This document provides an outline for a lecture series revising key concepts for Test B, including:
- Pythagoras' theorem, trigonometry, sine and cosine rules, and calculating triangle areas.
- Probability, probability trees, and examples calculating probabilities of dice rolls.
- Descriptive statistics like mode, median, interquartile range, mean, absolute deviation, and standard deviation.
- Hypothesis testing using z-tests, t-tests, and chi-squared tests; including setting up hypotheses, finding critical values, calculating test statistics, and making conclusions.
The revision is in preparation for Standard Track Test B which will be held the week of April 21st.
NUMERICA METHODS 1 final touch summary for test 1musadoto
MY FINAL TOUCH SUMMARY FOR TEST 1
ON 6TH MAY 2018
TOPICS AND MATERIALS COVERED
1. Class lecture notes (Basic concepts, errors and roots of function).
2. Lecture’s examples.
3. Past Years Examples.
4. Past Years examination papers.
5. Tutorial Questions.
6. Reference Books + web.
NUMERICA METHODS 1 final touch summary for test 1musadoto
MY FINAL TOUCH SUMMARY FOR TEST 1
ON 6TH MAY 2018
TOPICS AND MATERIALS COVERED
1. Class lecture notes (Basic concepts, errors and roots of function).
2. Lecture’s examples.
3. Past Years Examples.
4. Past Years examination papers.
5. Tutorial Questions.
6. Reference Books + web.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Siegel-Tukey test named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to the data measured at least on an ordinal scale. It tests for the differences in scale between two groups.
The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.
The test was published in 1980 by Sidney Siegel and John Wilder Tukey in the journal of the American Statistical Association in the article “A Non-parametric Sum Of Ranks Procedure For Relative Spread in Unpaired Samples “.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Siegel-Tukey test named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to the data measured at least on an ordinal scale. It tests for the differences in scale between two groups.
The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.
The test was published in 1980 by Sidney Siegel and John Wilder Tukey in the journal of the American Statistical Association in the article “A Non-parametric Sum Of Ranks Procedure For Relative Spread in Unpaired Samples “.
Descriptive Statistics Formula Sheet Sample Populatio.docxsimonithomas47935
Descriptive Statistics Formula Sheet
Sample Population
Characteristic statistic Parameter
raw scores x, y, . . . . . X, Y, . . . . .
mean (central tendency) M =
∑ x
n
μ =
∑ X
N
range (interval/ratio data) highest minus lowest value highest minus lowest value
deviation (distance from mean) Deviation = (x − M ) Deviation = (X − μ )
average deviation (average
distance from mean)
∑(x − M )
n
= 0
∑(X − μ )
N
sum of the squares (SS)
(computational formula) SS = ∑ x
2 −
(∑ x)2
n
SS = ∑ X2 −
(∑ X)2
N
variance ( average deviation2 or
standard deviation
2
)
(computational formula)
s2 =
∑ x2 −
(∑ x)2
n
n − 1
=
SS
df
σ2 =
∑ X2 −
(∑ X)2
N
N
standard deviation (average
deviation or distance from mean)
(computational formula) s =
√∑ x
2 −
(∑ x)2
n
n − 1
σ =
√∑ X
2 −
(∑ X)2
N
N
Z scores (standard scores)
mean = 0
standard deviation = ± 1.0
Z =
x − M
s
=
deviation
stand. dev.
