ch04 - part 2 (Cont unifm dist).ppt explained
•Download as PPT, PDF•
0 likes•11 views
Good lecture and detailed
Report
Share
Report
Share
Recommended
Discrete and continuous probability distributions ppt @ bec doms
The document discusses various probability distributions including discrete and continuous distributions. It covers the binomial, hypergeometric, Poisson, and normal distributions. It provides the characteristics and formulas for each distribution and examples of how to calculate probabilities using the distributions.
Discrete probability distribution.pptx
This document provides an assignment on discrete probability distributions, including binomial, Poisson, and negative binomial distributions. It defines each distribution and provides examples of their properties and applications. It also includes numerical problems demonstrating how to fit data to each distribution and calculate relevant probabilities.
Normal Distribution
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
AppsDiff3c.pdf
This document provides guidance for teachers on applications of differentiation for Years 11 and 12. It covers key topics like graph sketching, maxima and minima problems, and related rates. For graph sketching, it discusses increasing and decreasing functions, stationary points, local maxima and minima, and uses the first derivative test to determine the nature of stationary points. Examples are provided to illustrate these concepts.
discrete and continuous probability distributions pptbecdoms-120223034321-php...
This document discusses various probability distributions including discrete and continuous distributions. It covers the binomial, hypergeometric, Poisson, and normal distributions. For each distribution, it provides the characteristics and formulas, and examples of how to calculate probabilities using the distributions and probability tables or software. It also illustrates how the parameters impact the shape of the distributions. The goal is to help readers apply different probability distributions to problems and compute probabilities.
Chapter 04 answers
This document summarizes solutions to odd-numbered homework problems from Chapter 4 of a statistics textbook. It covers topics like discrete vs. continuous random variables, probability distributions, the normal and binomial distributions, and how to calculate probabilities using the z-table. Examples include determining the type of random variable, finding probabilities of intervals for different distributions, and approximating binomial probabilities with the normal distribution for large n.
Normal as Approximation to Binomial
The document discusses approximating binomial probabilities with a normal distribution. It defines the binomial distribution and states the requirements for the normal approximation are that np and nq must both be greater than or equal to 5. The normal approximation involves using a normal distribution with mean np and standard deviation npq. Examples are provided demonstrating how to calculate probabilities for binomial experiments using the normal approximation.
Recommended
Discrete and continuous probability distributions ppt @ bec doms
The document discusses various probability distributions including discrete and continuous distributions. It covers the binomial, hypergeometric, Poisson, and normal distributions. It provides the characteristics and formulas for each distribution and examples of how to calculate probabilities using the distributions.
Discrete probability distribution.pptx
This document provides an assignment on discrete probability distributions, including binomial, Poisson, and negative binomial distributions. It defines each distribution and provides examples of their properties and applications. It also includes numerical problems demonstrating how to fit data to each distribution and calculate relevant probabilities.
Normal Distribution
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
AppsDiff3c.pdf
This document provides guidance for teachers on applications of differentiation for Years 11 and 12. It covers key topics like graph sketching, maxima and minima problems, and related rates. For graph sketching, it discusses increasing and decreasing functions, stationary points, local maxima and minima, and uses the first derivative test to determine the nature of stationary points. Examples are provided to illustrate these concepts.
discrete and continuous probability distributions pptbecdoms-120223034321-php...
This document discusses various probability distributions including discrete and continuous distributions. It covers the binomial, hypergeometric, Poisson, and normal distributions. For each distribution, it provides the characteristics and formulas, and examples of how to calculate probabilities using the distributions and probability tables or software. It also illustrates how the parameters impact the shape of the distributions. The goal is to help readers apply different probability distributions to problems and compute probabilities.
Chapter 04 answers
This document summarizes solutions to odd-numbered homework problems from Chapter 4 of a statistics textbook. It covers topics like discrete vs. continuous random variables, probability distributions, the normal and binomial distributions, and how to calculate probabilities using the z-table. Examples include determining the type of random variable, finding probabilities of intervals for different distributions, and approximating binomial probabilities with the normal distribution for large n.
