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Chapter 6: Normal Probability Distribution
6.5: Assessing Normality
Solution to the practice test ch 10 correlation reg ch 11 gof ch12 anovaLong Beach City College
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Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
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Elementary Statistics Practice Test 2 Solutions
Chapter 4: Probability
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Chapter 11: Goodness-of-Fit and Contingency Tables
11.1: Goodness of Fit Notation
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Chapter 10: Correlation and Regression
10.1: Correlation
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Elementary Statistics Practice Test 2
Chapter 4: Probability
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Chapter 10: Correlation and Regression
10.2: Regression
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Chapter 5: Discrete Probability Distribution
5.2 - Binomial Probability Distributions
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Chapter 6: Normal Probability Distribution
6.3: Sampling Distributions and Estimators
Solution to the practice test ch 10 correlation reg ch 11 gof ch12 anovaLong Beach City College
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Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
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Elementary Statistics Practice Test 2 Solutions
Chapter 4: Probability
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Chapter 11: Goodness-of-Fit and Contingency Tables
11.1: Goodness of Fit Notation
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Chapter 10: Correlation and Regression
10.1: Correlation
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Elementary Statistics Practice Test 2
Chapter 4: Probability
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Chapter 10: Correlation and Regression
10.2: Regression
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Chapter 5: Discrete Probability Distribution
5.2 - Binomial Probability Distributions
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Chapter 6: Normal Probability Distribution
6.3: Sampling Distributions and Estimators
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Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
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Chapter 6: Normal Probability Distribution
6.1: The Standard Normal Distribution
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Chapter 2: Exploring Data with Tables and Graphs
2.3: Graphs that Enlighten and Graphs that Deceive
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Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
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Chapter 5: Discrete Probability Distribution
5.1: Probability Distribution
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Chapter 8: Hypothesis Testing
8.2: Testing a Claim About a Proportion
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Chapter 2: Exploring Data with Tables and Graphs
2.1: Frequency Distributions for Organizing and Summarizing Data
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Chapter 5: Discrete Probability Distribution
5.3 - Poisson Probability Distributions
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Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
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Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
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Chapter 1: Introduction to Statistics
Section 1.3: Collecting Sample Data
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Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
Solution to the Practice Test 3A, Chapter 6 Normal Probability DistributionLong Beach City College
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Elementary Statistics Practice Test 3
Practice Test Chapter 6 (Normal Probability Distributions)
Chapter 6: Normal Probability Distributions
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Chapter 7: Estimating Parameters and Determining Sample Sizes
7.1: Estimating a Population Proportion
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Chapter 8: Hypothesis Testing
8.4: Testing a Claim About a Standard Deviation or Variance
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Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
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Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
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Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
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Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
PAGE 1 Chapter 5 Normal Probability Distributions .docxgerardkortney
PAGE 1
Chapter 5: Normal Probability Distributions
Section 5.1: Intro to Normal Distributions and the Standard Normal Distributions
Objectives:
Normal Distribution Properties
Use z-scores to Calculate Area Under the Standard Normal Curve (using StatCrunch or Calculator)
Discuss Unusual Values
In this section we will revisit histograms which can be estimated with normal (symmetric, bell-shaped) curves. From
Test 1 remember that normal curves have z-scores (for any data value) and areas under the curve (one way: Empirical
Rule). Now we will use these normal curves to find probabilities (areas) and z-scores for any data value. Why do we
need to study this? Eventually we will use these probabilities and z-scores to make decisions.
By using the normal distribution curve, we are treating the data as a continuous random variable that has its own
continuous probability distribution. (Remember that any probability distribution has two properties: all probabilities
are between 0 and 1 and the sum of the probabilities is 1.) **Probabilities = Areas under the curve**
Ex: Consider the normal distribution curves below. Which normal curve has the greatest mean? Which normal curve has
the greatest standard deviation?