X = M + Zs
Z =
X − μ
σ
X = μ + Zσ
Area Under the Normal Curve -1s to +1s = 68.3%
-2s to +2s = 95.4%
-3s to +3s = 99.7%
Using Z Score Table for Normal Distribution
(Note: see graph and table in A-23)
for percentiles (proportion or %) below X
for positive Z scores – use body column
for negative Z scores – use tail column
for proportions or percentage above X
for positive Z scores – use tail column
for negative Z scores – use body column
to discover percentage / proportion between two X values
1. Convert each X to Z score
2. Find appropriate area (body or tail) for each Z score
3. Subtract or add areas as appropriate
4. Change area to % (area × 100 = %)
Regression lines
(central tendency line for all
points; used for predictions
only) formula uses raw
scores
b = slope
a = y-intercept
y = bx + a
(plug in x
to predict y)
b =
∑ xy −
(∑ x)(∑ y)
n
∑ x2 −
(∑ x)2
n
a = My - bMx
where My is mean of y
and Mx is mean of x
SEest (measures accuracy of predictions; same properties as standard deviation)
Pearson Correlation Coefficient
(used to measure relationship;
uses Z scores)
r =
∑ xy−
(∑ x)(∑ y)
n
√(∑ x2−
(∑ x)2
n
)(∑ y2−
(∑ y)2
n
)
r =
degree x & 𝑦 𝑣𝑎𝑟𝑦 𝑡𝑜𝑔𝑒𝑡ℎ𝑒𝑟
degree x & 𝑦 𝑣𝑎𝑟𝑦 𝑠𝑒𝑝𝑎𝑟𝑎𝑡𝑒𝑙𝑦
r
2
= estimate or % of accuracy of predictions
PSYC 2317 Mark W. Tengler, M.S.
Assignment #9
Hypothesis Testing
9.1 Briefly explain in your own words the advantage of using an alpha level (α) = .01
versus an α = .05. In general, what is the disadvantage of using a smaller alpha
level?
9.2 Discuss in your own words the errors that can be made in hypothesis testing.
a. What is a type I error? Why might it occur?
b. What is a type II error? How does it happen?
9.3 The term error is used in two different ways in the context of a hypothesis test.
First, there is the concept of sta
1. Rentang
2. Rentang antarkuartil
3. Rentang semikuartil
4. Rentang a-b persentil
5. Simpangan baku
6. Variansi
7. Ukuran penyebaaran relatif
8. Bilangan baku
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
How to Make a Field invisible in Odoo 17Celine George
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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.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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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.
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C2 st lecture 13 revision for test b handout
1. Lecture 13 - Revision for Test B
C2 Foundation Mathematics (Standard Track)
Dr Linda Stringer Dr Simon Craik
l.stringer@uea.ac.uk s.craik@uea.ac.uk
INTO City/UEA London
2. Outline
Lecture 8
Pythagoras’ Theorem and Trigonometry
Sine and Cosine rules, Areas
Lecture 9
Probability
Lecture 10
Mode, Median and Interquartile range
Mean, Absolute deviation, Standard deviation
Z-test
Lecture 11
T-test
Lecture 12
χ2 -test
9. Probability
The sum of all probabilities is 1.
The probability an event A happens is denoted P(A) and is
a number between 0 and 1.
The probability A does not happen is 1 − P(A).
The probability A and B happens is P(A) × P(B).
The probability A or B happens is P(A) + P(B).
10. Probability trees
You can use a probability tree when there is more than one
event.
Draw different tiers of branches for different events, and
write the probability next to the branch.
Multiply probabilities along the branches (AND)
Add up the probabilities at the end of each branch (OR)
The total of all the probabilities at the end of the branches
should be 1
The probabilities for the second tier may be the same as
for the first tier, or they may be different
11. Probability tree - roll a fair die twice (or roll two fair
dice)
What is the probability that I roll two fours?
What is the probability that I roll no fours?
What is the probability that I roll exactly one four?
What is the probability that I roll at least one four?
First roll Second roll
P(4, 4) = 1
6 × 1
6 = 1
36
P(4, ¬4) = 1
6 × 5
6 = 5
36
P(¬4, 4) = 5
6 × 1
6 = 5
36
P(¬4, ¬4) = 5
6 × 5
6 = 25
36
¬4
¬4
5/6
41/6
5/6
4
¬4
5/6
41/6
1/6
12. Mode
The mode is the most frequent object in your data.