Normal as Approximation to Binomial
The document discusses approximating binomial probabilities with a normal distribution. It defines the binomial distribution and states the requirements for the normal approximation are that np and nq must both be greater than or equal to 5. The normal approximation involves using a normal distribution with mean np and standard deviation npq. Examples are provided demonstrating how to calculate probabilities for binomial experiments using the normal approximation.
Statistics lecture 6 (ch5)
This document discusses probability distributions and some key concepts:
1. It describes discrete and continuous random variables and examples like the binomial, Poisson, and normal distributions.
2. For discrete random variables, it explains how to calculate probabilities, mean, and standard deviation from a probability distribution table.
3. An example is provided to demonstrate calculating these values from data on the number of vehicles owned by households.
4. It also introduces continuous random variables and density functions, noting that the probability of any single value is zero due to the infinite number of possible outcomes. The area under the density function curve represents probabilities.
lecture6.ppt
The document provides information about the normal distribution and standard normal distribution. It discusses key properties of the normal distribution including that it is defined by its mean and standard deviation. It also describes the 68-95-99.7 rule for how much of the data falls within 1, 2, and 3 standard deviations of the mean in a normal distribution. The document then introduces the standard normal distribution and how it allows converting any normal distribution to a standard scale for looking up probabilities. It provides examples of calculating probabilities and finding values corresponding to percentiles for both raw and standard normal distributions. Finally, it discusses checking if data are approximately normally distributed.
continuous probability distributions.ppt
The document provides information about the normal distribution and standard normal distribution:
- The normal distribution is defined by its mean (μ) and standard deviation (σ). Changing μ shifts the distribution left or right, while changing σ increases or decreases the spread.
- All normal distributions can be converted to the standard normal distribution (with μ=0 and σ=1) by subtracting the mean and dividing by the standard deviation.
- The standard normal distribution is useful because probability tables and computer programs provide the integral values, avoiding the need to calculate integrals manually.
- For a normal distribution, approximately 68% of the data falls within 1 standard deviation of the mean, 95% falls
Probability-1.pptx
This document provides an overview of key concepts in probability and probability distributions. It introduces random variables and their probability distributions, and covers discrete and continuous random variables. Specific probability distributions discussed include the binomial, Poisson, and normal distributions. Expected value and variance are defined as measures of the central tendency and variability of random variables. Examples are provided to illustrate calculating probabilities and parameters for different probability distributions.
5.5 Zeros of Polynomial Functions
* Evaluate a polynomial using the Remainder Theorem.
* Use the Factor Theorem to solve a polynomial equation.
* Use the Rational Zero Theorem to find rational zeros.
* Find zeros of a polynomial function.
* Use the Linear Factorization Theorem to find polynomials with given zeros.
* Use Descartes’ Rule of Signs.
Lect05 BT1211.pdf
This document provides an overview of continuous probability distributions covered in Lecture 5, including:
- Continuous random variables can take on uncountably infinite values within an interval, unlike discrete variables. Probability density functions (PDFs) are used instead of probabilities.
- The uniform, normal, and exponential distributions are introduced as examples of continuous distributions. Key properties like expected value and variance are discussed.
- The standard normal distribution is especially important, and its probabilities are provided in tables. Examples show how to calculate probabilities for normal distributions using the tables.
04 random-variables-probability-distributionsrv
The document discusses probability distributions, which model the probabilities of outcomes in random phenomena. It covers:
- Discrete and continuous probability distributions, which model outcomes that are discrete or continuous.
- Key properties of distributions like the mean, variance, and standard deviation, which describe the central tendency and variation of outcomes.
- Specific discrete distributions like the binomial and Poisson, which model counts of successes/failures and rare events.
- The normal/Gaussian distribution as the most common continuous distribution, fully described by its mean and variance.
- Standardizing normal variables to the standard normal distribution with mean 0 and variance 1 for easier probability calculations.
