A. Measures of Central Tendency
Terms such as “the most common” or “average” used in regular vocabulary refer to the typical or middle value of a data set. In descriptive statistics, this value is called a measure of central
tendency.
The three measures used most commonly to describe central tendency are mean, median, and
mode.
Mean (also called arithmetic average): The sum of the data entries divided by the number of
entries.
Median: The middle value of an ordered data set.
Outlier: A data entry that is “very different” from the other entries in the data set.
Mode: The data value that occurs most frequently in a data set.
While explaining mode, pay attention to the two special cases:
• No repeat entry
• Two entries that occur with the same highest frequency
Weighted mean: It calculates the mean of a data set by taking into consideration the weight
assigned to each data entry.
If in a frequency distribution graph, the mean, median, and mode are equal and located on the
same value of the x-axis, the distribution is symmetric.
A distribution in which the mean, median, and mode are unequal is called a skewed distribution.
A distribution where the graph has a tail stretching to the left is called skewed left. In this
distribution, mean < median < mode. If the graph of the distribution has a tail stretching to the
right, the distribution is called skewed right. In this distribution, mode < median < mean.
Outliers can create a skewed distribution.
B. Measures of Variation
Range: The difference between the largest and the smallest data entries.
Deviation: The difference between a data entry x in a population and the population mean μ, or
the difference between a data entry x in a sample and the sample mean x .
Variance: A measure of the deviation of the population data set or sample data set from its
mean. Population variance is represented using the symbol σ2—pronounced sigma square.
Saturday, March 28, 2009
March 28, 2009 - Descriptive Statistics (Part II)
Outline
A. Measures of Central Tendency
o Mean
o Median
o Mode
B. Measures of Variation
o Variance
o Standard deviation
C. Measures of Position
o Percentiles
A. Measures of Central Tendency
o Mean
o Median
o Mode
B. Measures of Variation
o Variance
o Standard deviation
C. Measures of Position
o Percentiles
March 28, 2009 - Take Home Quiz Assignment
You will have 45 minutes in class to complete the following assignment/quiz (11:45 to 12:30).
Title: Frequency Distributions and Their Graphs
Introduction: This set of exercises will help you read and construct
a frequency distribution and organize data using a graph.
Tasks:
Complete the following exercises from your textbook
Elementary Statistics—Picturing the World:
1. Section 2.1, Exercise #10, p. 43
2. Section 2.1, Exercise #24, p. 45
3. Section 2.2, Exercise #20, p. 58
GOOD LUCK!
Title: Frequency Distributions and Their Graphs
Introduction: This set of exercises will help you read and construct
a frequency distribution and organize data using a graph.
Tasks:
Complete the following exercises from your textbook
Elementary Statistics—Picturing the World:
1. Section 2.1, Exercise #10, p. 43
2. Section 2.1, Exercise #24, p. 45
3. Section 2.2, Exercise #20, p. 58
GOOD LUCK!
March 28, 2009 - Using Excel
Excel has built-in functionalities that facilitate quick and easy graphing of histograms, pie
charts, and scatter plots.
Follow the instructions listed in Student Solution & Technology Manual,
pages 207–212, to construct a pie chart for the given data set.
The data represents the top seven American Kennel Club registrations, in thousands, in 2003.
(Source: American Kennel Club)
Breed Labrador 145
Retriever 53
Golden 45
Retriever 44
Beagle 39
German
Shepherd 38
Dachshund 34
charts, and scatter plots.
Follow the instructions listed in Student Solution & Technology Manual,
pages 207–212, to construct a pie chart for the given data set.
The data represents the top seven American Kennel Club registrations, in thousands, in 2003.
