Katonah-Lewisboro School Closure: Response to 10/21/13 Public Hearing
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The school board may vote to close Lewisboro Elementary in 2014 due to declining elementary school enrollment over the past 20 years. At a public hearing on 10/21/2013, the superintendent Dr. Kreutzer presented a case for closure, showing a correlation between declining housing sales and enrollment, with a statistical correlation of 0.85 indicating a strong relationship. Closing a school would not lower taxes as projected and there is no need to rush into a closure, instead they should get a new demographic report considering the impact of full day kindergarten and an expected uptick in housing sales in the next two years.
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BUS 625 Week 2 Response for Discussion 1 & 2Week 2 Discussion 1 .docx
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Below there are two of my classmate’s discussion that needs I need to response to their names are Kristopher Wentworth and Ashley Thiberville
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This graph is a representation of single people versus married couples from the year 1950 to the year 2019. This information was gathered and presented by the U.S. Department of Commerce and the U.S. Census Bureau who have a good record of presenting accurate data and are highly credible. The U.S. Department of Commerce is responsible for promoting economic growth in the united states. The U.S. Census Bureau is an agency of the Federal government that is responsible for producing data about the people of America and the economy.
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This document examines the impact of affordable housing on the education of low-income students in Polk County, Iowa. It finds that residential and school mobility negatively impact student achievement, with students in high-poverty schools scoring 54% lower on standardized tests on average. Housing instability and high housing costs contribute to stress and poor health for low-income families and children. The document recommends investing in affordable housing programs and inclusionary zoning policies to promote residential stability and improve educational outcomes for low-income students.
2015 Fast Forward Report - compressed
The document is a report from Louisville's 55,000 Degrees movement, which aims to increase educational attainment in the city. It finds that increasing the percentage of residents with postsecondary degrees or certificates by just 1.4% per year would provide Louisville with the skilled workforce needed for economic growth. However, progress toward this goal is slowing. To accelerate, the report calls for new approaches and urgency in developing a workforce with skills matching the high-wage, high-skill jobs that are critical for a strong regional economy.
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"National Information Standards Organization (NISO)
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.More Related Content
Similar to Katonah-Lewisboro School Closure: Response to 10/21/13 Public Hearing
BUS308 Week 4 Lecture 1 Examining Relationships Expect.docx
BUS308 Week 4 Lecture 1
Examining Relationships
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around correlation
2. The basics of Correlation analysis
3. The basics of Linear Regression
4. The basics of the Multiple Regression
Overview
Often in our detective shows when the clues are not providing a clear answer – such as
we are seeing with the apparent continuing contradiction between the compa-ratio and salary
related results – we hear the line “maybe we need to look at this from a different viewpoint.”
That is what we will be doing this week.
Our investigation changes focus a bit this week. We started the class by finding ways to
describe and summarize data sets – finding measures of the center and dispersion of the data with
means, medians, standard deviations, ranges, etc. As interesting as these clues were, they did not
tell us all we needed to know to solve our question about equal work for equal pay. In fact, the
evidence was somewhat contradictory depending upon what measure we focused on. In Weeks 2
and 3, we changed our focus to asking questions about differences and how important different
sample outcomes were. We found that all differences were not important, and that for many
relatively small result differences we could safely ignore them for decision making purposes –
they were due to simple sampling (or chance) errors. We found that this idea of sampling error
could extend into work and individual performance outcomes observed over time; and that over-
reacting to such differences did not make much sense.
Now, in our continuing efforts to detect and uncover what the data is hiding from us, we
change focus again as we start to find out why something happened, what caused the data to act
as it did; rather than merely what happened (describing the data as we have been doing). This
week we move from examining differences to looking at relationships; that is, if some measure
changes does another measure change as well? And, if so, can we use this information to make
predictions and/or understand what underlies this common movement?
Our tools in doing this involve correlation, the measurement of how closely two
variables move together; and regression, an equation showing the impact of inputs on a final
output. A regression is similar to a recipe for a cake or other food dish; take a bit of this and
some of that, put them together, and we get our result.
Correlation
We have seen correlations a lot, and probably have even used them (formally or
informally). We know, for example, that all other things being equal; the more we eat. the more
we weigh. Kids, up to the early teens, grow taller the older they get. If we consistently speed,
we will get more speeding tickets than those who obey the speed limit. The more efforts we put
into studying, the better grades we get. All of these are examples of correlations.
Correlatio ...