Statistical computing2
Statistical Computing
This document discusses various probability distributions that are important in data analytics. It begins by defining a probability distribution and giving examples of discrete probability distributions like the binomial distribution. It then discusses properties of discrete and continuous probability distributions. The document also covers specific continuous distributions like the normal, uniform, and Poisson distributions. It provides examples of calculating probabilities and distribution parameters for each type of distribution. In summary, the document presents an overview of key probability distributions and their applications in data analytics and statistics.
Input analysis
The document discusses various methods for modeling input distributions in simulation models, including trace-driven simulation, empirical distributions, and fitting theoretical distributions to real data. It provides examples of several continuous and discrete probability distributions commonly used in simulation, including the exponential, normal, gamma, Weibull, binomial, and Poisson distributions. Key parameters and properties of each distribution are defined. Methods for selecting an appropriate input distribution based on summary statistics of real data are also presented.
03 optimization
The document discusses optimization techniques for finding the minimum or maximum of a function. It begins by distinguishing optimization from root location, noting that optimization involves finding extrema rather than zeros. Several one-dimensional optimization methods are then described, including golden section search, parabolic interpolation, and Newton's method. The document notes that multidimensional optimization poses additional challenges and describes some direct methods that do not require derivatives, such as random search, for tackling multidimensional problems.
Statistik 1 6 distribusi probabilitas normal
This document discusses the key characteristics and concepts of the normal probability distribution. It outlines six goals related to understanding the normal distribution, its properties, calculating z-values, and using the normal distribution to approximate the binomial probability distribution. The key points covered include defining the mean, standard deviation, and shape of the normal curve; transforming variables to the standard normal distribution; and determining probabilities based on the areas under the normal curve.
Advanced matlab codigos matematicos
This document discusses various ways to work with polynomials in MATLAB. It covers representing polynomials as vectors, evaluating polynomials, finding roots, computing coefficients from roots, adding, subtracting, multiplying, dividing, taking derivatives and integrals of polynomials, fitting curves to data using polynomial regression, and interpolating values between data points using polynomials.
Week7 Quiz Help 2009[1]
The document provides information and examples to study for the week's quiz on normal distributions and the central limit theorem. Key points to remember include: the total area under the standard normal curve is 1; the mean and variance of any normal distribution do not depend on sample size; and the central limit theorem states that as sample size increases, the sampling distribution of the mean approaches a normal distribution, regardless of the original data's distribution. Examples are provided to practice calculating probabilities and confidence intervals using the normal distribution.
슬로우캠퍼스: scikit-learn & 머신러닝 (강박사)
This document discusses the normal distribution and its key properties. It also discusses sampling distributions and the central limit theorem. Some key points:
- The normal distribution is bell-shaped and symmetric. It is defined by its mean and standard deviation. Approximately 68% of values fall within 1 standard deviation of the mean.
- Sample statistics like the sample mean follow sampling distributions. When samples are large and random, the sampling distributions are often normally distributed according to the central limit theorem.
- Correlation and regression analyze the relationship between two variables. Correlation measures the strength and direction of association, while regression finds the best-fitting linear relationship to predict one variable from the other.
Chapter 5.pptx
This document provides information about binomial and Poisson distributions. It includes examples of calculating probabilities for binomial distributions using the binomial probability formula and binomial tables. It also provides the key characteristics and formula for the Poisson distribution. The mean, variance and standard deviation are defined for binomial distributions. Examples are provided to demonstrate calculating these values.
DirectionsUse what you have learned in this course to answer th.docx
Directions:
Use what you have learned in this course to answer the following questions. Justify your responses completely. Each question is worth 5 points.
1.
Solve for
n
:
–6(
n
– 8) = 4(12 – 5
n
) + 14
n
.
2
. For
f(
x
) = 2|
x
+3| – 5
, name the type of function and describe each of the three transformations from the parent function
f(
x
) = |
x
|
.
3.
Determine whether
f
(
x
) = –5 – 10
x
+ 6
has a maximum or a minimum value. Find that value and explain how you know.
4.
The median weekly earnings for American workers in 1990 was $412 and in 1999 it was $549. Calculate the average rate of change between 1990 and 1999.
