I am Mrs. Abdul. Muneera, Completed M.Sc. Mathematics from Acharya Nagarjuna University in the year 2004. Presently working as Assistant Professor, Department of Mathematics, Andhra Loyola Institute of Engineering and Technology, Vijayawada. Having a total 15 years of teaching experience in the Engineering field. Qualified APSET in 2017. Total 14 papers published. Four SCOPUS publications, Four UGC publication,s and some international journals. Also Participated and Presented research Articles at various National and International Conferences.
Tuesday 20 October 2020
Saturday 26 September 2020
P&S LESSON PLAN
Andhra
Loyola Institute of Engineering and Technology
Teacher/Instructor:
Mrs. Abdul Muneera
Assistant
Professor
Mathematics
Lesson
Plan for a Day
Term/Semester/Year:
Sem- II Syllabus 2020-21
Subject: Probability and Statistics
Main
Objectives
·
Acquire knowledge in various types of
probability distributions and gain knowledge of modeling in the presence of
uncertainties.
·
Learn properties and nature of
probability distributions
·
Study elementary concepts in sampling
theory, and the use of statistical inference in practical data analysis.
·
Aware of principle steps in hypothesis
testing, and use of statistics in decision making.
·
Know how to use computers and/or
calculators for statistical analysis of relationship between/among variables.
·
Obtain Process quality through control
charts, and improve Statistical skills.
Lesson Objectives:
|
Factual |
From this unit student get the knowledge of how to
find the chance or probability of occurrence of an event and improved their
logical and reasoning skills which will be useful further in their
engineering studies. |
|
|
2 |
Conceptual |
They
will get the concept of dealing with problems where they can apply
conditional probability or probability to solve the problems like Baye’s
theorem. |
|
3 |
Procedural |
They
are able to understand the given data and analyse that and apply the formula
to give the result. |
|
4 |
Applied |
They
can use this knowledge of random sample techniques in many competitive exams
like GATE,GRE, Banking services. |
Detailed
Text
Contents/Activities – Lesson 1
|
1 |
Factual |
Student get the knowledge to identify
the sample as a large or small sample and to apply for the give data by using
sampling techniques. |
|
2 |
Conceptual |
They get concept to identify
systematic, cluster and random samples. |
|
3 |
Procedural |
They are able to understand the given
data and analyse that and apply the formula to give the result. |
|
4 |
Applied |
They can use this knowledge of random
sample techniques in many competitive exams like GATE,GRE, Banking services. |
Schedule and Sequence:
Day
Plan for Lesson 1 – Random Variables and Distributions
Infotech Lesson 1– Total Classes 12
|
Total
Classes -12 |
Topic |
Objectives |
Before
Class - Videos, e-Books, Case studies |
In-Class
– Activities, Quiz (Micro
teaching) |
Post
Class - Assignment, Discussion Forum |
|
Day-1 |
Introduction to Probability concepts |
The probability estimate
is computed using mathematical equations that manipulate the data to
determine the likelihood of an independent event occurring |
https://www.slideshare.net/getyourcheaton/intro-to-probability-57576766 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
Day-2 |
Introduction to conditional
Probability |
To apply the basic rules and theorems
of probability theory such as Baye’s theorem to determine probabilities that
help to solve engineering problems. |
He
has 15 socks that are black with small stripes and 15 socks that are plain
black. Andrew has to pull out one sock; then the second sock Andrew pulls out
must match the first sock. Andrew having two matching socks is dependent upon
which sock he pulls out first and which sock he pulls out second. In this
case, you have two events to consider. This is an example of conditional
probability, which is probability of a second event happening given that
a first event has already occurred. This particular case of conditional
probability deals with dependent events, which is when one event
influences the outcome of another event in a probability scenario. |
https://www.slideshare.net/MariaRominaAngustia/conditional-probability-66117183 |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
Day -3 |
Baye’s Theorem and Problems |
To understand about Bayes’ Theorem
functioning. |
|
https://www.slideshare.net/BalajiP6/probability-basics-and-bayes-theorem |
To apply the basic rules and theorems
of probability theory such as Baye’s Theorem to determine probabilities that
help to solve engineering problems |
|
Day - 4 DAY 5 |
Baye’s Theorem and Problems |