BUS308 Week 4 Lecture 1 Examining Relationships Expect.docx
BUS308 Week 4 Lecture 1
Examining Relationships
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around correlation
2. The basics of Correlation analysis
3. The basics of Linear Regression
4. The basics of the Multiple Regression
Overview
Often in our detective shows when the clues are not providing a clear answer – such as
we are seeing with the apparent continuing contradiction between the compa-ratio and salary
related results – we hear the line “maybe we need to look at this from a different viewpoint.”
That is what we will be doing this week.
Our investigation changes focus a bit this week. We started the class by finding ways to
describe and summarize data sets – finding measures of the center and dispersion of the data with
means, medians, standard deviations, ranges, etc. As interesting as these clues were, they did not
tell us all we needed to know to solve our question about equal work for equal pay. In fact, the
evidence was somewhat contradictory depending upon what measure we focused on. In Weeks 2
and 3, we changed our focus to asking questions about differences and how important different
sample outcomes were. We found that all differences were not important, and that for many
relatively small result differences we could safely ignore them for decision making purposes –
they were due to simple sampling (or chance) errors. We found that this idea of sampling error
could extend into work and individual performance outcomes observed over time; and that over-
reacting to such differences did not make much sense.
Now, in our continuing efforts to detect and uncover what the data is hiding from us, we
change focus again as we start to find out why something happened, what caused the data to act
as it did; rather than merely what happened (describing the data as we have been doing). This
week we move from examining differences to looking at relationships; that is, if some measure
changes does another measure change as well? And, if so, can we use this information to make
predictions and/or understand what underlies this common movement?
Our tools in doing this involve correlation, the measurement of how closely two
variables move together; and regression, an equation showing the impact of inputs on a final
output. A regression is similar to a recipe for a cake or other food dish; take a bit of this and
some of that, put them together, and we get our result.
Correlation
We have seen correlations a lot, and probably have even used them (formally or
informally). We know, for example, that all other things being equal; the more we eat. the more
we weigh. Kids, up to the early teens, grow taller the older they get. If we consistently speed,
we will get more speeding tickets than those who obey the speed limit. The more efforts we put
into studying, the better grades we get. All of these are examples of correlations.
Correlatio.
Greg J. Duncan is Distinguished Professor in the School of Edu.docx
This document summarizes how rising income inequality in the United States has contributed to widening gaps in educational achievement and attainment between children from low- and high-income families. It describes how gaps in family incomes led to greater differences in resources available to families that support children's development, including expenditures on enrichment activities and parental interactions. These early differences contribute to the growing test score and educational attainment gaps seen between children from different income backgrounds.
Economics Homework Help.pptx
The document provides information about a program called Programa Escuelas de Calidad (PEC) instituted in Mexico in 2001 that provided grants and training to primary schools. It describes a dataset on dropout rates of 74,701 primary schools from 2000-2001 to 2003-2004, including schools that enrolled in PEC. It asks a series of questions about how to estimate the causal effect of PEC on dropout rates using this data, and discusses the strengths and weaknesses of different approaches such as simple before-after comparisons or comparing enrolled vs. non-enrolled schools. The best approach is determined to be a differences-in-differences analysis comparing changes in dropout rates over time between schools that did and did not enroll in P
BUS 625 Week 2 Response for Discussion 1 & 2Week 2 Discussion 1 .docx
BUS 625 Week 2 Response for Discussion 1 & 2
Week 2 Discussion 1 Response
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. In your response, provide your own interpretation of their distribution graph. Note any differences between your classmate’s interpretation and your own. Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Below there are two of my classmate’s discussion that needs I need to response to their names are Kristopher Wentworth and Ashley Thiberville
Kristopher Wentworth
This graph is a representation of single people versus married couples from the year 1950 to the year 2019. This information was gathered and presented by the U.S. Department of Commerce and the U.S. Census Bureau who have a good record of presenting accurate data and are highly credible. The U.S. Department of Commerce is responsible for promoting economic growth in the united states. The U.S. Census Bureau is an agency of the Federal government that is responsible for producing data about the people of America and the economy.
So, the graph that I chose to talk about is one showing the gap between how many people are married and how many people are single in the united states from 1950 - 2019. I chose this graph because it caught my attention right away because of the contrasting colors but also because of the information displayed. It is crazy to think that since 1950 the American population has more than doubled according to this graph and with the growing population, the numbers of married couples and singles rise too. However, if you look at the percentages of singles they haven't changed all too much. For example, the number of single Americans in 1950 was 37.3M and in 2019 it was 125.7M. Even with such a large population boom the percentage that was never married really hadn't changed going from 69% to 68%.
