This activity introduces students to the concept of continuity of a function at a point through three steps. In the first step, students are asked to determine if a machine producing antibiotic pills can be accurately adjusted given allowable errors in the detected amount of medication in blood. In the second step, students experiment with smaller allowable errors to see if a suitable machine accuracy can always be found. In the third step, a new formula revealing less detected medication above a certain amount leads to failure to find a suitable accuracy, highlighting discontinuity as the cause.
A decision tree is a guide to the potential results of a progression of related choices. It permits an individual or association to gauge potential activities against each other dependent on their costs, probabilities, and advantages. They can be utilized either to drive casual conversation or to outline a calculation that predicts the most ideal decision scientifically.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
Unsteady MHD Flow Past A Semi-Infinite Vertical Plate With Heat Source/ Sink:...IJERA Editor
In the present paper a numerical attempt is made to study the combined effects of heat source and sink on unsteady laminar boundary layer flow of a viscous, incompressible, electrically conducting fluid along a semiinfinite vertical plate. A magnetic field of uniform strength is applied normal to the flow. The governing boundary layer equations are solved numerically, using Crank-Nicolson method. Graphical results of velocity and temperature fields, tabular values of Skin-friction and Nusselt are presented and discussed at various parametric conditions. From this study, it is found that the velocity and temperature of the fluid increase in the presence of heat source but they decrease in the presence of heat absorption parameter.
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.
A decision tree is a guide to the potential results of a progression of related choices. It permits an individual or association to gauge potential activities against each other dependent on their costs, probabilities, and advantages. They can be utilized either to drive casual conversation or to outline a calculation that predicts the most ideal decision scientifically.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
Unsteady MHD Flow Past A Semi-Infinite Vertical Plate With Heat Source/ Sink:...IJERA Editor
In the present paper a numerical attempt is made to study the combined effects of heat source and sink on unsteady laminar boundary layer flow of a viscous, incompressible, electrically conducting fluid along a semiinfinite vertical plate. A magnetic field of uniform strength is applied normal to the flow. The governing boundary layer equations are solved numerically, using Crank-Nicolson method. Graphical results of velocity and temperature fields, tabular values of Skin-friction and Nusselt are presented and discussed at various parametric conditions. From this study, it is found that the velocity and temperature of the fluid increase in the presence of heat source but they decrease in the presence of heat absorption parameter.
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.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
A Hybrid Immunological Search for theWeighted Feedback Vertex Set ProblemMario Pavone
In this paper we present a hybrid immunological inspired algorithm (HYBRID-IA) for solving the Minimum Weighted Feedback Vertex Set (MWFVS) problem. MWFV S is one of the most interesting and challenging combinatorial optimization problem, which finds application in many fields and in many real life tasks. The proposed algorithm is inspired by the clonal selection principle, and therefore it takes advantage of the main strength characteristics of the operators of (i) cloning; (ii) hypermutation; and (iii) aging. Along with these operators, the algorithm uses a local search procedure, based on a deterministic approach, whose purpose is to refine the solutions found so far. In order to evaluate the efficiency and robustness of HYBRID-IA several experiments were performed on different instances, and for each instance it was compared to three different algorithms: (1) a memetic algorithm based on a genetic algorithm (MA); (2) a tabu search metaheuristic (XTS); and (3) an iterative tabu search (ITS). The obtained results prove the efficiency and reliability of HYBRID-IA on all instances in term of the best solutions found and also similar performances with all compared algorithms, which represent nowadays the state-of-the-art on for MWFV S problem.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.1: Measures of Center
A Hybrid Immunological Search for theWeighted Feedback Vertex Set ProblemMario Pavone
In this paper we present a hybrid immunological inspired algorithm (HYBRID-IA) for solving the Minimum Weighted Feedback Vertex Set (MWFVS) problem. MWFV S is one of the most interesting and challenging combinatorial optimization problem, which finds application in many fields and in many real life tasks. The proposed algorithm is inspired by the clonal selection principle, and therefore it takes advantage of the main strength characteristics of the operators of (i) cloning; (ii) hypermutation; and (iii) aging. Along with these operators, the algorithm uses a local search procedure, based on a deterministic approach, whose purpose is to refine the solutions found so far. In order to evaluate the efficiency and robustness of HYBRID-IA several experiments were performed on different instances, and for each instance it was compared to three different algorithms: (1) a memetic algorithm based on a genetic algorithm (MA); (2) a tabu search metaheuristic (XTS); and (3) an iterative tabu search (ITS). The obtained results prove the efficiency and reliability of HYBRID-IA on all instances in term of the best solutions found and also similar performances with all compared algorithms, which represent nowadays the state-of-the-art on for MWFV S problem.