(Source: American Kennel Club)
Breed Labrador 145
Retriever 53
Golden 45
Retriever 44
Beagle 39
German
Shepherd 38
Dachshund 34
March 28, 2009 - Histogram
The following link leads to an animation that illustrates the effect of a sample size and the
number of classes on a histogram:
http://media.pearsoncmg.com/ph/esm/esm_larson_statlet_questions_2e/Sample_Size_Histogra
m_Statlet/histogram.html
Experiment with the animation, using the:
• Horizontal scrollbar to change the sample size
• Vertical scrollbar to change the number of classes
Respond to this question: “How do you think the sample size and number of classes affect a histogram?”
number of classes on a histogram:
http://media.pearsoncmg.com/ph/esm/esm_larson_statlet_questions_2e/Sample_Size_Histogra
m_Statlet/histogram.html
Experiment with the animation, using the:
• Horizontal scrollbar to change the sample size
• Vertical scrollbar to change the number of classes
Respond to this question: “How do you think the sample size and number of classes affect a histogram?”
March 28, 2009, Quick Quiz
Quiz - True or False
1. The midpoint of a class is the sum of its lower and upper limits.
2. The cumulative frequency of a class is the sample size divided by the frequency of the
class.
3. Relative frequency is the portion of the data that falls in that class.
1. The midpoint of a class is the sum of its lower and upper limits.
2. The cumulative frequency of a class is the sample size divided by the frequency of the
class.
3. Relative frequency is the portion of the data that falls in that class.
Friday, March 20, 2009
March 21, 2009, Class Notes
A. Frequency Distributions
Classes or intervals are units used to group data entries.
A frequency distribution table shows the number of data entries—frequency (f)—in each
class.
The class width is the difference between the upper and lower limits of each class. It can be
calculated using the following formula:
____Range______
Number of classes
Range of a data set is the difference between the maximum and minimum data entries in the
set. Range is calculated as: Maximum data entry − Minimum data entry
The class boundary is the half-way point between two classes.
Midpoint = (Lower limit) + (Upper limit)
Cumulative frequency of a class is the sum of the frequencies for that class and all the
previous classes.
Relative frequency is the percentage of the data in a particular class. It can be calculated
using the formula:
Relative frequency =
n
f
where,
f is the class frequency and n is the sample size.
B. Graphs and Displays
A histogram is a bar graph often used to display quantitative data. The horizontal scale
displays the class boundaries or midpoints. The vertical scale indicates the frequencies of each
class. In a histogram, the bars touch each other.
A pie chart is a circular graph divided into sectors that represent qualitative data categories,
such as colors, races, and genders. The area of each sector is proportional to the relative
frequency of each data category.
A scatter plot is a graph that represents the relationship between paired data, where each entry
in a data set corresponds to an entry in the second data set. The pair of data entries is shown as
a point or dot in the coordinate plane.
Classes or intervals are units used to group data entries.
A frequency distribution table shows the number of data entries—frequency (f)—in each
class.
The class width is the difference between the upper and lower limits of each class. It can be
calculated using the following formula:
____Range______
Number of classes
Range of a data set is the difference between the maximum and minimum data entries in the
set. Range is calculated as: Maximum data entry − Minimum data entry
The class boundary is the half-way point between two classes.
Midpoint = (Lower limit) + (Upper limit)
Cumulative frequency of a class is the sum of the frequencies for that class and all the
previous classes.
Relative frequency is the percentage of the data in a particular class. It can be calculated
using the formula:
Relative frequency =
n
f
where,
f is the class frequency and n is the sample size.
B. Graphs and Displays
A histogram is a bar graph often used to display quantitative data. The horizontal scale
displays the class boundaries or midpoints. The vertical scale indicates the frequencies of each
class. In a histogram, the bars touch each other.
A pie chart is a circular graph divided into sectors that represent qualitative data categories,
such as colors, races, and genders. The area of each sector is proportional to the relative
frequency of each data category.
A scatter plot is a graph that represents the relationship between paired data, where each entry
in a data set corresponds to an entry in the second data set. The pair of data entries is shown as
a point or dot in the coordinate plane.