X = 5 8 -3 7 8 2 8
The mode of X is 8.
Y = 0 2 1 6 5 1 2 3
The modes of Y are 1 and 2.
13. Median
The median is the middle data.
X = 5 8 -3 7 8 2 8
Order the data
X = -3 2 5 7 8 8 8
7 × 1/2 = 3.5
Round up to 4.
The median of X is the fourth term 7.
14. Median
Y = 0 2 1 6 5 1 2 3
Order the data
Y = 0 1 1 2 2 3 5 6
8 × 1/2 = 4
Add the fourth term and the fifth term and divide by 2.
The median of Y is 2+2
2 = 2.
15. Interquartile range
First order the data, with values increasing from left to right.
We want to find two values: the first quartile Q1 and the
third quartile Q3.
Let n be the size of the data set (the number of values).
To find Q1 we multiply n by 1
4 . If n
4 is an integer (whole
number) then Q1 is the midpoint of the (n
4 )th value and the
(n
4 + 1)th value
If n
4 is not an integer then round it up to the nearest integer.
Q1 is the corresponding value.
To find Q3 we multiply n by 3
4 . If 3n
4 is an integer then Q3 is
the midpoint of the (3n
4 )th value and the (3n
4 + 1)th value
If 3n
4 is not an integer then round it up to the nearest
integer. Q3 is the corresponding value.
The interquartile range is Q3 − Q1.
16. Interquartile range
The interquartile range is the middle half of the data. Take the
ordered data
X = -3 2 5 7 8 8 8
Find Q1 :
7 × 1/4 = 1.75
Round up to 2.
The first quartile if X is the second term, Q1 = 2.
Find Q3 :
7 × 3/4 = 5.25
Round up to 6.
The third quartile if X is the sixth term, Q3 = 8.
The interquartile range is Q3 − Q1 = 8 − 2 = 6
17. Interquartile range
The interquartile range is the middle half of the data. Take the
ordered data
Y = 0 1 1 2 2 3 5 6
Find Q1 :
8 × 1/4 = 2
Add the second term and the third term and divide by 2.
The first quartile of X is Q1 = 1+1
2 = 1.
Find Q3 :
8 × 3/4 = 6
Add the sixth term and the seventh term and divide by 2.
The third quartile of X is Q3 = 3+5
2 = 4.
The interquartile range is Q3 − Q1 = 4 − 1 = 3
18. Mean
The mean is the average of the data.
¯x =
x
n
5 8 -3 7 8 2 8
¯x =
5 + 8 + (−3) + 7 + 8 + 2 + 8
7
=
35
7
= 5
19. Absolute deviation
The absolute deviation is the average distance of the data from
the mean.
AD =
|x − ¯x|
n
5 8 -3 7 8 2 8
Calculate |x − ¯x| (remember that we calculated ¯x = 5)
0 3 8 2 3 3 3
Add these values together and divide by the number of values
in the list
AD =
0 + 3 + 8 + 2 + 3 + 3 + 3
7
=
22
7
20. Standard deviation
5 8 -3 7 8 2 8
The standard deviation, σ, is a measure of spread.
First find the variance, σ2.
σ2
=
x2
n
− ¯x2
Calculate x2:
25 64 9 49 64 4 64
Add these values together, divide by the size of the data and
subtract the mean squared.
σ2
=
279
7
− 52
=
104
7
The standard deviation is the square root of the variance
σ = 3.85 to 2 d.p.
21. Structure of hypothesis tests
The Z-test, the T-test and the χ2-test all have the same
structure
Hypotheses (H0 and H1)
Critical value (look it up in a table, and for the Z-test and
T-test add a sketch showing rejection regions )
Test statistic (calculate)
Decision (accept/reject H0)
Conclusion (write a sentence)
22. The Z-test
The question will tell you the population mean µ and the
standard deviation of the population σ. It may also give you
information for your alternative hypothesis.