5.
Find the roots of the parabola given by the following equation.
2
x
2+ 5
x
- 9 = 2
x
6.
Describe the end behavior and determine whether the graph represents an odd-degree or an even-degree polynomial function. Then state the number of real zeros.
7.
GEOMETRY
Recall the formula for finding the area of a rectangle. Define a variable for the width and set up an equation to find the dimensions of a rectangle that has an area 144 square inches, given that the length is 10 inches longer than its width.
DIMENSIONS:
Length: Width:
8.
The amount
f
(
t
) of a certain medicine, in milligrams, in a patient’s bloodstream
t
minutes after being taken is given by
f
(
t
) =
.
Find the amount of medicine in the blood after 20 minutes.
9.
Graph
f(
x
) =
x
2 + 2
x
- 3
, label the function’s x-intercepts,
y
-intercept and vertex with their coordinates. Also draw in and label the axis of symmetry.
Image result for x y axis
10.
Determine whether the relation shown is a function. Explain how you know.
73-1.jpg
11.
Solve the inequality and graph the solution on a number line.
–3(5
y
– 4) ≥ 17
12.
Assume that the wooden triangle shown is a right triangle.
a. Write an equation using the Pythagorean Theorem and the measurements provided in the diagram.
Hint: (leg 1)2 + (leg 2)2 = (hypotenuse)2
b. Transform each side of the equation to determine if it is an identity.
13.
Use long division or synthetic division to find the quotient of .
14.
Simplify
(9 + 8 – 6)(4 – 5)
.
15.
Find the inverse of
h(
x
) = .
16.
If
f(
x
) = 2
x
– 1
and
g(
x
) = – 2
, find
[g
◦ f](
x
).
17.
Graph the function
y
= – 2
. Then state the domain and range of the function.
Domain:
Range:
18.
If
f(
x
) = 3
x
2 – 2
and
g(
x
) = 4x + 2
, what is the value of
f
+ g 2
?
The price of a sweatshirt at a local shop is twice the price of a pair of shorts. The price of a T-shirt at the shop is $4 less than the price of a pair of shorts. Brad purchased 3 sweatshirts, 2 pairs of shorts, and 5 T-shirts for a total cost of $136.
19.
Let
w
represent the price of one sweatshirt,
t
represent the price of one T-shirt, and
h
represent the price of one pair of shorts. Write a system of three equations that represents the prices of the clothing.
20.
Solve the system. Find the cost of eac.
Normal as Approximation to Binomial
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 6: Normal Probability Distribution
6.6: Normal as Approximation to Binomial
Matlab polynimials and curve fitting
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
Binomial probability distributions
The document provides information about binomial probability distributions including:
- Binomial experiments have a fixed number (n) of independent trials with two possible outcomes and a constant probability (p) of success.
- The binomial probability distribution gives the probability of getting exactly x successes in n trials. It is calculated using the binomial coefficient and p and q=1-p.
- The mean, variance and standard deviation of a binomial distribution are np, npq, and √npq respectively.
- Examples demonstrate calculating probabilities of outcomes for binomial experiments and determining if results are significantly low or high using the range rule of μ ± 2σ.
Discrete Probability Distributions
This document discusses several discrete probability distributions:
1. Binomial distribution - For experiments with a fixed number of trials, two possible outcomes, and constant probability of success. The probability of x successes is given by the binomial formula.
2. Geometric distribution - For experiments repeated until the first success. The probability of the first success on the xth trial is p(1-p)^(x-1).
3. Poisson distribution - For counting the number of rare, independent events occurring in an interval. The probability of x events is (e^-μ μ^x)/x!, where μ is the mean number of events.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
More Related Content
Similar to ch04 - part 2 (Cont unifm dist).ppt explained
Statistics lecture 6 (ch5)
This document discusses probability distributions and some key concepts:
1. It describes discrete and continuous random variables and examples like the binomial, Poisson, and normal distributions.
2. For discrete random variables, it explains how to calculate probabilities, mean, and standard deviation from a probability distribution table.