March 21, 2009, Descriptive Statistics Outline
KEY CONCEPTS THAT MUST BE COVERED IN CLASS
A. Frequency distributions
o Meaning of a Frequency distribution
o Constructing a Frequency distribution
B. Graphs and displays
o Histogram
o Pie chart
o Scatter Plot
A. Frequency distributions
o Meaning of a Frequency distribution
o Constructing a Frequency distribution
B. Graphs and displays
o Histogram
o Pie chart
o Scatter Plot
March 21, 2009, Case Study #2
Assume that you are conducting a study to determine the number of years of education of the
teachers in your college. Here are some situations related to this study.
1. You randomly select two departments and survey each teacher in those departments.
Each department is a naturally occurring subdivision of a college, sharing similar
characteristics. All departments are not surveyed. Which of the sampling techniques will
you use in this situation?
2. Assume that for the survey, you select only the teachers who are instructing you in the
current semester. Which of the sampling techniques will you use in this situation?
3. You categorize the teachers according to their departments and then survey some teachers
in each department. Which of the sampling techniques will you use in this situation?
teachers in your college. Here are some situations related to this study.
1. You randomly select two departments and survey each teacher in those departments.
Each department is a naturally occurring subdivision of a college, sharing similar
characteristics. All departments are not surveyed. Which of the sampling techniques will
you use in this situation?
2. Assume that for the survey, you select only the teachers who are instructing you in the
current semester. Which of the sampling techniques will you use in this situation?
3. You categorize the teachers according to their departments and then survey some teachers
in each department. Which of the sampling techniques will you use in this situation?
March 21, 2009 - Case Study #2, 80's Hair Bands! Class Activity
The following table lists the top five music bands according to the box office ranks and the ticket
prices for their concerts as on July 23, this year.
Ranks Data Set #1 Data Set #2
Band Average Ticket Price
1 The Eagles $104
2 Dave Matthews Band $85
3 The Dixie Chicks $68
4 Fleetwood Mac $60
5 Cher $42
Questions:
1. Identify the level of measurement for the first data set—or the top five bands.
2. Identify the level of measurement for the second data set.
prices for their concerts as on July 23, this year.
Ranks Data Set #1 Data Set #2
Band Average Ticket Price
1 The Eagles $104
2 Dave Matthews Band $85
3 The Dixie Chicks $68
4 Fleetwood Mac $60
5 Cher $42
Questions:
1. Identify the level of measurement for the first data set—or the top five bands.
2. Identify the level of measurement for the second data set.
Thursday, March 12, 2009
March 14, 2009 - Case Study #1
Case 1:
A survey of 2,104 households in the United States found that 65% of them subscribe to cable
television. The survey also found that the households without Internet connection are twice more likely to subscribe to a daily newspaper than the households with Internet connection. The
households with Internet connection often get their news from Web sites instead of daily
newspapers.
Questions:
1. Identify the population and the sample for the survey.
2. Of the 2,104 households surveyed, how many households subscribe to cable television?
3. A follow-up survey of a sample of 1,200 U.S. households found that 360 households have
high-speed Internet connection. What does the number 360 represent?
4. Which statement in the given data represents the descriptive branch of statistics?
5. Which statement in the given data represents the inferential branch of statistics?
A survey of 2,104 households in the United States found that 65% of them subscribe to cable
television. The survey also found that the households without Internet connection are twice more likely to subscribe to a daily newspaper than the households with Internet connection. The
households with Internet connection often get their news from Web sites instead of daily
newspapers.
Questions:
1. Identify the population and the sample for the survey.
2. Of the 2,104 households surveyed, how many households subscribe to cable television?
3. A follow-up survey of a sample of 1,200 U.S. households found that 360 households have
high-speed Internet connection. What does the number 360 represent?
4. Which statement in the given data represents the descriptive branch of statistics?
5. Which statement in the given data represents the inferential branch of statistics?
March 14, 2009 - Statistics T/F Excercise
TRUE OR FALSE (Post true or false for each question)
1. A statistic is a measure that describes a population characteristic.
2. The two main branches of statistics are population and sample.
3. Inferential statistics involves using a population to draw a conclusion about a
corresponding sample.