To apply the basic rules and theorems
of probability theory such as Baye’s theorem to determine probabilities that
help to solve engineering problems |
https://www.slideshare.net/getyourcheaton/intro-to-probability-57576766 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5
min |
To apply the basic rules and theorems
of probability theory such as Baye’s Theorem to determine probabilities that
help to solve engineering problems. |
|
DAY 6 |
Binomial Distributions |
The
binomial distribution model allows us to compute the probability of observing
a specified number of "successes" when the process is repeated a
specific number of times . |
https://www.slideshare.net/SushmitaR2/binomial-distribution-171208579 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
DAY 7 |
Binomial Distributions |
He will get the knowledge o f how to
apply binomial distribution and in which situation it can be applicable. |
binomial
distribution model is an important probability model that is used when there
are two possible outcomes (hence "binomial"). In a situation in
which there were more than two distinct outcomes, a multinomial probability
model might be appropriate, but here we focus on the situation in which the
outcome is dichotomous. |
https://www.slideshare.net/TayabAli/binomial-probability-distributions-ppt |
https://nptel.ac.in/courses/111/104/111104032/ |
|
DAY 8 |
Poison distributions |
The Poisson
distribution may be useful to model events such as ·
The number of meteorites greater than
1 meter diameter that strike Earth in a year |
https://www.slideshare.net/jillmitchell8778/poisson-lecture |
https://www.slideshare.net/abdulkader28696/poisson-distribution-assign |
Because of this application, Poisson distributions are used
by businessmen to make forecasts about
the number of customers or sales on certain days or seasons of the year. In
business, overstocking will sometimes mean losses if the goods are not sold. |
|
DAY 9 |
Poison distributions |
·
The number of patients arriving in an
emergency room between 10 and 11 pm ·
The number of laser photons hitting a
detector in a particular time interval |
https://www.slideshare.net/abdulkader28696/poisson-distribution-assign |
A textbook store rents an average of 200 books every
Saturday night. Using this data, you can predict
the probability that more books will sell (perhaps 300
or 400) on the following Saturday nights. Another example is the number of
diners in a certain restaurant every day. If the average number of diners for seven days is 500, you can predict the
probability of a certain day having more customers. |
|
|
DAY 10 |
Normal Distributions |
A normal
distribution is the proper term for a probability bell curve. In a normal
distribution the mean is zero and the standard deviation is 1. It has zero
skew and a kurtosis of 3.Normal distributions are symmetrical, but not all
symmetrical distributions are normal. |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 11 |
Normal Distributions |
To understand about normal
distributons. |
https://www.slideshare.net/dsaadeddin/normal-distribution-77299816 |
The normal distribution is the most important probability
distribution in statistics because
it fits many natural phenomena. For example, heights, blood pressure,
measurement error, and IQ scores follow the normal distribution. It is also
known as the Gaussian distribution and the bell curve. |
|
|
DAY 12 |
Rivision |
|
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
||
|
Revision |
|||||
Contents/Activities –
Lesson 2
|
1 |
Factual |
The moment-generating
function is the expectation of a function of the
random variable, it can be written as: For a discrete probability mass function,
For a continuous probability density function |
|
2 |
Conceptual |
They get concept to identify
systematic, cluster and random samples. |
|
3 |
Procedural |
They are able to understand the given
data and analyse that and apply the formula to give the result. |
|
4 |
Applied |
They can use this knowledge of random
sample techniques in many competitive exams like GATE,GRE, Banking services. |
Schedule and Sequence:
Day
Plan for Lesson 2 – Moment and
Generating Functions
Infotech Lesson 2 – Total Classes 10
|
Session/week/ Module -1 Total Classes -10 |
Topic |
Objectives |
Before Class - Videos, e-Books, Case
studies |
In-Class – Activities, Quiz (Micro teaching) |
Post Class - Assignment, Discussion
Forum |
|
Day-1 |
Mathematical Expectation |
The mathematical
expectation of an indicator variable can be zero if there is no
occurrence of an event A, and the mathematical expectation of
an indicator variable can be one if there is an occurrence of an event A.