The presentation of this graph is excellent with the line graph being yellow and on a blue backdrop, it allows it to really stand out. The shape of the graph shows a sharp incline as the population in us explodes. Since this graph is focused on the single population of America it puts the focus on that with stats like "never been married, divorced, widowed" because there are multiple ways to be single and really only one way to be married.
Ashley Thiberville
The above histogram was compiled by the United States Census Bureau to show the rise of one-person households in the US. The Census Bureau is a branch of the Department of Commerce within the United States gov ...
BUS 625 Week 2 Response for Discussion 1 & 2Week 2 Discussion 1 .docx
BUS 625 Week 2 Response for Discussion 1 & 2
Week 2 Discussion 1 Response
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. In your response, provide your own interpretation of their distribution graph. Note any differences between your classmate’s interpretation and your own. Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Below there are two of my classmate’s discussion that needs I need to response to their names are Kristopher Wentworth and Ashley Thiberville
Kristopher Wentworth
This graph is a representation of single people versus married couples from the year 1950 to the year 2019. This information was gathered and presented by the U.S. Department of Commerce and the U.S. Census Bureau who have a good record of presenting accurate data and are highly credible. The U.S. Department of Commerce is responsible for promoting economic growth in the united states. The U.S. Census Bureau is an agency of the Federal government that is responsible for producing data about the people of America and the economy.
So, the graph that I chose to talk about is one showing the gap between how many people are married and how many people are single in the united states from 1950 - 2019. I chose this graph because it caught my attention right away because of the contrasting colors but also because of the information displayed. It is crazy to think that since 1950 the American population has more than doubled according to this graph and with the growing population, the numbers of married couples and singles rise too. However, if you look at the percentages of singles they haven't changed all too much. For example, the number of single Americans in 1950 was 37.3M and in 2019 it was 125.7M. Even with such a large population boom the percentage that was never married really hadn't changed going from 69% to 68%.
The presentation of this graph is excellent with the line graph being yellow and on a blue backdrop, it allows it to really stand out. The shape of the graph shows a sharp incline as the population in us explodes. Since this graph is focused on the single population of America it puts the focus on that with stats like "never been married, divorced, widowed" because there are multiple ways to be single and really only one way to be married.
Ashley Thiberville
The above histogram was compiled by the United States Census Bureau to show the rise of one-person households in the US. The Census Bureau is a branch of the Department of Commerce within the United States gov.
$320,000 Kindergarten Teachers
Your kindergarten classroom can leave a lasting
impact on your earnings and your quality of life long
after circle time is a distant memory.
Edupreneurship, the new business opportunity by Prof. M.M. Pant
Bill Gates discusses the importance of improving teacher quality to enhance education outcomes. He notes that the variation in teacher effectiveness is immense, with top teachers able to increase student performance by over 10% annually. However, the system does little to identify and reward great teachers or understand their techniques. Places like KIPP charter schools excel by closely tracking teacher impact, sharing best practices, and maintaining a culture of continuous improvement. Improving access to data on teaching quality and exemplar teaching methods could help make every teacher better.
Education quality and returns to schooling: evidence from migrants in Brazil
We provide a new education quality index for states within a developing country using 2010 Brazilian data. This measure is constructed based on the notion that the financial returns obtained from an additional year of schooling can be
seen as being derived from the value that market forces assign to this education. We use migrant data to estimate returns to schooling of individuals who studied in different states but who work in the same labor market. We find very heterogeneous educational qualities across states: the poorest Brazilian region presents education quality levels that are approximately equal to one-third of the average of all other regions, a gap three times larger than the one suggested by standardized test scores. We compare our index with standardized test scores, educational outcome variables, and public expenditure per schooling stage at the state level, producing new evidence related to education in a large developing country. We conduct an education quality-adjusted development accounting exercise for Brazilian states and find that human capital accounts for 26%-31% of output per worker differences. Adjusting for quality increases human capital’s explanatory power by 60%.
Date: 2017-02
Authors:
Brotherhood, Luiz Mário
Ferreira, Pedro Cavalcanti
Santos, Cézar Augusto Ramos
Critique of image~$itique.docxCritique of imagecritique.do.docx
Critique of image/~$itique.docx
Critique of image/critique.docx
I want critique for each image. No Plagiarism plzz
How has the artist used colors in the work(s)?
- What sort of effect do the colors have on the artwork?
- How as the artist used shapes within the work of art?
- How have lines been used in the work(s)? Has the artist used them as an
important or dominant part of the work, or do they play a different roll?
- What role does texture play in the work(s)? Has the artist used the illusion of texture or has the artist used actual texture? How has texture been used within the work(s)?