Note: Every normal distribution can be transformed into the Standard Normal Distribution (the distribution for z-
scores). This means we will use the z-score formula to transform any data value into a “measure of position” with the
formula:
data value mean
standard deviation
z
PAGE 2
**All probability calculations will be done with either StatCrunch or the TI 83/84 calculator. You do NOT need to learn
how to read the Standard Normal Table.**
**Also < and are treated the same as well as > and for any continuous probability distribution.**
Ex: Confirm that the area to the left of z = 1.15 is 0.8749. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, enter inequality symbol and z-score, Compute
TI-83/84: 2nd VARS normalcdf( -1000000000 Comma 1.15 Comma 0 Comma 1 enter
P(z 1.15) = 0.8749
Ex: Confirm that the cumulative area that corresponds to z = -0.24 is 0.4052. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, Standard, enter inequality symbol and z-score, Compute
TI-83/84: 2nd VARS normalcdf( -1000000000 Comma -0.24 Comma 0 Comma 1 enter
P(z -0.24) = 0.4052
PAGE 3
Ex: Find the area to right of each z-score. Hint: Use the fact that the total area (probability) is 1. **Label the z-score and
the area.**
a) b)
P(z 1.15) = _________________ P(z -0.24) = _________________
Ex: Find the shaded area. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, Stand.
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Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
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Chapter 6: Normal Probability Distribution
6.1: The Standard Normal Distribution
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Chapter 2: Exploring Data with Tables and Graphs
2.3: Graphs that Enlighten and Graphs that Deceive
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Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
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Chapter 5: Discrete Probability Distribution
5.1: Probability Distribution
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Chapter 8: Hypothesis Testing
8.2: Testing a Claim About a Proportion
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Chapter 2: Exploring Data with Tables and Graphs
2.1: Frequency Distributions for Organizing and Summarizing Data
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Chapter 5: Discrete Probability Distribution
5.3 - Poisson Probability Distributions
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Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
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Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
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Chapter 1: Introduction to Statistics
Section 1.3: Collecting Sample Data
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Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
Solution to the Practice Test 3A, Chapter 6 Normal Probability DistributionLong Beach City College
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Elementary Statistics Practice Test 3
Practice Test Chapter 6 (Normal Probability Distributions)
Chapter 6: Normal Probability Distributions
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Chapter 7: Estimating Parameters and Determining Sample Sizes
7.1: Estimating a Population Proportion
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Chapter 8: Hypothesis Testing
8.4: Testing a Claim About a Standard Deviation or Variance
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Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
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Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
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Elementary Statistics Practice Test 1
Module 1: Chapters 1-3
Chapter 1: Introduction to Statistics.
Chapter 2: Exploring Data with Tables and Graphs.
Chapter 3: Describing, Exploring, and Comparing Data.
Please Subscribe to this Channel for more solutions and lectures
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Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
PAGE 1 Chapter 5 Normal Probability Distributions .docxgerardkortney
PAGE 1
Chapter 5: Normal Probability Distributions
Section 5.1: Intro to Normal Distributions and the Standard Normal Distributions
Objectives:
Normal Distribution Properties
Use z-scores to Calculate Area Under the Standard Normal Curve (using StatCrunch or Calculator)
Discuss Unusual Values
In this section we will revisit histograms which can be estimated with normal (symmetric, bell-shaped) curves. From
Test 1 remember that normal curves have z-scores (for any data value) and areas under the curve (one way: Empirical
Rule). Now we will use these normal curves to find probabilities (areas) and z-scores for any data value. Why do we
need to study this? Eventually we will use these probabilities and z-scores to make decisions.
By using the normal distribution curve, we are treating the data as a continuous random variable that has its own
continuous probability distribution. (Remember that any probability distribution has two properties: all probabilities
are between 0 and 1 and the sum of the probabilities is 1.) **Probabilities = Areas under the curve**
Ex: Consider the normal distribution curves below. Which normal curve has the greatest mean? Which normal curve has
the greatest standard deviation?
Note: Every normal distribution can be transformed into the Standard Normal Distribution (the distribution for z-
scores). This means we will use the z-score formula to transform any data value into a “measure of position” with the
formula:
data value mean
standard deviation
z
PAGE 2
**All probability calculations will be done with either StatCrunch or the TI 83/84 calculator. You do NOT need to learn
how to read the Standard Normal Table.**
**Also < and are treated the same as well as > and for any continuous probability distribution.**
Ex: Confirm that the area to the left of z = 1.15 is 0.8749. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, enter inequality symbol and z-score, Compute
TI-83/84: 2nd VARS normalcdf( -1000000000 Comma 1.15 Comma 0 Comma 1 enter
P(z 1.15) = 0.8749
Ex: Confirm that the cumulative area that corresponds to z = -0.24 is 0.4052. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, Standard, enter inequality symbol and z-score, Compute
TI-83/84: 2nd VARS normalcdf( -1000000000 Comma -0.24 Comma 0 Comma 1 enter
P(z -0.24) = 0.4052
PAGE 3
Ex: Find the area to right of each z-score. Hint: Use the fact that the total area (probability) is 1. **Label the z-score and
the area.**
a) b)
P(z 1.15) = _________________ P(z -0.24) = _________________
Ex: Find the shaded area. **Label the z-score and the area.**
StatCrunch: Stat menu, Calculators, Normal, Stand.