4. Data at the ordinal level is quantitative only.
5. Data at the ratio level cannot be put in order.
6. For data at the interval level, you cannot calculate meaningful differences between
7. Using a systematic sample guarantees that members of each group within a
population will be sampled.
8. A census is a count of part of a population.
9. Performing an experiment is the only way to collect reliable data.
1. A statistic is a measure that describes a population characteristic.
2. The two main branches of statistics are population and sample.
3. Inferential statistics involves using a population to draw a conclusion about a
corresponding sample.
4. Data at the ordinal level is quantitative only.
5. Data at the ratio level cannot be put in order.
6. For data at the interval level, you cannot calculate meaningful differences between
7. Using a systematic sample guarantees that members of each group within a
population will be sampled.
8. A census is a count of part of a population.
9. Performing an experiment is the only way to collect reliable data.
March 14, 2009 - Overview of Statistics I
A. Overview of Statistics
Statistics is defined as the science of collecting, organizing, analyzing, and interpreting data in
order to make decisions. It uses mathematical formulas for analysis, and it involves the
understanding and interpretation of the results.
Statistics involves studying two types of data sets—population and sample.
Population:
• It is a collection of all outcomes, responses, measurements, and counts that are of interest.
• The numerical description of a characteristic of a population is called a parameter.
• A count or measure of an entire population is called a census.
Sample:
• A subset of a population is called a sample.
• The numerical description of a characteristic of a sample is called a statistic.
• A count or measure of a part of a population is called a sample.
The study of statistics is divided into two major branches:
Descriptive statistics: The branch of statistics that involves organization, summarization, and
display of data
Inferential statistics: The branch of statistics that involves the use of a sample to draw
conclusions about a population
B. Data Classification
A clear understanding of the meaning of the term “data” is central to the study of statistics. Data
consists of information related to observations, counts, measurements, or responses.
Class Activity
Qualitative: It consists of attributes, labels, or nonnumeric entries. For example, gender—male
or female—refers to qualitative data.
Quantitative: It consists of numerical measurements or counts. For example, age—1, 2, 10, or
20—refers to quantitative data.
Another way to classify data is by its level of measurement, which determines the relevance of
statistical calculations. Using this categorization, we have nominal, ordinal, interval, and ratio
data.
C. Experimental Design
In the real world, statistical results can mislead or misrepresent the facts if the research
conducted does not use proper procedures. Therefore, while making a decision based on
statistical analysis, we should be aware of the process used to obtain the data and the potential
misuse of the data. Given here are some guidelines for designing a statistical study:
1. Identify the variables of interest and the population of the study.
2. Develop a detailed plan for collecting data.
3. Collect the data using any of the following methods:
i. Doing an observational study
ii. Performing an experiment
iii. Using a simulation
iv. Using a survey
4. Describe the data using appropriate descriptive statistics or graphs or both.
5. Interpret the data and make decisions about the population by using inferential statistics.
6. Identify any possible errors.
Surveys can be done by taking a census or using a sample. To ensure an accurate representation
of the population, appropriate sampling techniques should be used; otherwise, the results from
the study may be considered invalid. Some commonly used sampling techniques are random
sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling.
HOMEWORK: DESIGN YOUR STUDY & POST FOR EACH DESIGN NUMBER
Statistics is defined as the science of collecting, organizing, analyzing, and interpreting data in
order to make decisions. It uses mathematical formulas for analysis, and it involves the
understanding and interpretation of the results.
Statistics involves studying two types of data sets—population and sample.
Population:
• It is a collection of all outcomes, responses, measurements, and counts that are of interest.
• The numerical description of a characteristic of a population is called a parameter.
• A count or measure of an entire population is called a census.
Sample:
• A subset of a population is called a sample.
• The numerical description of a characteristic of a sample is called a statistic.