- How has the artist used light in the work(s)? Is there the illusion of a scene with lights and shadows, or does the artist use light and dark values in a more abstracted way?
- How has the overall visual effect or mood of the work(s)? Been achieved by the use of elements of art and principles of design.
- How were the artists design tools used to achieve a particular look or focus?
Critique of image/golden retriever 1 5x7.jpg
Critique of image/marilyn.jpg
Assignment 2
The goal of the next few questions is to help you intuitively understand omitted variable bias and too-many
variables bias, multicollinearity, and heteroskedasticity using a simulation in Stata.
Introduction. There's an important debate over how we can get more children to be able to read by 3rd
grade. I have heard anecdotal evidence that the state government plans the number of prisons to build based on
regressions that use current-year 3rd grade reading scores on the RHS, which might be suggestive of the importance
of this goal. But we won't worry about the causal e�ect of literacy on crime in this assignment.
As we saw in class, children from poor families are already behind in terms of reading ability in fall of Kinder-
garten. This is a problem. Local governments have two main policies to try to solve this problem: free full-day
preschool, and family income supports.
Omitted variable & too-many variables bias.
Question 1. Suppose the process that determines child test score in Kindergarten is given by
test scorei = β0 + β1preschooli + β2incomei + �i
where β1,β2 > 0 and preschooli is a continuous measure of �preschool quality.�
Preschool quality can be purchased with cash, and is purchased in cash according to the linear model
preschooli = α0 + α1incomei + ηi
You should interpret both of these linear models as structural models of human behavior. In other words, if you
gave a random family another dollar, the family would indeed purchase α1 more units of preschool quality, and the
family would purchase other children's stu� that has β2 additional e�ect on test scores. This means that the total
e�ect on test scores of giving the family another dollar is:
(a) α0β1
(b) α1
(c) β2 + β1α1
(d) α1β1 + β2
Question 2. Let's try to simulate this model in Stata. Run Part I in the �le �assignment2.do.� This will
generate a fake dataset of 1000 students for this problem a.
ECTM RP
This document analyzes income inequality in relation to education level using data from the 2003 and 2013 Current Population Surveys. It finds:
1) The largest education group in both surveys was those with a high school degree or less, followed by some college, college graduate, and advanced degree.
2) Calculated Gini coefficients showed some education groups and gender combinations became more equal over time, while others increased in inequality.
3) Regression analysis rejected the hypothesis of no difference in earnings between similarly educated males and females, indicating income inequality.
The Principles of Graphing
This document provides an overview of graphing principles and their application in economics. It begins with an introduction explaining the importance of understanding graphs. It then presents a series of slides explaining key graphing concepts like the Cartesian plane, linear relationships between variables, calculating slope, and using the equation y=mx+b to represent relationships. Examples are given for positive, negative, and no relationships between real-world variables. Both linear and non-linear relationships are covered. The goal is to establish the foundational graphing skills needed for understanding economics.
The Principle of Graphing
This document provides an overview of graphing principles and their application in economics. It begins with an introduction explaining the importance of understanding graphs. It then presents a series of slides explaining key graphing concepts like the Cartesian plane, linear relationships between variables, measuring slope, and using the equation y=mx+b to represent linear relationships algebraically. Examples are given for positive, negative, and no relationships between real-world variables like income and life expectancy. Non-linear relationships are also briefly discussed. The document emphasizes that understanding graphs is critical for success in economics courses.
PEST ANALYSIS OF EDUCATIONAL ACADEMY IN UK
PEST analysis examines the political, economic, social, and technological factors affecting an educational academy in the UK. The analysis found that political factors like emigration rates and curriculum changes, economic factors like inflation and exchange rates, social factors like cultural diversity and public policies, and technological advancements all impact the academy's operations and strategies. The educational academy has adapted successfully to these various factors through strategic planning, innovative teaching methods, and community engagement to thrive in its environment.
Richard Murnane - Universidad de Harvard
El lunes 13 de noviembre organizamos en la Fundación Ramón Areces, una conferencia sobre los cambios económicos, desigualdad de renta y pérdida de oportunidades para niños en familias de renta baja. Una conferencia expuesta por Richard Murnane, de la Universidad de Harvard.