Data reduction: breaking down large sets of data into more-manageable groups or segments that provide better insight.
- Data sampling
- Data cleaning
- Data transformation
- Data segmentation
- Dimension reduction
Module Five Normal Distributions & Hypothesis TestingTop of F.docxroushhsiu
Module Five: Normal Distributions & Hypothesis Testing
Top of Form
Bottom of Form
·
Introduction & Goals
This week's investigations introduce and explore one of the most common distributions (one you may be familiar with): the Normal Distribution. In our explorations of the distribution and its associated curve, we will revisit the question of "What is typical?" and look at the likelihood (probability) that certain observations would occur in a given population with a variable that is normally distributed. We will apply our work with Normal Distributions to briefly explore some big concepts of inferential statistics, including the Central Limit Theorem and Hypothesis Testing. There are a lot of new ideas in this week’s work. This week is more exploratory in nature.
Goals:
· Explore the Empirical Rule
· Become familiar with the normal curve as a mathematical model, its applications and limitations
· Calculate z-scores & explain what they mean
· Use technology to calculate normal probabilities
· Determine the statistical significance of an observed difference in two means
· Use technology to perform a hypothesis test comparing means (z-test) and interpret its meaning
· Use technology to perform a hypothesis test comparing means (t-test) (optional)
· Gather data for Comparative Study Final Project.
·
DoW #5: The SAT & The ACT
Two Common Tests for college admission are the SAT (Scholastic Aptitude Test) and the ACT (American College Test). The scores for these tests are scaled so that they follow a normal distribution.
· The SAT reported that its scores were normally distributed with a mean μ=896 and a standard deviation σ=174
· The ACT reported that its scores were normally distributed with a mean μ=20.6 and a standard deviation σ=5.2.
We have two questions to consider for this week’s DoW:
2. A high school student Bobby takes both of these tests. On the SAT, he achieves a score of 1080. On the ACT, he achieves a score of 30. He cannot decide which score is the better one to send with his college applications.
. Question: Which test score is the stronger score to send to his colleges?
· A hypothetical group called SAT Prep claims that students who take their SAT Preparatory course score higher on the SAT than the general population. To support their claim, they site a study in which a random sample of 50 SAT Prep students had a mean SAT score of 1000. They claim that since this mean is higher than the known mean of 896 for all SAT scores, their program must improve SAT scores.
. Question: Is this difference in the mean scores statistically significant? Does SAT Prep truly improve SAT Scores?
.
Investigation 1: What is Normal?
One reason for gathering data is to see which observations are most likely. For instance, when we looked at the raisin data in DoW #3, we were looking to see what the most likely number of raisins was for each brand of raisins. We cannot ever be certain of the exact number of raisins in a box (because it varies) ...
The Normal Distribution is a symmetrical probability distribution where most results are located in the middle and few are spread on both sides. It has the shape of a bell and can entirely be described by its mean and standard deviation.
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Chapter 6: Normal Probability Distribution
6.2: Real Applications of Normal Distributions
A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data. This allows the inspection of the data for its underlying distribution (e.g., normal distribution), outliers, skewness, etc.
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Elementary Statistics Practice Test 4
Chapter 9: Inferences about Two Samples
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Elementary Statistics Practice Test 4
Chapter 8: Hypothesis Testing
Solution to the practice test ch 8 hypothesis testing ch 9 two populationsLong Beach City College
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Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
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Elementary Statistics Practice Test 3
Practice Test Chapter 6 (Normal Probability Distributions)
Chapter 6: Normal Probability Distributions
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Chapter 12: Analysis of Variance
12.2: Two-Way ANOVA
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Chapter 12: Analysis of Variance
12.1: One-Way ANOVA
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Chapter 11: Goodness-of-Fit and Contingency Tables
11.2: Contingency Tables
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Chapter 9: Inferences from Two Samples
9.4: Two Variances or Standard Deviations
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Chapter 9: Inferences from Two Samples
9.3 Two Means, Two Dependent Samples, Matched Pairs
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Chapter 9: Inferences from Two Samples
9.2: Two Means, Independent Samples
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Chapter 9: Inferences from Two Samples
9.1: Inferences about Two Proportions
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Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. Chapter 6: Normal Probability Distribution
6.1 The Standard Normal Distribution
6.2 Real Applications of Normal Distributions
6.3 Sampling Distributions and Estimators
6.4 The Central Limit Theorem
6.5 Assessing Normality
6.6 Normal as Approximation to Binomial
2
Objectives:
• Identify distributions as symmetric or skewed.