Thus, it is a useful tool to find the probability of event
A. |
The concept
of MGF is an elegant one. The MGF packages all the moments for a random
variable into one simple expression. It provides a unique characterization of
the distribution of a random variable. By finding the first and second
moment, one can easily calculate the mean and the variance, and so on. More
importantly, it can be used to prove the equivalence of two probability
distributions. |
||
|
Day-2 |
Properties |
|
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
Day -3 |
Moment Generating Functions |
moments of a
function are quantitative measures related to
the shape of the function's graph. ... If the function represents
mass, then the zeroth moment is the total mass, the
first moment divided by the total mass is the center of
mass, and the second moment is the rotational inertia. |
In most
basic probability theory courses your told moment generating functions
(m.g.f) are useful for calculating the moments of a random variable. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
Day - 4 |
Standard distsributions |
He will get the knowledge of
distributions. |
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 5 |
Moments of standard distributions |
To understand about moments, and
standard distributions |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 7 |
M.G.F. Binomial distsirbtions |
He will get the knowledge of
distributions. |
https://www.slideshare.net/eddyboadu/moment-generating-function |
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
DAY 8 |
M.G.F. of Poison distributions |
To understand about moments, and
standard distributions |
https://www.slideshare.net/mathscontent/moment-generating-functions-3209542 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
DAY 9 |
M.G.F. Normal distributions |
He will get the knowledge of moment
generating functions of normal distributions. |
https://www.slideserve.com/niveditha/moment-generating-functions |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
DAY 10 |
Properties |
|
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
|
|||||
Contents/Activities –
Lesson 3
|
1 |
Factual |
In statistics, quality assurance, and survey methodology, sampling is the selection
of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the
whole population. |
|
2 |
Conceptual |
The primary
goal of sampling is to get a representative sample, or a
small collection of units or cases from a much larger collection or
population, such that the researcher can study the smaller group and produce
accurate generalizations about the larger group. |
|
3 |
Procedural |
It allows us
to get near-accurate results in much lesser time. When you use proper
methods, you are likely to achieve higher level of accuracy by
using sampling than without using sampling in some cases due to reduction in
monotony, data handling issues etc. |
|
4 |
Applied |
A large
number of analyses is carried out, e.g., for process control, product quality
control for consumer safety, and environmental control purposes. The sampling theory developed by Pierre Gy, together with
the theory of stratified sampling, can be used to audit and optimize
analytical measurement protocols. |
chedule and Sequence:
Day
Plan for Lesson 3 – Sampling Theory
Infotech Lesson 3 – Total Classes 10
|
Session/week/ Module -1 Total Classes -10 |
Topic |
Objectives |
Before Class - Videos, e-Books, Case
studies |
In-Class – Activities, Quiz (Micro teaching) |
Post Class - Assignment, Discussion
Forum |
|
Day-1 |
Population and samples |
The major objective of
sampling theory and statistical inference is to provide estimates of unknown
parameters from sample statistics. |
A sample is
a set of data collected and/or selected from a population by a defined
procedure. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
Day-2 |
Sampling Distribution |
Explain the concepts of estimation, point estimates, confidence level,
and confidence interval Calculate and interpret confidence intervals for means |
population: a group of units (persons, objects,
or other items) enumerated in a census or from which a sample is drawn |
https://www.youtube.com/watch?v=KeHwAvsoOz0 https://www.youtube.com/watch?v=KeHwAvsoOz0 |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
Day -3 |
Proportion sums |
Describe the concept of risk and how to reduce it Calculate and interpret confidence intervals for proportion |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
||
|
Day - 4 |
Proportion difference of means |
unbiased
(representative) sample is a set of objects chosen from a complete sample
using a selection process that does not depend on the properties of the
objects. |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 5 |
Sampling distribution of variance |
He will understood how to variate the
things and to find the variance between the items. |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 7 |
Point estimators for means |
Point
estimation, in statistics, the
process of finding an approximate value of some parameter—such as the mean (average)—of
a population from random samples of the population. . |
Point
estimation gives us a
particular value as an estimate of the population parameter.
