Hello! This is my PowerPoint Presentation on free falling bodies.
Some transition might failed when viewing. so if you want a better presentation using this, you could ask me.
The Galileo vs Aristotle part is kind-of a video presentation. You could find a better video on Youtube.
For further question, just comment on the comment box below.
or
Send me an Email ( glydelle27@gmail.com )
Elementary introduction to the scientific method focusing on variables. This is the first of a two part lesson on the scientific method which focuses variables and the later one covers the procedures of the scientific method, at about the 5th and 6th grade level.
The second slide show is called Scientific Method Procedures.
Illustrate the nature of bivariate data;
Construct a scatter plot;
Describe shapes (form), trend (direction), and variation (strength) based on the scatter plot; and
Estimate strength of association between the variables based on a scatter plot.
Visit the website for other Services: https://cristinamontenegro92.wixsite.com/onevs
Hello! This is my PowerPoint Presentation on free falling bodies.
Some transition might failed when viewing. so if you want a better presentation using this, you could ask me.
The Galileo vs Aristotle part is kind-of a video presentation. You could find a better video on Youtube.
For further question, just comment on the comment box below.
or
Send me an Email ( glydelle27@gmail.com )
Elementary introduction to the scientific method focusing on variables. This is the first of a two part lesson on the scientific method which focuses variables and the later one covers the procedures of the scientific method, at about the 5th and 6th grade level.
The second slide show is called Scientific Method Procedures.
Illustrate the nature of bivariate data;
Construct a scatter plot;
Describe shapes (form), trend (direction), and variation (strength) based on the scatter plot; and
Estimate strength of association between the variables based on a scatter plot.
Visit the website for other Services: https://cristinamontenegro92.wixsite.com/onevs
STAT200 Assignment #1 - Descriptive Statistics Analysis Plan - Te.docxsusanschei
STAT200: Assignment #1 - Descriptive Statistics Analysis Plan - Template
Page 1 of 3
University of Maryland University College
STAT200 - Assignment #1: Descriptive Statistics Data Analysis Plan
Identifying Information
Student (Full Name):
Class:
Instructor:
Date:
Scenario: Please write a few lines describing your scenario and the four variables (in addition to income) you have selected.
Use Table 1 to report the variables selected for this assignment. Note: The information for the required variable, “Income,” has already been completed and can be used as a guide for completing information on the remaining variables.
Table 1. Variables Selected for the Analysis
Variable Name in the Data Set
Description
(See the data dictionary for describing the variables.)
Type of Variable
(Qualitative or Quantitative)
Variable 1: “Income”
Annual household income in USD.
Quantitative
Variable 2:
Variable 3:
Variable 4:
Variable 5:
Reason(s) for Selecting the Variables and Expected Outcome(s):
Variable 1: “Income” -
Variable 2: “ “ -
Variable 3: “ “ -
Variable 4: “ “ -
Variable 5: “ “ -
Data Set Description:
Proposed Data Analysis:
Measures of Central Tendency and Dispersion
Complete Table 2. Numerical Summaries of the Selected Variables and briefly explain why you choose those measurements. Note: The information for the required variable, “Income,” has already been completed and can be used as a guide for completing information on the remaining variables.
Table 2. Numerical Summaries of the Selected Variables
Variable Name
Measures of Central Tendency and Dispersion
Rationale for Why Appropriate
Variable 1:
“Income”
Number of ObservationsMedianSample Standard Deviation
I am using median for two reasons:If there are any outliers or the data is not normally distributed, the median is the best measure of central tendency.The variable is quantitative.
I am using sample standard deviation for three reasons:The data is a sample from a larger data set.It is the most commonly used measure of dispersion.The variable is quantitative.
Variable 2:
Variable 3:
Variable 4:
Variable 5:
Graphs and/or Tables
Complete Table 3. Type of Graphs and/or Table for Selected Variables and briefly explain why you choose those graphs and/or tables. Note: The information for the required variable, “Income,” has already been completed and can be used as a guide for completing information on the remaining variables.
Table 3. Type of Graphs and/or Tables for Selected Variables
Variable Name
Graph and/or Table
Rationale for why Appropriate?
Variable 1:
“Income”
Graph: I will use the histogram to show the normal distribution of data.
Histogram is one of the best plot to show the normal distribution of quantitative level data .
Variable 2:
Variable 3:
Variable 4:
Variable 5:
STAT200: Written Assignment #1 - Descriptive Statistics Data Analysis Plan - Instructions
Page 1 of 4
STAT200 Introduction to Statistics
Assig.
Experimental Design Scientific Method and GraphingREVISED.pptMathandScienced
Experimental Design Scientific Method and Graphing. Scientific method. Graphing and experimental science. Chemistry and learning . Problem solving to the degree of fluency
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
Similar to Graphing notes & practice problems (20)
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
2. Date: 8/19/14
Main Topic: Graphing, Scientific
Method & Variables
Learning Objective:
How to set up a successful graph.
Subtitle: Different Types of Graphs
3. Bar graphs
◦ Used when data isn’t continuous.
◦ To compare categories
Histograms
◦ Groups data into ranges, shows
continuous data
Line Graphs
◦ For continuous data
◦ useful for showing trends over time
◦ can include more than 1 data set
Types of Graphs:
4. Pie Graph – for comparing parts
of a whole. They do not show
changes over time.