• Identify the properties of a normal distribution.
• Find the area under the standard normal distribution, given various z values.
• Find probabilities for a normally distributed variable by transforming it into a standard normal variable.
• Find specific data values for given percentages, using the standard normal distribution.
• Use the central limit theorem to solve problems involving sample means for large samples.
• Use the normal approximation to compute probabilities for a binomial variable.
3. Key Concept:
Determine whether the requirement of a normal distribution is
satisfied:
(1) visual inspection of a histogram to see if it is roughly bell-
shaped
(2) identifying any outliers; and
(3) constructing a normal quantile plot.
Normal Quantile Plot
A normal quantile plot (or normal probability plot) is a graph
of points (x, y) where each x value is from the original set of
sample data, and each y value is the corresponding z score that is
expected from the standard normal distribution.
6.5 Assessing Normality
3
(x, y = Z-score)
TI Calculator:
How to enter data & Normal
Quantile Plot :
1. Stat
2. Edit
3. ClrList 𝑳𝟏
4. Or Highlight & Clear
5. Type in your data in L1, ..
6. Press 2nd y (for stat Plot)
7. Enter
8. Select ON
9. Select Type item (Last one in
the 2nd row option)
10. Enter 𝑳𝟏for the data List
11. Press Zoom
12. Press 9 (ZoomStat)
4. Procedure for Determining Whether It Is Reasonable to Assume That Sample
DataAre from a Population Having a Normal Distribution
1. Histogram: Construct a histogram. If the histogram departs dramatically from a bell
shape, conclude that the data do not have a normal distribution.
2. Outliers: Identify outliers. If there is more than one outlier present, conclude that the data
might not have a normal distribution.
3. Normal quantile plot: If the histogram is basically symmetric and the number of outliers
is 0 or 1, use technology to generate a normal quantile plot.
Apply the following criteria to determine whether the distribution is normal. (These
criteria can be used loosely for small samples, but they should be used more strictly for
large samples.)
Normal Distribution: The population distribution is normal if the pattern of the points
is reasonably close to a straight line and the points do not show some systematic
pattern that is not a straight-line pattern.
Not a Normal Distribution: The population distribution is not normal if either or both
of these two conditions applies:
• The points do not lie reasonably close to a straight line.
• The points show some systematic pattern that is not a straight-line pattern.
4
5. Normal Example
Normal: The first case shows a histogram of IQ scores that is close to being bell-
shaped suggesting a normal distribution. The corresponding normal quantile plot
shows points that are reasonably close to a straight-line pattern, and the points do
not show any other systematic pattern that is not a straight line. It is safe to assume
that these IQ scores are from a population that has a normal distribution.
(x, y = Z-score)
5
6. Uniform Example
Uniform: The second case shows a histogram of data having a uniform (flat)
distribution. The corresponding normal quantile plot suggests that the points are
not normally distributed. Although the pattern of points is reasonably close to a
straight-line pattern, there is another systematic pattern that is not a straight-
line pattern. We conclude that these sample values are from a population having
a distribution that is not normal.
(x, y = Z-score)
6
7. Skewed Example
Skewed: The third case shows a histogram of the amounts of rainfall (in inches) in
Boston for every Monday during one year. The shape of the histogram is skewed to
the right, not bell-shaped. The corresponding normal quantile plot shows points that
are not at all close to a straight-line pattern. These rainfall amounts are from a
population having a distribution that is not normal.
(x, y = Z-score)
7
8. Tools for Determining Normality
Histogram / Outliers: If the requirement of a normal
distribution is not too strict, simply look at a histogram and
find the number of outliers. If the histogram is roughly bell-
shaped and the number of outliers is 0 or 1, treat the
population as if it has a normal distribution.
Normal Quantile Plot: Normal quantile plots can be difficult
to construct on your own, but they can be generated with a TI-
83/84 Plus calculator or suitable software, such as Statdisk,
Minitab, Excel, or StatCrunch.