Hospice medical director performance appraisalrosiebrooks030
Hospice medical director job description,Hospice medical director goals & objectives,Hospice medical director KPIs & KRAs,Hospice medical director self appraisal
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
The University of Maine at Augusta Name ______.docxchristalgrieg
The University of Maine at Augusta Name: ________________________
Mathematics Department Date: _________________________
MAT 280 F16 Location:______________________
MAT 280
Exam 1 Chapter 1.1-1.6
Please answer the following questions. Part credit is possible if the work indicates an understanding of
the objective under investigation. Students may use their laptops, tablets, textbooks, notes, calculators
and scrap paper. If used, scrap paper should be turned in with the exam. Students may not use smart
phones. If available homework should be turned in with the exam.
Time: 2:45 If staff is available, extra time is permitted.
1. Find a proposition with the given truth table.
p q ?
T T F
T F T
F T T
F F T
2. Write the truth table for the proposition (r q) (p → r). Use as many columns as necessary.
Label each column.
p Ans: p q r
T T T
T T F
T F T
T F F
F T T
F T F
F F T
F F F
Name: ________________________
3. Find a proposition using only pq and the connective V with the given truth table.
p
q
?
T T F
T F F
F T F
F F T
4. Determine whether p (q r) is equivalent to q (p r). Use as many columns as necessary.
Label each used column. Credit will only be when a completed truth table accompanies the answer.
p Ans: p q r
T T T
T T F
T F T
T F F
F T T
F T F
F F T
F F F
Name: ________________________
5. Write a proposition equivalent to ( p q) using only pq and the connective . . Support your
answer with a truth table. If necessary, insert columns in the truth table below to support your
answer
p
q
T T
T F
F T
F F
6. Prove that q p and its contrapositive are logically equivalent. If they are not equivalent, explain
why. Do the same for its inverse.
p
q
p
q
T T T T
T F T F
F T F T
F F F F
7. In the questions below write the statement in the form “If …, then ….”
a. Whenever the temperature drops below 35 degrees, children should wear boots
during recess.
b. You have completed your program’s requirements only if you are eligible to graduate.
Name: ________________________
8. Write the contrapositive, converse, and inverse of the following:
If I have a valid passport, I will be able to travel to Cuba.
.
a. Contrapositive:
b. Converse:
c. Inverse:
9. How many rows are required to show the truth table for the following compound proposition?
(q r ) → (p s) ...
Directions The purpose of Project 8 is to prepare you for the final.docxeve2xjazwa
Directions: The purpose of Project 8 is to prepare you for the final, comprehensive exam and is set up EXACTLY the same. Questions 1 and 2 are not graded in this exercise, but are on the final. Be sure to answer them still so you can receive feedback. Once done with these, move into the calculation questions.
Be advised that you will need to decide which type of test to use in most of the problems. Please write out all pertinent information for each of the 4 steps of hypothesis testing. For the calculations, you only need to provide the values of all statistics for that test. There is no need to show work.
List the four Steps of the Hypothesis test:
Step 1 –
Step 2 –
Step 3 –
Step 4 –
This semester we have discussed the following statistical analyses.
Z-test
One-Sample
t
-test
Independent Groups
t
-test
Repeated Measures
t
-test
One-Way ANOVA
Repeated Measures ANOVA
Correlation
When do you use them? Please type your answer in the Test Used column.