• A count or measure of a part of a population is called a sample.
The study of statistics is divided into two major branches:
Descriptive statistics: The branch of statistics that involves organization, summarization, and
display of data
Inferential statistics: The branch of statistics that involves the use of a sample to draw
conclusions about a population
B. Data Classification
A clear understanding of the meaning of the term “data” is central to the study of statistics. Data
consists of information related to observations, counts, measurements, or responses.
Class Activity
Qualitative: It consists of attributes, labels, or nonnumeric entries. For example, gender—male
or female—refers to qualitative data.
Quantitative: It consists of numerical measurements or counts. For example, age—1, 2, 10, or
20—refers to quantitative data.
Another way to classify data is by its level of measurement, which determines the relevance of
statistical calculations. Using this categorization, we have nominal, ordinal, interval, and ratio
data.
C. Experimental Design
In the real world, statistical results can mislead or misrepresent the facts if the research
conducted does not use proper procedures. Therefore, while making a decision based on
statistical analysis, we should be aware of the process used to obtain the data and the potential
misuse of the data. Given here are some guidelines for designing a statistical study:
1. Identify the variables of interest and the population of the study.
2. Develop a detailed plan for collecting data.
3. Collect the data using any of the following methods:
i. Doing an observational study
ii. Performing an experiment
iii. Using a simulation
iv. Using a survey
4. Describe the data using appropriate descriptive statistics or graphs or both.
5. Interpret the data and make decisions about the population by using inferential statistics.
6. Identify any possible errors.
Surveys can be done by taking a census or using a sample. To ensure an accurate representation
of the population, appropriate sampling techniques should be used; otherwise, the results from
the study may be considered invalid. Some commonly used sampling techniques are random
sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling.
HOMEWORK: DESIGN YOUR STUDY & POST FOR EACH DESIGN NUMBER
March 14, 2009 - Why Statistics
Discuss the fact that every day we are bombarded with data and information on several issues.
These include a variety of social, economic, and political issues.
A few examples are:
For each bullet point, discuss how and why statistics is important.
• Impact of violent TV programs on children
• Outsourcing and its effects
• Claims made by presidential candidates
• U.S. nuclear policy
These include a variety of social, economic, and political issues.
A few examples are:
For each bullet point, discuss how and why statistics is important.
• Impact of violent TV programs on children
• Outsourcing and its effects
• Claims made by presidential candidates
• U.S. nuclear policy
March 14, 2009 - Introductions
Welcome to Statistics -- please introduce yourself to the other students and complete the following:
• Name
• Educational experience
• Program of study pursued at the ITT Technical Institute
• Work experience
• Expectations from the course
• Career aspirations
• Name
• Educational experience
• Program of study pursued at the ITT Technical Institute
• Work experience
• Expectations from the course
• Career aspirations
March 14, 2009 - Syllabus
SYLLABUS
Instructor: ________________________________________
Office hours: ________________________________________
Class hours: ________________________________________
COURSE DESCRIPTION
This course is designed to offer students the skills necessary to interpret and critically
evaluate statistics commonly used to describe, predict and evaluate data in an
information-driven environment. The focus is on the conceptual understanding of how
statistics can be used and on how to evaluate statistical data.
MAJOR INSTRUCTIONAL AREAS
1. Experimental Design and Collecting Data
2. Describing Data
3. Determining Probabilities
4. Probability Distributions
5. Confidence Testing
6. Hypothesis Testing
7. Correlation and Regression
COURSE OBJECTIVES
1. Explain the fundamentals of a statistical study.
2. Describe data sets and their measures in different forms.
3. Use statistics to conduct and summarize an observation that has both qualitative and
quantitative components.
4. Calculate probabilities by using counting principles.
5. Identify various discrete probability distributions and calculate corresponding
probabilities.