... Interval estimation gives us a range of values which is
likely to contain the population parameter. This interval is
called a confidence interval |
https://www.slideshare.net/ShubhamMehta5/point-and-interval-estimation-56832707 |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
DAY 8 |
Point estimator for proportions |
To
understand about point estimation. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
||
|
DAY 9 |
Interval estimators for means |
To understand about interval
estimation |
|
https://www.slideshare.net/SimarpreetSingh16/types-of-estimates |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
DAY 10 |
Interval estimator for proportions |
To understand about interval
estimation |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
Contents/Activities – Lesson 4
|
1 |
Factual |
Sample size
determination is the act of
choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of
any empirical study in which the goal is to make inferences about a population from a sample. |
|
2 |
Conceptual |
Sampling is a tool that is used to indicate how
much data to collect and how often it should be collected. This tool defines
the samples to take in order to quantify a system, process, issue, or problem.
... The sample, the slice of bread, is a subset or a part of the
population. |
|
3 |
Procedural |
Hypothesis
testing is a formal procedure for investigating our ideas about the world
using statistics. It is most often used by scientists to test specific predictions,
called hypotheses, that arise from theories. |
|
4 |
Applied |
Application of hypothesis testing will
allow manufacturers to better understand quality data and provide guidance to
production control. |
Schedule and Sequence:
Day
Plan for Lesson 4 –Test of Hypothesis
Infotech Lesson 4– Total Classes 12
|
Session/week/ Module -1 Total Classes -12 |
Topic |
Objectives |
Before Class - Videos, e-Books, Case
studies |
In-Class – Activities, Quiz (Micro teaching) |
Post Class - Assignment, Discussion
Forum |
|
Day-1 |
Small samples and Large samples |
The purpose of estimating the appropriate
sample size is to produce studies capable of detecting clinically relevant
differences |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
Day-2 |
Small samples testing of means by Z-
Test |
A z-test is
a statistical test to determine whether two population means
are different when the variances are known and the sample size is large. |
A Z-test is
any statistical
test for which
the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution. Z-test tests the mean of a distribution. |
|
|
|
Day -3 |
Large Samples – Testing of means
t-test |
To understand about sampling means. |
|
||
|
Day - 4 |
Testing of proportions for small
samples by Z-test |
To understand about Z- test. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
||
|
DAY 5 |
Testing of proportions for large samples
by t-test |
A t-test is
a type of inferential statistic used to determine if there is a significant
difference between the means of two groups, which may be related in certain
features |
|
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 7 |
Testing of variance by F-test |
Get the knowledge of F- test of
hypothesis. |
|
The F-test is designed to test if
two population variances are equal. It does this by comparing the ratio of
two variances. |
|
|
DAY 8 |
Type I and Type II errors |
a type I error is the
rejection of a true null hypothesis (also known as a "false
positive" finding or conclusion; example: "an innocent person is
convicted"), while a type II error is the non-rejection
of a false null hypothesis (also known as a "false negative"
finding or conclusion .. |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5
min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
|
DAY 9 |
One tail and two tailed tests |
Our
null hypothesis is that the mean is equal to x. A one-tailed test
will test either if the mean is significantly greater than x
or if the mean is significantly less than x, but not both |
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
The one-tailed test provides more
power to detect an effect in one direction
by not testing the effect in the other direction. |
|
|
DAY 10 |
Testing of attributes by Chi-square
test. |
To know about attributes tests. |
|
The Chi square test is used to compare a group with a value,
or to compare two or more groups, always using categorical data. |
Contents/Activities – Lesson 5
|
1 |
Factual |
Curve
fitting examines the relationship between one
or more predictors (independent variables) and a response variable (dependent
variable), with the goal of defining a "best fit" model
of the relationship. |