Advanced Methods: In addition to the procedures discussed
in this section, there are other more advanced procedures for
assessing normality, such as the chi-square goodness-of-fit
test, the Kolmogorov-Smirnov test, the Lilliefors test, the
Anderson-Darling test, the Jarque-Bera test, and the Ryan-
Joiner test (discussed briefly in Part 2).
8
TI Calculator:
How to enter data & Normal
Quantile Plot :
1. Stat
2. Edit
3. ClrList 𝑳𝟏
4. Or Highlight & Clear
5. Type in your data in L1, ..
6. Press 2nd y (for stat Plot)
7. Enter
8. Select ON
9. Select Type item (Last one in
the 2nd row option)
10. Enter 𝑳𝟏for the data List
11. Press Zoom
12. Press 9 (ZoomStat)
9. Manual Construction of Normal Quantile Plot
Step 1: First sort the data by arranging the values in order from lowest to highest.
Step 2. With a sample of size n, each value represents a proportion of 1/n of the
sample. Using the known sample size n, identify the areas of 1/2n, 3/2n, and so
on. These are the cumulative areas to the left of the corresponding sample values.
These values are the cumulative areas to the left of the corresponding sample
values.
Step 3: Use the standard normal distribution (software or a calculator or Table A-
2) to find the z scores corresponding to the cumulative left areas found in Step 2.
(These are the z scores that are expected from a normally distributed sample.)
Step 4: Match the original sorted data values with their corresponding z scores
found in Step 3, then plot the points (x, y), where each x is an original sample
value and y is the corresponding z score.
Step 5: Examine the normal quantile plot and use the criteria given in Part 1.
Conclude that the population has a normal distribution if the pattern of the points
is reasonably close to a straight line and the points do not show some systematic
pattern that is not a straight-line pattern.
9
10. Old Faithful Eruption Times
We can use the
Normality
Assessment
feature of
Statdisk with all
250 eruption
times listed in
the “Old
Faithful” data set
to get the
accompanying
display.
10
Let’s use the display with the three criteria for assessing normality.
1. Histogram: We can see that the histogram is skewed to the left and is far from being bell-shaped.
2. Outliers: The display shows that there are 20 possible outliers. If we examine a sorted list of the 250 eruption times, the 20 lowest
times do appear to be outliers.
3. Normal quantile plot: Whoa! The points in the normal quantile plot are very far from a straight-line pattern. We conclude that the
250 eruption times do not appear to be from a population with a normal distribution.
11. Example 1
(x, y = Z-score)
11
Construct a Normal
Quantile plot for a
sample of breaking
distance in feet
measured under
standard condition for a
sample of large cars are
as follows:
131, 134, 139, 143, 145
Normal. The points have coordinates:
(131, –1.28), (134, –0.52), (139, 0), (143, 0.52), (145, 1.28)
12. 12
Checking for Normality
Histogram
Pearson’s Index PI of Skewness
Outliers
Other Tests
Normal Quantile Plot
Chi-Square Goodness-of-Fit Test
Kolmogorov-Smikirov Test
Lilliefors Test
A survey of 18 high-technology firms showed the
number of days’ inventory they had on hand. Determine
if the data are approximately normally distributed.
5 29 34 44 45 63 68 74 74
81 88 91 97 98 113 118 151 158
Method 1: Construct a Histogram.
Example 2 (No Need)
The histogram is approximately bell-shaped.
13. 13
Checking for Normality
Histogram
Pearson’s Index PI of Skewness
Outliers
Other Tests
Normal Quantile Plot
Chi-Square Goodness-of-Fit Test
Kolmogorov-Smikirov Test
Lilliefors Test
Example 2 (No Need)
Method 2: Check for Skewness.
The PI is not greater than 1 or less than –1, so it can be
concluded that the distribution is not significantly
skewed.
Method 3: Check for Outliers.
Five-Number Summary: 5 - 45 - 77.5 - 98 - 158
Q1 – 1.5(IQR) = 45 – 1.5(53) = –34.5
Q3 + 1.5(IQR) = 98 + 1.5(53) = 177.5
No data below –34.5 or above 177.5, so no outliers.
3 79.5 77.5
3( )
PI 0.148
40.5
X MD
s
79.5, 77.5, 40.5
X MD s
Conclusion:
The histogram is approximately
bell-shaped.
The data are not significantly
skewed.
There are no outliers.
Thus, it can be concluded that the
distribution is approximately
normally distributed.