Area Graph – for tracking
changes over time in two or more
related groups that make up one
whole.
X-Y Scattered Plot – used to
show the relationship between two
sets of data (ex. height vs. weight)
5. Types of data
a) Dependent Variable:
It’s the “measured” variable
unpredictable change
On Y-axis
<we don’t know how it will change until we do the experiment>
b) Independent Variable:
It’s the manipulated variable
predictable change
On the X-axis
<it only changes because we chose how it would change>
The effect of
_____________
on
_____________
INDEPENDENT
DEPENDENT
11. TAILS
T - Title
A - Axis
Teachers’s Favorite Singer
Y Axis =
Dependent
Variable
X Axis =
Independent
Variable
12. TAILS
T - Title
A – Axis
S – Scale
Teachers’s Favorite Singer
Decide on an appropriate
scale for each axis.
Choose a scale that lets
you make the graph as
large as possible for your
paper and data
13. How to determine scale
Scale is
determined by
your highest &
lowest number.
In this case your
scale would be
from 2 – 22.
Favorite
Singer
Number of
Teachers
Toby Keith 22
Madonna 15
Elvis 11
Sting 5
Sinatra 2
14. How to determine Intervals
The interval is
decided by your
scale.
In this case your
scale would be from
2 – 22 and you want
the scale to fit the
graph.
The best interval
would be to go by
5’s.
Favorite
Singer
Number of
Teachers
Toby Keith 22
Madonna 15
Elvis 11
Sting 5
Sinatra 2
15. TAILS
T – Title
A – Axis
I – Interval
S – Scale
Teachers’s Favorite Singer
The amount of space between one
number and the next or one type of
data and the next on the graph.
The interval is just as important as
the scale
Choose an interval that lets you
make the graph as large as possible
for your paper and data
16. TAILS
T – Title
A – Axis
I – Interval
S – Scale
Teachers’s Favorite Singer
0
5
10
15
20
25
17. TAILS
T – Title
A – Axis
I – Interval
L – Labels
S – Scale
Teachers’s Favorite Singer
0
5
10
15
20
25
LABEL your bars or
data points
Singers
Give the bars a general label. What
do those words mean?
NumberofTeachers
Label your Y Axis. What do those
numbers mean?
18. Examples of experiments 1
Q: How does fertilizer affect the growth
rate of plants?
A: We set up an experiment testing
different amounts of fertilizer on different
plants & measuring the growth (height) of
the plants:
Dependent variable (Y-axis)?
◦ height of plants
Independent variable (X-axis)?
◦ amount of fertilizer
The effect of
_____________
on
_____________
Amount of Fertilizer
Height of Plants
19. Examples of experiments 2
Q:How does exercise affect heart rate of
10th grade student?
A: We set up an experiment testing
different lengths of time of exercise
(minutes) on the heart rate of students:
Dependent variable (Y-axis)?
◦ heart rate
Independent variable (X-axis)?
◦ minutes of exercise
The effect of
_____________
on
_____________
Exercise
Heart Rate
20. Examples of experiments 3
Q: What’s the favorite drink of students?
A: we set up an experiment surveying
students and asking which is their favorite
drink :
Dependent variable (Y-axis)?
◦ number of students
Independent variable (X-axis)?
◦ type of drink
The effect of
_____________
on
_____________
Type of Drink
How many students chose it
22. Which drink do you like best?
type of drink
numberofstudents
1
2
3
4
5
6
Coke Pepsi water tea Gatorade
0
drink number
Coke 1
Pepsi 1
Water 4
Iced tea 4
Gatorade 3
Red Bull?
RBRB
Bar graph!
23. Classwork:
*Do problems #1 Questions A - E
*Write the Questions & Answers
*Graph Data
*Do the same for Problem #3
24. Problem #1
A study was conducted on the feeding preferences of slugs. Specimens were
fed a variety of food sources and data were collected on number of grams of
each type of food eaten. Construct the appropriate type of graph and make a
conclusion on food preference.
Questions for problem #1:
a) What type of graph will you use? Why?
b) What is the DEPENDENT variable? (hint: what is being measured?)
c) What is the INDEPENDENT variable (hint: what are we testing?)
d) Which food source was favored by slugs the most, and how do you know
that?
e) What is the title of your graph?
**REMEMBER TO GRAPH THE
DATA SET
25. Problem #3
A study was made of endangered birds to see if their populations were
increasing by being protected from hunters. Scientists went out into the
field every ten years and counted the number of Whooping Crane,
California Condor, and Black Swans they found in their spring feeding
grounds. Review the data table and draw an appropriate graph with
labeled lines and axes and a title.
a) What type of graph will you use? Why?
b) What is the DEPENDENT variable?
c) What is the INDEPENDENT variable?
d) What is the title of your graph?
e) By interpreting the graph, what can you conclude about the whooping
crane population?
f) By interpreting the graph, what can you conclude about the California
Condor population?
g) By interpreting the graph, what can you conclude about the black
swan population?
YEARS
Bird Species 1950 1960 1970
Whooping Crane 24 41 78
California Condor 76 43 20
Black Swan 56 58 57