3. An example is provided to demonstrate calculating these values from data on the number of vehicles owned by households.
4. It also introduces continuous random variables and density functions, noting that the probability of any single value is zero due to the infinite number of possible outcomes. The area under the density function curve represents probabilities.
lecture6.ppt
The document provides information about the normal distribution and standard normal distribution. It discusses key properties of the normal distribution including that it is defined by its mean and standard deviation. It also describes the 68-95-99.7 rule for how much of the data falls within 1, 2, and 3 standard deviations of the mean in a normal distribution. The document then introduces the standard normal distribution and how it allows converting any normal distribution to a standard scale for looking up probabilities. It provides examples of calculating probabilities and finding values corresponding to percentiles for both raw and standard normal distributions. Finally, it discusses checking if data are approximately normally distributed.
continuous probability distributions.ppt
The document provides information about the normal distribution and standard normal distribution:
- The normal distribution is defined by its mean (μ) and standard deviation (σ). Changing μ shifts the distribution left or right, while changing σ increases or decreases the spread.
- All normal distributions can be converted to the standard normal distribution (with μ=0 and σ=1) by subtracting the mean and dividing by the standard deviation.
- The standard normal distribution is useful because probability tables and computer programs provide the integral values, avoiding the need to calculate integrals manually.
- For a normal distribution, approximately 68% of the data falls within 1 standard deviation of the mean, 95% falls
Probability-1.pptx
This document provides an overview of key concepts in probability and probability distributions. It introduces random variables and their probability distributions, and covers discrete and continuous random variables. Specific probability distributions discussed include the binomial, Poisson, and normal distributions. Expected value and variance are defined as measures of the central tendency and variability of random variables. Examples are provided to illustrate calculating probabilities and parameters for different probability distributions.
5.5 Zeros of Polynomial Functions
* Evaluate a polynomial using the Remainder Theorem.
* Use the Factor Theorem to solve a polynomial equation.
* Use the Rational Zero Theorem to find rational zeros.
* Find zeros of a polynomial function.
* Use the Linear Factorization Theorem to find polynomials with given zeros.
* Use Descartes’ Rule of Signs.
Lect05 BT1211.pdf
This document provides an overview of continuous probability distributions covered in Lecture 5, including:
- Continuous random variables can take on uncountably infinite values within an interval, unlike discrete variables. Probability density functions (PDFs) are used instead of probabilities.
- The uniform, normal, and exponential distributions are introduced as examples of continuous distributions. Key properties like expected value and variance are discussed.
- The standard normal distribution is especially important, and its probabilities are provided in tables. Examples show how to calculate probabilities for normal distributions using the tables.
04 random-variables-probability-distributionsrv
The document discusses probability distributions, which model the probabilities of outcomes in random phenomena. It covers:
- Discrete and continuous probability distributions, which model outcomes that are discrete or continuous.
- Key properties of distributions like the mean, variance, and standard deviation, which describe the central tendency and variation of outcomes.
- Specific discrete distributions like the binomial and Poisson, which model counts of successes/failures and rare events.
- The normal/Gaussian distribution as the most common continuous distribution, fully described by its mean and variance.
- Standardizing normal variables to the standard normal distribution with mean 0 and variance 1 for easier probability calculations.
Statistical computing2
Statistical Computing
This document discusses various probability distributions that are important in data analytics. It begins by defining a probability distribution and giving examples of discrete probability distributions like the binomial distribution. It then discusses properties of discrete and continuous probability distributions. The document also covers specific continuous distributions like the normal, uniform, and Poisson distributions. It provides examples of calculating probabilities and distribution parameters for each type of distribution. In summary, the document presents an overview of key probability distributions and their applications in data analytics and statistics.
Input analysis
The document discusses various methods for modeling input distributions in simulation models, including trace-driven simulation, empirical distributions, and fitting theoretical distributions to real data. It provides examples of several continuous and discrete probability distributions commonly used in simulation, including the exponential, normal, gamma, Weibull, binomial, and Poisson distributions. Key parameters and properties of each distribution are defined. Methods for selecting an appropriate input distribution based on summary statistics of real data are also presented.