|
2 |
Conceptual |
Correlation is a measure of the association between
two variables. This tests the strength of linear association
between two variables. ... Thus data that followed an exponential pattern
would have a Pearson correlation coefficient less than one
(possibly much less), although there is a perfect association. |
|
3 |
Procedural |
Curve fitting is
the process of constructing a curve, or
mathematical function, that has
the best fit to a series of data points, possibly subject to constraints |
|
4 |
Applied |
The most
commonly used techniques for investigating the relationship
between two quantitative variables are correlation and
linear regression. Correlation quantifies the
strength of the linear relationship between a pair of variables,
whereas regression expresses the relationship in the form of
an equation. |
Schedule and Sequence:
Day
Plan for Lesson 5 – Curve Fitting and Correlation
Infotech Lesson 5– Total Classes 10
|
Session/week/ Module -1 Total Classes -10 |
Topi |
Objectives |
Before Class - Videos, e-Books, Case
studies |
In-Class – Activities, Quiz (Micro teaching) |
Post Class - Assignment, Discussion
Forum |
|
Day-1 |
Define curve
fitting |
Curve
fitting is one of the most powerful and most widely used analysis tools in
Origin. Curve fitting examines the relationship between one or more predictors
(independent variables) and a response variable (dependent variable), with
the goal of defining a "best fit" model of the relationship. |
https://www.slideshare.net/shopnohinami/curve-fitting-53775511 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Learning outcome Curve
Fitting" is the process of constructing a curve or mathematical function
that has the best fit to a series of data points, possibly subject to
constraints. Curves such as parabola and hyperbola are used in architecture
to design arches in buildings. They are known to be theoretically the
strongest form of arches and commonly used in architectural design. Curves
are preferred primarily as an aesthetic choice and at times make a building
into something beautiful in a way rectilinear forms cannot |
|
Day-2 |
Fitting of a Straight line |
. Curve fitting[1][2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points,[3] possibly subject to constraints.[4][5 |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
||
|
Day -3 |
Fitting of Second degree parabola |
Curve
fitting is the process of finding the curve that best approximates a set of
points from within a set of curves. The least squares method does this by
minimizing the sum of the squares of the differences between the actual and
predicted values. The linear least squares method, which is used here,
restricts the set of curves to linear combinations of a set of basis
functions. |
|
|
|
|
Day - 4 |
Fitting of exponential curve |
|
https://www.youtube.com/watch?v=s94k4H6AE54&list=PLU6SqdYcYsfL1Mrdj7bs2A6bQOU7FMqKX&index=2 |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 5 |
Fitting of Power curve Binomial Distributions |
They came to know about the uses of
power curve fitting. |
|
|
|
|
DAY 7 |
Simple correlation |
They will understand how to correlate
the relation between two things. |
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
DAY 8 |
Regressions |
To
study the degree of level of correlation. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
||
|
DAY 9 |
Rank correlation |
To understand about rank correlation. |
|
||
|
DAY 10 |
Multiple Regression |
Multiple Linear Regression
fits multiple independent variables |
A unique feature of
Origin's Multiple Linear Regression is Partial Leverage Plots, useful in
studying the relationship between the independent variable and a given
dependent variable: |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
Contents/Activities –
Lesson 6
|
1 |
Factual |
ensuring quality standards by approving
incoming materials, in-process production, and finished products. QCs perform
biological tests (e.g salmonella) and quality tests (e.g. fat) at specified
stages in the production process and keep a record of these results |
|
2 |
Conceptual |
We can apply the concept of Quality
Control to many manufacturing companies like medicine, food, machinery, etc. |
|
3 |
Procedural |
They are able to understand by
studying the data which chart x bar-chart, R-chart, c-Chart.. is
suitable to draw the conclusion by
using the concept of Q.C. |
|
4 |
Applied |
The application of statistical methods
of process control provides a better understanding of the behaviour of any
operation. This is an essential piece of management information that is
required for making smart decisions about process improvements regardless of