ơ is given
µ is given
Groups Compared
Test Used
No
No
Looks at the same group at 2 different times or across two different conditions
Yes
Yes
Sample against population
Examines the degree to which two variables relate to one another
No
No
Looks at the same group at 2 or more times or across 2 or more conditions
No
No
Examines mean differences between two different groups
No
Yes
Sample against population
No
No
Examines mean differences between 2 or more groups
1. A researcher for a cereal company wanted to demonstrate the health benefits of eating oatmeal. A sample of 9 volunteers was obtained and each participant ate a fixed diet without any oatmeal for 30 days. At the end of the 30-day period, cholesterol was measured for each individual. Then the participants began a second 30-day period in which they repeated exactly the same diet except that they added 2 cups of oatmeal each day. After the second 30-day period, cholesterol levels were measured again and the researcher recorded the difference between the two scores for each participant. For this sample, cholesterol scores average M = 16 points lower with the oatmeal diet with SS = 538 for the difference scores.
10 points
·
Are the data sufficient to indicate a significant change in cholesterol level? Use a two-tailed test with α = .01.
·
Compute r
2
to measure the size of the treatment effect.
2. One possible explanation for why some birds migrate and others maintain year round residency in a single location is intelligence. Specifically, birds with smaller brain, relative to their body size, are not simply smart enough to find food during the winter and must migrate to warmer climates where food is easily available. Birds with bigger brains, on the other hand, are more creative and can find food even when the weather turns harsh. Following are hypothetical data similar to the actual results. The numbers represent relative brain size for the individual birds in each sample.
10 points
Non-Migrating
S.
Name________________________________
PSU ID_____________________________
STAT 100 Lesson 13 Assignment
Answer the following questions and submit for grading. Each question or part of a question is worth 1 point except: 1, 2A, 2B, 2C, 3E, 4E, 5D, & 6C are worth 2 points; 7- 7 is worth 6 points.
1.
An ESP experiment is done in which a participant guesses which of 8 cards the researcher has randomly picked, where each card is equally likely to be selected. This is repeated for 200 trials. The null hypothesis is that the subject is guessing, while the alternative is that the subject has ESP and can guess at higher than the chance rate. Write out the type 1 and type 2 errors in terms of this problem.
2.
For each of the following, write out the null and alternative hypotheses. Also identify what type of data is found. Refer to the information inTable 13.1.
a.
Do female students, on the average, have a higher GPA?
b.
Is there a linear relationship between height and weight?
c.
Is there a difference in the proportions of male and female college students who smoke?
3.
A Researcher asked a sample of 50 1st grade teachers and a sample of 50 12th grade teachers how much of their own money they spent on school supplies in the previous school year. The researcher wanted to see if the mean spending at one grade level is different from the mean spending at another grade level.
Two-sample T for 1st_Grade vs 12th_Grade
N Mean StDev SE Mean
1st_Grade 50 111.2 88.9 13
12th_Grade 50 49.5 38.8 5.5
Difference = mu 1st_Grade - mu 12th_Grade
Estimate for difference: 61.7
95% CI for difference: (34.3, 89.1)
T-Test of difference = 0 (vs not =): T-Value = 4.50 P-Value = 0.000 DF = 66
Figure A.1.
a.
What is the response variable in this problem?
b.
What is the explanatory variable in this problem?
c.
What type of variable is the response variable? categorical or measurement
d.
What is the appropriate population value for this problem? population mean or population proportion
e.
Write out the null and alternative hypotheses in terms of the appropriate population value.
f.
On the output in Figure A.1 the test statistic is 4.50. Use this test statistic to write a one-sentence interpretation of the p-value in terms of this problem.
g.
What conclusion can be made in terms of this problem? Why?
h.
Using the 95% confidence interval of the difference as your basis, do you think practical significance has been found with regard to the mean amount spent when comparing 1st grade teachers to 12th grade teachers? Include reasoning. Hint: Refer to Example 13.10.
4.