03 optimization
The document discusses optimization techniques for finding the minimum or maximum of a function. It begins by distinguishing optimization from root location, noting that optimization involves finding extrema rather than zeros. Several one-dimensional optimization methods are then described, including golden section search, parabolic interpolation, and Newton's method. The document notes that multidimensional optimization poses additional challenges and describes some direct methods that do not require derivatives, such as random search, for tackling multidimensional problems.
Statistik 1 6 distribusi probabilitas normal
This document discusses the key characteristics and concepts of the normal probability distribution. It outlines six goals related to understanding the normal distribution, its properties, calculating z-values, and using the normal distribution to approximate the binomial probability distribution. The key points covered include defining the mean, standard deviation, and shape of the normal curve; transforming variables to the standard normal distribution; and determining probabilities based on the areas under the normal curve.
Advanced matlab codigos matematicos
This document discusses various ways to work with polynomials in MATLAB. It covers representing polynomials as vectors, evaluating polynomials, finding roots, computing coefficients from roots, adding, subtracting, multiplying, dividing, taking derivatives and integrals of polynomials, fitting curves to data using polynomial regression, and interpolating values between data points using polynomials.
Week7 Quiz Help 2009[1]
The document provides information and examples to study for the week's quiz on normal distributions and the central limit theorem. Key points to remember include: the total area under the standard normal curve is 1; the mean and variance of any normal distribution do not depend on sample size; and the central limit theorem states that as sample size increases, the sampling distribution of the mean approaches a normal distribution, regardless of the original data's distribution. Examples are provided to practice calculating probabilities and confidence intervals using the normal distribution.
슬로우캠퍼스: scikit-learn & 머신러닝 (강박사)
This document discusses the normal distribution and its key properties. It also discusses sampling distributions and the central limit theorem. Some key points:
- The normal distribution is bell-shaped and symmetric. It is defined by its mean and standard deviation. Approximately 68% of values fall within 1 standard deviation of the mean.
- Sample statistics like the sample mean follow sampling distributions. When samples are large and random, the sampling distributions are often normally distributed according to the central limit theorem.
- Correlation and regression analyze the relationship between two variables. Correlation measures the strength and direction of association, while regression finds the best-fitting linear relationship to predict one variable from the other.
Chapter 5.pptx
This document provides information about binomial and Poisson distributions. It includes examples of calculating probabilities for binomial distributions using the binomial probability formula and binomial tables. It also provides the key characteristics and formula for the Poisson distribution. The mean, variance and standard deviation are defined for binomial distributions. Examples are provided to demonstrate calculating these values.
DirectionsUse what you have learned in this course to answer th.docx
Directions:
Use what you have learned in this course to answer the following questions. Justify your responses completely. Each question is worth 5 points.
1.
Solve for
n
:
–6(
n
– 8) = 4(12 – 5
n
) + 14
n
.
2
. For
f(
x
) = 2|
x
+3| – 5
, name the type of function and describe each of the three transformations from the parent function
f(
x
) = |
x
|
.
3.
Determine whether
f
(
x
) = –5 – 10
x
+ 6
has a maximum or a minimum value. Find that value and explain how you know.
4.
The median weekly earnings for American workers in 1990 was $412 and in 1999 it was $549. Calculate the average rate of change between 1990 and 1999.
5.
Find the roots of the parabola given by the following equation.
2
x
2+ 5
x
- 9 = 2
x
6.
Describe the end behavior and determine whether the graph represents an odd-degree or an even-degree polynomial function. Then state the number of real zeros.
7.
GEOMETRY
Recall the formula for finding the area of a rectangle. Define a variable for the width and set up an equation to find the dimensions of a rectangle that has an area 144 square inches, given that the length is 10 inches longer than its width.
DIMENSIONS:
Length: Width:
8.
The amount
f
(
t
) of a certain medicine, in milligrams, in a patient’s bloodstream
t
minutes after being taken is given by
f
(
t
) =
.