Thurman Model 1 Cross Sectional ECN 405
The document discusses a study analyzing how personal income and level of education affect housing prices across US states. A regression model is presented with housing price as the dependent variable and income, education, taxes, unemployment, GDP, population, and burglary rates as independent variables. Initial results found education, taxes, and burglary significantly correlated with housing prices as expected, while GDP was less correlated. Tests showed evidence of heteroscedasticity but not endogeneity. Overall, the study found income more correlated with housing prices than education.
thesis talk transcript
Hailey Evans and her thesis mentor Professor Stokes developed a mathematics curriculum called Only The NUMBERS Count for kindergarten through second grade. Their research found it helps students master concepts more easily and perform above average on state tests aligned with Common Core standards. It uses a single manipulative called blocks and a base-10 counting system to make math less abstract. Their longitudinal study of three classrooms from kindergarten to second grade found students consistently scored above the 50th percentile on state tests using this curriculum. They conclude it allows students to exceed Common Core expectations and revisions should be made to incorporate its approach.
EDL 710. EIP Final. 04.28.15
Brian Piazza proposes an Educational Improvement Plan to address high failure rates and low graduation rates among 9th grade students at Ossining High School. The plan involves transitioning from a Freshman Counselor model to an "At-Risk Counselor" position focused on struggling students. It also includes developing a Freshman Academy and study hall classes for at-risk students. The proposal analyzes data showing over 30% of 9th graders failed classes in the current year, and graduation rates have remained around 80% for 10 years despite the Freshman Counselor. Nearby schools with higher graduation rates use a 9-12 counseling model rather than focusing solely on freshmen. The plan will be implemented over 4 years,
The State of Low-Income Students in Polk County
This document examines the impact of affordable housing on the education of low-income students in Polk County, Iowa. It finds that residential and school mobility negatively impact student achievement, with students in high-poverty schools scoring 54% lower on standardized tests on average. Housing instability and high housing costs contribute to stress and poor health for low-income families and children. The document recommends investing in affordable housing programs and inclusionary zoning policies to promote residential stability and improve educational outcomes for low-income students.
2015 Fast Forward Report - compressed
The document is a report from Louisville's 55,000 Degrees movement, which aims to increase educational attainment in the city. It finds that increasing the percentage of residents with postsecondary degrees or certificates by just 1.4% per year would provide Louisville with the skilled workforce needed for economic growth. However, progress toward this goal is slowing. To accelerate, the report calls for new approaches and urgency in developing a workforce with skills matching the high-wage, high-skill jobs that are critical for a strong regional economy.
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BUS308 Week 4 Lecture 1 Examining Relationships Expect.docx
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The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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Agenda
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For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
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Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
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spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Katonah-Lewisboro School Closure: Response to 10/21/13 Public Hearing
- 1. KLSD School Closure A response to Dr. Kreutzer’s 10/21/2013 presentation
- 2. The KLSD school board may vote to close Lewisboro Elementary in 2014
- 3. On 10/21/2013, Dr. Paul Kreutzer, superintendent, presented the case for closure as a public hearing. Let’s look at his slides.
- 4. This slide tells the story of declining enrollment in elementary schools
- 5. Let’s take a closer look …
- 7. Let’s drop projected and look at actuals.
- 9. This represents a 20 year period. It looks a little stuffed together.
- 10. Better, but still not quite right. Let’s add in some breathing room
- 11. Interesting, what’s that big point in 1997?
- 12. In 1997, Increase Miller was overflowing with students, so in 1998, redistricting was done. Want to see what this looks like after that?
- 14. Any idea about the changes over time?
- 15. More houses sold, means more elementary enrollment.
- 17. Housing sales
- 18. This is both on the same slide
- 19. It kind of seems like when housing sales decrease, so does enrollment. What about average enrollment?
- 20. Average enrollment and housing
- 21. Housing and enrollment definitely seem to move the same way. Is there a correlation?
- 22. The Pearson correlation between housing and enrollment is .85.
- 23. Huh?
- 24. You can use a statistical function in Excel called correl or pearson to determine the correlation between 2 sets of numbers. For this the result is .85
- 25. Is that good, does that mean there is a correlation?
- 26. There is a very strong positive relationship between housing sales and elementary enrollment. If r = +.70 or higher Very strong positive relationship
- 27. We know this area. We see new families move in and old families move out. These numbers tell us to believe what we see.
- 28. The cost of this mistake is: • crowded classrooms • disruption • multi-million dollar cost reopen a school in 5 years • lower property values • higher taxes to
- 29. Taxes will not go down Not in 2014 … and not in the future Closing a school is not a plan for lower taxes.
- 30. There is no rush. Let’s get a demographic report that considers housing We’ve been graduating large classes during a housing slump.
- 31. In 2 years, we’ll see the effect of : • Full day kindergarten • A housing sale uptick And we’ll understand how correct those demographic projections are.
- 32. Slow down Let’s get this right.