots.
If D equals zero, the quadratic has one real root.
If D is negative, the quadratic has no real roots.
Growth or Decay formula:
•When the growth is given in percent.
Final amount = original amount (1 rate)number of changes
•When the growth is given as multiple,
Final amount = original amount (multiplier)number of changes
Total = Average × Number of elements
Mean, Median, Mode, Range
Distance = Speed × Time
Work = Rate × Time
Probability
P(A or B) = P(A) + P(B) – P(common for A, B)
P(A and B) = P(A) × P(B)
8. ‘Saraswati before Lakshmi’ 8
Quick Revision – Quant – Formulas not given on the booklet
Slope =
• Parallel lines have the same slope.
• Perpendicular lines have negative
reciprocal slopes
Standard Form of a Linear Equation:
Ax + By = C
The slope of the line is –A/B
Quadratic Equation:
• Standard form is, y = ax2 + bx + c
• Factored form is, y = a(x – m)(x – n) where m and n are the
x-intercepts of the parabola.
• Vertex form is, y =a(x – h)2 + k, where (h, k) is the vertex of
the parabola.
Standard form of a circle is (x – h)2 + (y – k)2 = r2 , where
(h, k) is the center and r is the radius.
To convert radians to degrees,
sin x = cos (90 – x)
cos x = sin (90 – x)
9. ‘Saraswati before Lakshmi’ 9
Quick Revision – Quant
Grid Ins – Pacing Advice
No Calculator Section:
– Q1 to Q10 (MCQ – easy and medium)
– Q16 to Q20 (Grid Ins)
– Q11 to Q15 (MCQ – hard )
Yes Calculator Section:
– Q1 to Q20 (MCQ – easy and medium)
– Q31 to Q38 (Grid Ins)
– Q21 to Q30 (MCQ – hard )
10. ‘Saraswati before Lakshmi’ 10
Quick Revision – Reading
Reading Basic Approach:
Read the Blurb
Select and understand a question
Read what you need (create a window)
Predict an answer
Process of elimination
Important strategies to get a high Reading score:
Identifying the question type is really important
Always make a prediction, no matter how vague it
may seem
Work on the elimination process rigorously
Mark your windows, important words and
phrases, and question’s subject
Do not deviate from your prediction (trust your
gut)
11. ‘Saraswati before Lakshmi’ 11
Quick Revision – W & L
Go for proof reader questions first
In Proofreader questions, look at the answer choices for understanding the
question
SAT tests only the knowledge of written English
Rules of Punctuation, Rules of Transition, consistency with sentence with regard
to verb, pronouns
Precisions with pronoun, modifier, and parallel construction.
Rely on POE
12. ‘Saraswati before Lakshmi’ 12
Before the test day
Pack your bag:
Admission Ticket – printed from
Collegeboard site
Photo Id – Original Passport
#2 pencils – 5 Nos
Eraser
Calculator – with new batteries
Also,
Water
Snacks
Watch
Jacket
Plan how early to start, to reach on
time
Don’t take any practice test
Just revise the concepts and formula
Take it easy and relax
Sleep well
13. ‘Saraswati before Lakshmi’ 13
Morning of the SAT
Wake up, wake up fully
Stretch
Shower
Have a good breakfast
Start on time
Because it’s 4 hours long test...
14. ‘Saraswati before Lakshmi’ 14
At the test center
Proctors
Breaks
Timing
Pencils and calculators
Be prepared and
attentive
15. ‘Saraswati before Lakshmi’ 15
EXPERIMENTAL SECTIExperimental Section conundrum
Heads Up!!!
You may have a 5th section, an experimental section
This is a 20 minutes section, could be Reading or W&L or Math
You have to treat this section as equally as the other sections.
16. ‘Saraswati before Lakshmi’ 16
After the test
If needed, you can cancel your score
Send your scores to the universities
within 9 days of the test
Can avail services like,
Student Answer Service
Multiple Choice Score Verification
Essay score Verification
18. ‘Saraswati before Lakshmi’
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