Write your null hypothesis H0 and your alternative
hypothesis H1.
Read your critical value off the table.This will depend on
your significance level and your alternative hypothesis.
You will be given a sample mean ¯x and the size of the
sample data n. Calculate your test statistic.
Z =
¯x − A
σ/
√
n
Compare the test statistic and the critical value. Decide to
accept or reject the null hypothesis. Write a concluding
sentence.
23. Example
The National Association of Florists say the perfect
sunflower should be 90cm tall with a standard deviation of
9.5cm. We grow 100 sunflowers with a mean height of 92
cm. We want to see whether or not our sunflowers are
perfect to a 5% level of significance.
H0 : µ = 90.
H1 : µ = 90.
We are doing a 2-tailed test. Reading off our table we get
critical values of −1.96 and 1.96.
The test statistic is 92−90
9.5/
√
100
= 2.11 to 2 d.p.
The test statistic 2.11 is bigger than the critical value 1.96
so we decide to reject the null hypothesis.
We conclude that our sunflowers are not perfect.
24. The T-test
The question will tell you the population mean µ. It will also
give you a small table of size n with some data. It may also
give you information for your alternative hypothesis.
Write your null hypothesis H0 and your alternative
hypothesis H1.
Read your critical value off the table.This will depend on
your significance level, your alternative hypothesis and the
degree of freedom of your data.
You will calculate the sample mean ¯x and the sample
standard deviation s. Calculate your test statistic.
T =
¯x − A
s/
√
n
Compare the test statistic and the critical value. Decide to
accept or reject the null hypothesis. Write a concluding
sentence.
25. Example
The National Association of Florists say the perfect
sunflower should be 90cm tall. We grow 8 sunflowers.
90 93 87 86 50 92 95 93
We want to see whether our sunflowers are too short at a
5% level of significance.
H0 : µ = 90.
H1 : µ < 90.
We are doing a 1-tailed test, our degrees of freedom is
8 − 1 = 7. Reading off our table we get a critical value of
−1.90.
The sample mean is 86 and the sample standard deviation
is 13.01. It follows the test statistic is −0.87.
The test statistic −0.87 is bigger than the critical value
−1.90 so we decide to accept the null hypothesis.
We conclude that our sunflowers are perfect.
26. The χ2
-test
The question will give you a table.
Write your null hypothesis H0 that the variables are
independent and your alternative hypothesis H1 that the
variables are dependent.
Read your critical value off the table.This will depend on
your significance level and the degrees of freedom of your
data (n − 1)(m − 1).
Calculate the row and column totals of the observed table.
Calculate the expected table. Row total times column total
divide by grand total.
Calculate the residual table. Observed minus expected.
Calculate the χ2 table. Residual value squared divided by
the expected value.
Calculate the test statistic. Add all the values in the χ2
table.
Compare the test statistic and the critical value. Decide to
accept or reject the null hypothesis. Write a concluding
sentence.
27. Example
The Institute for Studies want to know if the weather and
peoples happiness is independent to a 1% level of
significance. They collect the following data.
Happy Indifferent Sad
Rain 4 9 20
Snow 15 2 10
Sun 25 12 3
H0 : The weather and people’s happiness are independent.
H1 : People’s happiness depends on the weather.
Calculate the totals.
Happy Indifferent Sad Row total
Rain 4 9 20 33
Snow 15 2 10 27
Sun 25 12 3 40
Column total 44 23 33 100
29. Example
Calculate the χ2 table (to 3 d.p.).
7.623 0.262 7.621
0.819 2.854 0.133
3.111 0.8 7.882
The test statistic is 31.10 to 2 d.p.
The degree of freedom is (3 − 1) × (3 − 1) = 4. Our critical
value is 13.28.
Our test statistic is greater than our critical value so we
decide to reject our null hypothesis.
People’s happiness depends on the weather.