Find the amount of medicine in the blood after 20 minutes.
9.
Graph
f(
x
) =
x
2 + 2
x
- 3
, label the function’s x-intercepts,
y
-intercept and vertex with their coordinates. Also draw in and label the axis of symmetry.
Image result for x y axis
10.
Determine whether the relation shown is a function. Explain how you know.
73-1.jpg
11.
Solve the inequality and graph the solution on a number line.
–3(5
y
– 4) ≥ 17
12.
Assume that the wooden triangle shown is a right triangle.
a. Write an equation using the Pythagorean Theorem and the measurements provided in the diagram.
Hint: (leg 1)2 + (leg 2)2 = (hypotenuse)2
b. Transform each side of the equation to determine if it is an identity.
13.
Use long division or synthetic division to find the quotient of .
14.
Simplify
(9 + 8 – 6)(4 – 5)
.
15.
Find the inverse of
h(
x
) = .
16.
If
f(
x
) = 2
x
– 1
and
g(
x
) = – 2
, find
[g
◦ f](
x
).
17.
Graph the function
y
= – 2
. Then state the domain and range of the function.
Domain:
Range:
18.
If
f(
x
) = 3
x
2 – 2
and
g(
x
) = 4x + 2
, what is the value of
f
+ g 2
?
The price of a sweatshirt at a local shop is twice the price of a pair of shorts. The price of a T-shirt at the shop is $4 less than the price of a pair of shorts. Brad purchased 3 sweatshirts, 2 pairs of shorts, and 5 T-shirts for a total cost of $136.
19.
Let
w
represent the price of one sweatshirt,
t
represent the price of one T-shirt, and
h
represent the price of one pair of shorts. Write a system of three equations that represents the prices of the clothing.
20.
Solve the system. Find the cost of eac.
Normal as Approximation to Binomial
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 6: Normal Probability Distribution
6.6: Normal as Approximation to Binomial
Matlab polynimials and curve fitting
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
- Using polyfit for polynomial curve fitting to minimize squared errors between a polynomial and data set.
- An example of fitting increasing degree polynomials from 2 to 8 to cosine wave data, showing better fitting with higher degrees.
Binomial probability distributions
The document provides information about binomial probability distributions including:
- Binomial experiments have a fixed number (n) of independent trials with two possible outcomes and a constant probability (p) of success.
- The binomial probability distribution gives the probability of getting exactly x successes in n trials. It is calculated using the binomial coefficient and p and q=1-p.
- The mean, variance and standard deviation of a binomial distribution are np, npq, and √npq respectively.
- Examples demonstrate calculating probabilities of outcomes for binomial experiments and determining if results are significantly low or high using the range rule of μ ± 2σ.
Discrete Probability Distributions
This document discusses several discrete probability distributions:
1. Binomial distribution - For experiments with a fixed number of trials, two possible outcomes, and constant probability of success. The probability of x successes is given by the binomial formula.
2. Geometric distribution - For experiments repeated until the first success. The probability of the first success on the xth trial is p(1-p)^(x-1).
3. Poisson distribution - For counting the number of rare, independent events occurring in an interval. The probability of x events is (e^-μ μ^x)/x!, where μ is the mean number of events.
Similar to ch04 - part 2 (Cont unifm dist).ppt explained (20)
DirectionsUse what you have learned in this course to answer th.docx
DirectionsUse what you have learned in this course to answer th.docx
Recently uploaded
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
CalArts毕业证学历书【微信95270640】CalArts毕业证’圣力嘉学院毕业证《Q微信95270640》办理CalArts毕业证√文凭学历制作{CalArts文凭}购买学历学位证书本科硕士,CalArts毕业证学历学位证【实体公司】办毕业证、成绩单、学历认证、学位证、文凭认证、办留信网认证、（网上可查，实体公司，专业可靠）
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理加利福尼亚艺术学院加利福尼亚艺术学院毕业证文凭证书流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！子小伍玩小伍比山娃小一岁虎头虎脑的很霸气父亲让山娃跟小伍去夏令营听课山娃很高兴夏令营就设在附近一所小学山娃发现那所小学比自己的学校更大更美操场上还铺有塑胶跑道呢里面很多小朋友一班一班的快快乐乐原来城里娃都藏这儿来了怪不得平时见不到他们山娃恍然大悟起来吹拉弹唱琴棋书画山娃都不懂却什么都想学山娃怨自己太笨什么都不会斟酌再三山娃终于选定了学美术当听说每月要交元时父亲犹豫了山娃也说爸算了吧咱学校一学期才转
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION
AND CLASSIFICATION USING
ARTIFICIAL INTELLIGENCE
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Comparative analysis between traditional aquaponics and reconstructed aquapon...