A survey asked 2000 people whether or not they frequently exceed the speed limit. The collected data is summarized in the following contingency table. The goal is to determine if there is a difference in the population proportion that say “yes” when comparing those who are under 40 years in age to those who are at least 40 years in age.
.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In the t test for independent groups, ____.we estimate µ1 µ2.docxbradburgess22840
In the t test for independent groups, ____.
we estimate µ1 µ2
we estimate 2
we estimate X1-X2
df = N 1
Exhibit 14-1
A professor of women's studies is interested in determining if stress affects the menstrual cycle. Ten women are randomly sampled for an experiment and randomly divided into two groups. One of the groups is subjected to high stress for two months while the other lives in a relatively stress-free environment. The professor measures the menstrual cycle (in days) of each woman during the second month. The following data are obtained.
High stress
20
23
18
19
22
Relatively stress free
26
31
25
26
30
Refer to Exhibit 14-1. The obtained value of the appropriate statistic is ____.
tobt = 4.73
tobt = 4.71
tobt = 3.05
tobt = 0.47
Refer to Exhibit 14-1. The df for determining tcrit are ____.
4
9
8
3
Refer to Exhibit 14-1. Using = .052 tail, tcrit = ____.
+2.162
+2.506
±2.462
±2.306
Refer to Exhibit 14-1. Using = .052 tail, your conclusion is ____.
accept H0; stress does not affect the menstrual cycle
retain H0; we cannot conclude that stress affects the menstrual cycle
retain H0; stress affects the menstrual cycle
reject H0; stress affects the menstrual cycle
Refer to Exhibit 14-1. Estimate the size of the effect. = ____
0.8102
0.6810
0.4322
0.5776
A major advantage to using a two condition experiment (e.g. control and experimental groups) is ____.
the test has more power
the data are easier to analyze
the experiment does not need to know population parameters
the test has less power
Which of the following tests analyzes the difference between the means of two independent samples?
correlated t test
t test for independent groups
sign test
test of variance
If n1 = n2 and n is relatively large, then the t test is relatively robust against ____.
violations of the assumptions of homogeneity of variance and normality
violations of random samples
traffic violations
violations by the forces of evil
Exhibit 14-3
Five students were tested before and after taking a class to improve their study habits. They were given articles to read which contained a known number of facts in each story. After the story each student listed as many facts as he/she could recall. The following data was recorded.
Before
10
12
14
16
12
After
15
14
17
17
20
Refer to Exhibit 14-3. The obtained value of the appropriate statistic is ____.
3.92
3.06
4.12
2.58
Refer to Exhibit 14-3. What do you conclude using = 0.052 tail?
reject H0; the class appeared to improve study habits
retain H0; the class had no effect on study habits
retain H0; we cannot conclude that the class improved study habits
accept H0; the class appeared to improve study habits
Which of the following is (are) assumption(s) underlying the use of the F test?
the raw score populations are normally distributed
the variances of the raw score populations are the same
the mean of the populations differ
the raw score popul.
1. 3.1 Activity: Introduction to continuity of a function at a point
Content of the activity:
This activity introduces the students to the notion of continuity (and dis-continuity)
of a function at a point. Through the study of a problem, stu-dents
are leaded to an informal verbal representation of the continuity
definition, in a way that it enables a smooth passing to the algebraic rep-resentation
1
of the ε-δ definition.
Goals of the activity:
This activity intends:
• To introduce students intuitively to the ε-δ definition.
• To familiarize students to the “find a suitable δ whenever given an ε”
kind of process, within a dynamic geometry environment, free of al-gebraic
manipulation.
• To connect different representations of the concept of continuity.
• To realize the ε-δ definition's failure as a jump on the graph.
The rationale of the activity:
The ε-δ definition of continuity presents serious difficulties. Through this
activity we seek to introduce students to continuity of function at a point,
in an intuitive way free of algebraic manipulations.