6. Interpret a normal distribution and make calculations using standard scores.
7. Construct confidence intervals and use them to interpret population means.
8. Formulate null and alternative hypotheses for claims made about population means.
9. Use an appropriate statistical technique to test a hypothesis.
10. Describe the linear association for a set of paired data.
11. Utilize the ITT Tech Virtual Library to enhance understanding of statistics.
Related SCANS Objectives
1. Comprehend and use efficient learning techniques to acquire and apply knowledge.
2. Create documents including graphs and flowcharts to illustrate point.
3. Communicate ideas to justify positions; persuade and convince others.
4. Retrieve and organize data from a variety of sources, including computerized
databases, reference books, books, and periodicals.
5. Demonstrate the ability to utilize both traditional and electronic library sources.
6. Acquire and evaluate relevant information, and organize, maintain, analyze, interpret,
communicate, and use applicable information.
7. Develop and reinforce critical thinking processes.
8. Participate cooperatively as a team member, teaching, learning from, and negotiating
with diverse members making a contribution to team success.
9. Identify need for data; select, retrieve, and analyze information; and communicate
results to others in written, graphic, and pictorial format.
TEACHING STRATEGIES
This section details a strategy to help you run this course.
In-class time will be utilized as follows:
1. Review of concepts covered in previous unit: is included in all units in
order that there are periodic check points for all work to be completed and
instructors can track progress and follow up immediately with students who
are not getting assignments completed on-time. Review involves:
a. A discussion of homework assignments that students have completed
b. Graded quiz including a set of objective type questions (true/false and
MCQs)
2. Explanation and application of new concepts: EG381 has a dual focus—
building basic concepts/vocabulary and developing statistical skills. To
address these focal points, each unit involves:
a. Concept explanation: so that key concepts are covered in-class and
students are aware of the important formulas. To reinforce the
important concepts, concept explanation is followed by a review quiz
consisting of a set of 5–10 true/false statements.
b. Concept application: is done using the following tools:
i. A case study in most of the units. Each case study consists of
3–4 objective type questions, all based on a data set/scenario,
aimed at improving students’ calculation and interpretation
skills.
ii. Statlets from Course compass: provides about seven statlets
(java Applets) for this course. These JAVA applets consist of
a graphic display and corresponding input values that can be
changed in real time. Observing and commenting on the
changes enables development of students’ statistical analysis
skills. In most units, a statlet is included and students are
encouraged to their interpretation/analysis of the statlet. For
two units where statlets are not present, the discussion will be
based on a static graphic.
iii. Excel hands-On Sessions (when applicable): The purpose of
this component is to help students gain familiarity in the use
of MS Excel for solving certain statistical problems. These
sessions will utilize the student technology manual to
demonstrate application of built-in functionalities and
graphing utilities of MS Excel. Students will be provided with
a data set so that they can practice along with the excel
session.
Homework assignments in this course take the form of:
1. Writing Assignments: primarily consisting of 3–4 problems from the
textbook with an objective to provide more objective and mathematically
based assignments to students.
2. Preparing for the graded quiz
The following graphic illustrates instructional strategy for this course.
Note: Most units have been designed and developed consistently using a combination of
the components depicted above. A consistent organization is intended to help students
gain familiarity with content organization sooner and have clear expectations. Please feel
free to deviate from this strategy by using different examples or assignments based on
your teaching experiences and interactions with your class.
Discuss the homework assignment from the previous unit and
address students’ problems and concerns (15 minutes)
H
O
M
E
W
O
R
K
Conduct a graded quiz to assess students’ understanding of the
concepts covered in the previous unit (30 minutes)
Concept explanation with a focus on key concepts (1 hour)
Concept reinforcement using the review quiz (15-20 minutes)
Concept application and data interpretation using case study (1–
1.25 hours)
Excel hands-on session* (0.75 hours)
I
N
-
C
L
A
S
S
A
C
T
I
V
I
T
I
E
S
Writing assignment (2 hours)
Preparation for the graded quiz (0.5-0.75 hours)
Review
COURSE RESOURCES
Student Textbook Package
Textbook: Larson, Ron, and Betsy Farber. Elementary Statistics: Picturing the World.