the type of process. |
Schedule and Sequence:
Day
Plan for Lesson 6 – Statistical Quality Control Method
Infotech Lesson 6– Total Classes 10
|
Session/week/ Module -1 Total Classes -10 |
Topic |
Objectives |
Before Class - Videos, e-Books, Case
studies |
In-Class – Activities, Quiz (Micro teaching) |
Post Class - Assignment, Discussion
Forum |
|
Day-1 |
Quality control |
A quality control
chart is a graphic that depicts whether sampled products or processes are
meeting their intended specifications and, if not, the degree by which they
vary from those specifications. A common form of the
quality control chart is the X-Bar Chart, where the y-axis on the chart
tracks the degree to which the variance of the tested attribute is
acceptable. |
https://www.slideshare.net/MeenakshiSingh46/control-charts-47720726 |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Students get the knowledge of how to test the
quality of any product what tests we have to apply and how to apply. |
|
Day-2 |
Control Charts |
If a point is out of the control limits, it indicates that
the mean or variation of the process is out-of-control; assignable causes may
be suspected at this point. On the x-bar chart, the y-axis shows the grand
mean and the control limits while the x-axis shows the sample group. |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Students get the knowledge how to
identify the maximum number of disincentive pieces. How to control them. |
|
|
Day -3 |
Mean chart or x bar chart |
To understand about the control
charts. |
Brain storming (5 Min) Text – PPT (30 Min) Students Creative response (10 min) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
|
|
Day - 4 |
Range chart or R-chart |
An industrial process produces a speed
sensor for an ABS electronic control. The sensor specifications require that
the variable X = “impedance” is 30 kohms ±10 kohms. To perform the
statistical control of this process 6 sample sensors are collected every half
hour. With the collected data, a total of 30 samples, the following
information were obtained: ¯x = 30.11 and ˆs¯x = 2.00. a) Construct control charts
to monitor that the population mean and variance remain constant. |
Quality control charts represent a great tool for engineers
to monitor if a process is under statistical control.
They help visualize variation, find and correct problems when they occur,
predict expected ranges of outcomes and analyze patterns of process variation
from special or common causes. |
|
An industrial process produces a speed
sensor for an ABS electronic control. The sensor specifications require that
the variable X = “impedance” is 30 kohms ±10 kohms. To perform the
statistical control of this process 6 sample sensors are collected every half
hour. With the collected data, a total of 30 samples, the following
information were obtained: ¯x = 30.11 and ˆs¯x = 2.00. a) Construct control
charts to monitor that the population mean and variance remain constant. |
|
DAY 6 |
Proportions chart |
|
|
https://www.slideshare.net/buddykkrishna/control-charts-for-attributes |
|
|
DAY 7 |
Attributes charts |
The p,
np, c and u control charts are called attribute control charts. These four
control charts are used when you have "count" data. There are two
basic types of attributes data: yes/no type data and counting data. The type
of data you have determines the type of control chart you use. |
|
Revise previous class – (10 mins) Presenting Textual Ideas – (30 min) Solving Exercise problem (10 mins) |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
DAY 8 |
Construct c-chart |
To understand
about p-chart |
Control
charts dealing with the number of defects or nonconformities are
called c charts (for count). |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
|
DAY 9 |
Construct p-chart |
To understand about p-chart. |
Examples of quality characteristics that are attributes
are the number of failures in a production run, the proportion of
malfunctioning wafers in a lot, the number of people eating in the cafeteria
on a given day, etc. |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the
topic. |
|
|
DAY 10 |
Constsruct cp-chart |
We
measure weight, height, position, thickness, etc. If we cannot represent a
particular quality characteristic numerically, or if it is impractical to do
so, we then often resort to using a quality characteristic |
Revision of Previous class – 10min Presentation -30 min Poll activity-5 min Doubt clarification-5 min Summarty- 5 min |
Discussion forum on the topic in the
group. Shared material on the topic in Google
class. Review on the topic. |
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