The activity is divided into three steps. In the first step, through a prob-lem,
some concrete values of epsilon are given and the student is asked to
find the corresponding δ. In the second step ε becomes a parameter. In
the third step the student connects discontinuity to the existence of a
jump in the graph. In each step the questions are first implemented
graphically and then algebraically.
Activity and Curriculum:
Depending on the level of the target group, the activity can be taught in
two different contexts. It can either be used as a tool introduction to the
idea of continuity or –under suitable adjustment - can be also used as an
intuitive introduction to the main idea of continuity
Stages 1 and 2 can be discussed in an hour lesson. Stage 3 can be dis-cussed
in a separate one-hour lesson, combined with examples which will
2. dispel certain misunderstandings that could arise (for example δ we find
is unique, “I do not lift up my pen while drawing a continuous function”).
The concept of continuity appears independent from the limit concept
and it does not presuppose the limit understanding.
2
3. 3
3.1 Worksheet Analysis
Introduction to continuity of a function at a point
FIRST STEP
A chemical and health care corporation is about to produce a new antibi-otic
pill which will be able to cure a certain disease.
It is known that the pill should contain 3gr in order to provide the patient
with the right dose of medicine.
The function f (x) = x +1 −1gives the amount f (x) of the antibiotic
which is detected in the blood, when a patient gets a pill with x mgr of
the substance.
According to current research results, if the antibiotic detected in blood is
equal or less than 0.8gr, there will be no effect on the patient’s health and
if it is more than 1.2gr the patient is in danger due to overdose.
Q1: Which amount of medicine is presumably detected in the pa-tient’s
blood?
f (3) = 1
Q2: Which is the allowed error ε of divergence of the detected
amount of medicine from the ideal value, such that the pills stay safe
and effective?
The number ε=0.2 sets a boundary (1− 0.2,1+ 0.2) = (0.8,1.2) on the ac-cepted
quantities of antibiotic around f(3)=1. We will call ε the allowed
error.
Some questions that familiarise students with the allowed error could be useful since
epsilon plays a vital role throughout the activity. It is very useful to introduce the terms
“allowed error” and “precision” in our language as soon as possible.
The machine available to the corporation that produces pills of t=3gr has
an accuracy level adjusted to δ=1.1
This means that although the machine is programmed to produce 3gr
pills, the pills are not always 3gr but their weights vary between 3 – 1.1gr
and 3 + 1.1gr.
4. Q3: Is the machine suitably adjusted to produce safe and effective
pills?
• Open EucliDraw file 3.1.1a.activity.en.euc and try to answer question
4
E3 using it.
• In this environment we get the graph of f(x). By changing ε we can
alter the allowed error, and by changing δ we can alter machine’s ac-curacy.
• The magnification window allows us to focus in a neighbourhood of
point (3,1).
The student is expected to observe that there are some parts of the graph which lie in-side
the delta zone, but outside the epsilon zone. If we set the machine to produce pills,
some of them will be ineffective and some dangerous.
The machine can be adjusted to another accuracy level.
Q4: Can the machine be adjusted in order to produce pills within the
allowed error?
We expect the students to change delta in order to fix the problem of the previous ques-tion.
The students should work from both sides in order to find a delta of about 0.8.
Some discussion may be held in order to emphasize that δ is not unique.
Every δ less than the one already found can work fine.
Another point that needs extra attention is the fact that we are not interested in finding a
better δ.
The results of a new research indicated that the error level should be re-duced
to ε=0.1.
Q5: Is there any problem with this change in pill production?
Does the accuracy level have to be adjusted anew?
Each student's answer depends on which δ has given in the preceding question.
For example if a student has given δ=0.8 before, the machine after the change of ε does
not work right. If a student's δ was 0.2, then the machine still does not have a problem.
A δ of 0.3 or less does work.
SECOND STEP
Q1: If the results of another research suggest that ε should be lessen
more, will the corporation be always able to adjust suitably the ma-chine?
5. Open 3.1.1.activity.en.euc file and check whether we can always find an
adequate δ>0, as ε gets smaller and smaller. Experiment graphically.