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Slides for the 4th Presentation on LLM Fine-Tuning with QLoRA Presented by Anant, featuring DataStax Astra
Recently uploaded (20)
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Engineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
ch04 - part 2 (Cont unifm dist).ppt explained
- 1. Continuous Probability Distribution * Uniform Distribution * Normal Distribution
- 2. Definition 4-5 Continuous Uniform Distribution - Discrete uniform distribution, f(x) = 1/n
- 3. Figure 4-8 Continuous uniform probability density function. 4-5 Continuous Uniform Distribution Discrete uni. dis
- 4. Mean and Variance 4-5 Continuous Uniform Distribution
- 5. Example 4-5 Continuous Uniform Distribution To know that it is unifm. dist f(x)=1/ (b-a) =1/ (20-0) =0.05
- 6. Figure 4-9 Probability for Example 4-9. 4-5 Continuous Uniform Distribution
- 7. 4-5 Continuous Uniform Distribution
- 8. Text Book Problem • 441 a. F(X) = (x-0.9)/0.15 b. P(X>1.02) = 0.2 c. Here you have to identify the value of x such that the function will equate to 0.9 or 90% i.e., P(X>x)=0.9 x=0.915 d. E(X)=0.976; V(X)=0.00188
- 9. Text Book Problem • 446 (Please note that there is a mistake in the problem. Change 746 with 374 and 752 with 380) a. E(X) = 377; s(X)=1.732 b. P(X<375) = 0.1667 c. P(X>x)=0.95; x=374.3 d. Mean extra cost = 0.004/ container (Hint: mean extra cost = cost* mean extra volume)
- 11. • ‘Bell Shaped’ • Symmetrical • Mean, Median and Mode are Equal Location is determined by the mean, μ Spread is determined by the standard deviation, σ The random variable has an infinite theoretical range: + to X f(X) μ σ 4-6 Normal Distribution
- 13. 4-6 Normal Distribution Figure 4-10 Normal probability density functions for selected values of the parameters and s2.
- 14. f(X) X μ μ+1σ μ-1σ σ σ 68.26% x μ 2σ 2σ x μ 3σ 3σ 95.44% 99.73%
- 15. 4-6 Normal Distribution Some useful results concerning the normal distribution
- 16. 4-6 Normal Distribution Definition : Standard Normal z = (x-)/s
- 17. 4-6 Normal Distribution Example 4-11 Figure 4-13 Standard normal probability density function. Normal distribution table is in Page 718 and Page 719 P(Z<=1.5)=0.93319 P(Z<=1.53)=0.93699
- 20. Text-book problem 4.49 a. P(Z<1.32) =0.9066 c. P(Z>1.45) = 1-P(Z<1.45) (Because the table gives the lower value of Z i.e., P(Z<z) = 0.0735 d. P(Z>-2.15) = 1- 0.0158=0.9842 e. P(-2.34<Z<1.76) ---- see in the next slide 1.45 1 = 100% Non-shaded area = 1-shaded area
- 21. f(X) X 1.76 -2.34 a = P(Z<1.76) P(-2.34<Z<1.76) b = P(Z<-2.34) P(-2.34<Z<1.76) = P(Z<1.76)-P(Z<-2.34) = 0.9511
- 22. Thanks a lot for listening. Remaining portion we will discuss in the next class