Display the Red/Green region and describe what it means for the function
graph to lay in the green, red or white region.
This question offers the student the opportunity for a transition from a visual representa-tion
5
to a verbal one.
The green region represents the allowed (x,f(x)).
Algebraically, the green region is the set of those points (x,f(x)) of the plane for which,
|x-5|<δ and |f(x)-f(5)|<ε
When some part of the function lies in the red region then there is a problem in the pill
production. The machine is programmed in such a way, that it may produce pills which
are ineffective (red part bellow the green region), or harmful (red part above the green
region).
No point in the white region can be produced by the current state of the machine.
If necessary, use the magnification window.
In this question, the student plays an ε-δ game giving less and less epsi-lons.
When ε lessens enough the zones are indistinguishable, so the inter-est
moves to the the magnification window.
Q2: In each of the following sentences, fill in the blanks with the cor-rect
colours:
a. Whenever we are given an ε, we can find a δ,
such that the function does not lie in the .....red........ region.
b. For every ε we can find a δ such that for every x in the accuracy limits
of the machine, (x,f(x)) lies in the .......green........... region.
Q3: Write sentence b, replacing the colors with algebraic relations.
The teacher initially leads the students in a verbal description of the result like :
“For every arithmetic value of the error, there is a machine accuracy so that if x lies
inside the bounds of the allowed precision, f(x) lies inside the bounds of the allowed
error”.
Hereupon he can ask them to demonstrate the description above with the use of mathe-matical
symbols. That is
“For every ε we can find a δ such that for every x ∈(3 −δ , 3 +δ ) we have:
f (x)∈(1−ε ,1+ε ) ”.
6. Or
For every ε we can find a δ such that for every x such that | x − 3 |< δ ,
we have | f (x) −1 |< ε .
Under suitable circumstances the last proposition can drive to the introduction of ε-δ
definition of continuity in the case of a random function.
THIRD STEP
Another research shows that the formula given by the function above
which gives the quantity of drug traced in blood works well for values
less than 3mgr. When values of x exceed or equal 3 it shows 0.06mgr
less than the real amount detected in blood.
Q1. Find out the formula of the new function that gives the real
quantity of drug detected in blood, taking into account the results of
the last research.
6
⎪⎧ + − < = ⎨
⎩⎪ ≥
g x x x
( ) 1 1 , 3
x
.................. , 3
The second branch should be x +1 − 0.94
Q2: Can the machine be adjusted properly to produce effective and
safe pills?
Which δ should work for ε=0.1?
E.g. δ=0.1 works.
Open 3.1.2.activity.en.euc file
Give your answer by giving suitable values δ.
If needed use the magnification window.
ε is chosen in a way that a δ can be found.
Q3: What will happen if ε is reduced to 0.06? Can you find an ade-quate
δ?
No δ can make the machine produce only effective pills since whenever a pill is less
than 3mgr the quantity detected will be ineffective.
Q4: What causes this failure?
7. This question may lead the class to some very interesting discussion.
Some idea that can lead to the discontinuity concept is the fact that the gap which exists
in the graph of f(x) is what causes the failure in finding δ, as long as the points g(x)
around 3 appear distanced from each other.
Q5. In each of the following sentences, fill in the blanks with the cor-rect
7
colours:
a. For a given an ε, no δ could prevent the function from lying in the
..........red............ region.
b. There is an ε, such that for every δ, there is an x in the accuracy field
of the machine such that (x,g(x)) lies in the .....red......... region.
Q6. Write sentence Q5b using algebraic relationships instead of col-ours.
There is an ε such that for every δ, there is an x ∈ (3 −δ , 3 +δ ) , such that
g(x)∉(1−ε ,1+ε ) . Or There is an ε such that for every δ there is an x such
that | x − 3 |< δ , for which we have | g(x) −1 |≥ ε .
Last proposition in correspondence to the last one of the second step, can lead to the
generalization of the negation of the definition of continuity for a random function.
8. 8
3.1 Worksheet
Introduction to continuity of a function at a point
FIRST STEP
A chemical and health care corporation is about to produce a new antibi-otic
pill which will be able to cure a certain disease.
It is known that the pill should contain 3gr in order to provide the patient
with the right dose of medicine.
The function f (x) = x +1 −1gives the amount f (x) of the antibiotic
which is detected in the blood, when a patient gets a pill with x mgr of
the substance.
According to current research results, if the antibiotic detected in blood is
equal or less than 0.8gr, there will be no effect on the patient’s health and
if it is more than 1.2gr the patient is in danger due to overdose.
Q1: Which amount of medicine is presumably detected in the pa-tient’s
blood?
Q2: Which is the allowed error ε of divergence of the detected
amount of medicine from the ideal value, such that the pills stay safe
and effective?
The number ε=0.2 sets a boundary (1− 0.2,1+ 0.2) = (0.8,1.2) on the ac-cepted
quantities of antibiotic around f(3)=1. We will call ε the allowed
error.
9. The machine available to the corporation that produces pills of t=3gr has
an accuracy level adjusted to δ=1.1
This means that although the machine is programmed to produce 3gr
pills, the pills are not always 3gr but their weights vary between 3 – 1.1gr
and 3 + 1.1gr.
Q3: Is the machine suitably adjusted to produce safe and effective
pills?
• Open EucliDraw file 3.1.1a.activity.en.euc and try to answer question
9
E3 using it.
• In this environment we get the graph of f(x). By changing ε we can
alter the allowed error, and by changing δ we can alter machine’s ac-curacy.
• The magnification window allows us to focus in a neighbourhood of
point (3,1).
The machine can be adjusted to another accuracy level.
Q4: Can the machine be adjusted in order to produce pills within the
allowed error?
The results of a new research indicated that the error level should be re-duced
to ε=0.1.
Q5: Is there any problem with this change in pill production?
Does the accuracy level have to be adjusted anew?
10. SECOND STEP
Q1: If the results of another research suggest that ε should be lessen
more, will the corporation be always able to adjust suitably the ma-chine?
Open 3.1.1.activity.en.euc file and check whether we can always find an
adequate δ>0, as ε gets smaller and smaller. Experiment graphically.
Display the Red/Green region and describe what it means for the function
graph to lay in the green, red or white region.
If necessary, use the magnification window.
In this question, the student plays an ε-δ game giving less and less epsi-lons.
10
When ε lessens enough the zones are indistinguishable, so the inter-est
moves to the the magnification window.
Q2: In each of the following sentences, fill in the blanks with the cor-rect
colours:
a. Whenever we are given an ε, we can find a δ,
such that the function does not lie in the .....red........ region.
b. For every ε we can find a δ such that for every x in the accuracy limits
of the machine, (x,f(x)) lies in the .......green........... region.
Q3: Write sentence b, replacing the colors with algebraic relations.
11. 11
THIRD STEP
Another research shows that the formula given by the function above
which gives the quantity of drug traced in blood works well for values
less than 3mgr. When values of x exceed or equal 3 it shows 0.06mgr
less than the real amount detected in blood.
Q1. Find out the formula of the new function that gives the real
quantity of drug detected in blood, taking into account the results of
the last research.
⎪⎧ + − < = ⎨
⎩⎪ ≥
g x x x
( ) 1 1 , 3
x
.................. , 3
Q2: Can the machine be adjusted properly to produce effective and
safe pills?
Which δ should work for ε=0.1?
Open 3.1.2.activity.en.euc file
Give your answer by giving suitable values δ.
If needed use the magnification window.
Q3: What will happen if ε is reduced to 0.06? Can you find an ade-quate
δ?
Q4: What causes this failure?
12. Q5. In each of the following sentences, fill in the blanks with the cor-rect
12
colours:
a. For a given an ε, no δ could prevent the function from lying in the
...................... region.
b. There is an ε, such that for every δ, there is an x in the accuracy field
of the machine such that (x,g(x)) lies in the .............. region.
Q6. Write sentence Q5b using algebraic relationships instead of col-ours.