Indianapolis: Pearson Custom Publishing, 2006.
Solutions and Technology Manual: Larson, Ron, and Betsy Farber. Student’s Solutions
& Technology Manual for Elementary Statistics: Picturing the World. Indianapolis:
Pearson Custom Publishing, 2006.
CD-ROMs:
o Chapter quiz prep, videos, and data files to accompany Elementary Statistics:
Picturing the World
o Data files for use with Student’s Solutions & Technology Manual for Elementary
Statistics: Picturing the World
References and Resources
ITT Tech Virtual Library
Log on to the ITT Tech Virtual Library at http://www.library.itt-tech.edu/ to access
online books, journals, and other reference resources selected to support ITT Tech
curricula.
General References
• Reference Resources > Statistics
• Program Links > General Education/Technical Basics > Link Library > EG381
Statistics
All links to web references outside of the ITT Tech Virtual Library are always subject to change
without prior notice.
EVALUATION & GRADING
COURSE REQUIREMENTS
1. Attendance and Participation
Regular attendance and participation are essential for satisfactory progress in this course.
2. Completed Assignments
Each student is responsible for completing all assignments on time.
3. Team Participation (if applicable)
Each student is responsible for participating in team assignments and for completing the
delegated task. Each team member must honestly evaluate the contributions by all
members of their respective teams.
Evaluation Criteria Table
The final grade will be based on the following weighted categories:
CATEGORY WEIGHT
Participation 10%
Writing Assignments 45%
Quizzes 30%
Final Exam 15%
Total 100%
Grade Conversion Table
Final grades will be calculated from the percentages earned in class as follows:
Grade Percentage Credit
A 90–100% 4.0
B+ 85–89% 3.5
B 80–84% 3.0
C+ 75–79% 2.5
C 70–74% 2.0
D+ 65–69% 1.5
D 60–64% 1.0
F <60% 0.0
Instructor: ________________________________________
Office hours: ________________________________________
Class hours: ________________________________________
COURSE DESCRIPTION
This course is designed to offer students the skills necessary to interpret and critically
evaluate statistics commonly used to describe, predict and evaluate data in an
information-driven environment. The focus is on the conceptual understanding of how
statistics can be used and on how to evaluate statistical data.
MAJOR INSTRUCTIONAL AREAS
1. Experimental Design and Collecting Data
2. Describing Data
3. Determining Probabilities
4. Probability Distributions
5. Confidence Testing
6. Hypothesis Testing
7. Correlation and Regression
COURSE OBJECTIVES
1. Explain the fundamentals of a statistical study.
2. Describe data sets and their measures in different forms.
3. Use statistics to conduct and summarize an observation that has both qualitative and
quantitative components.
4. Calculate probabilities by using counting principles.
5. Identify various discrete probability distributions and calculate corresponding
probabilities.
6. Interpret a normal distribution and make calculations using standard scores.
7. Construct confidence intervals and use them to interpret population means.
8. Formulate null and alternative hypotheses for claims made about population means.
9. Use an appropriate statistical technique to test a hypothesis.
10. Describe the linear association for a set of paired data.
11. Utilize the ITT Tech Virtual Library to enhance understanding of statistics.
Related SCANS Objectives
1. Comprehend and use efficient learning techniques to acquire and apply knowledge.
2. Create documents including graphs and flowcharts to illustrate point.
3. Communicate ideas to justify positions; persuade and convince others.
4. Retrieve and organize data from a variety of sources, including computerized
databases, reference books, books, and periodicals.
5. Demonstrate the ability to utilize both traditional and electronic library sources.
6. Acquire and evaluate relevant information, and organize, maintain, analyze, interpret,
communicate, and use applicable information.
7. Develop and reinforce critical thinking processes.
8. Participate cooperatively as a team member, teaching, learning from, and negotiating
with diverse members making a contribution to team success.
9. Identify need for data; select, retrieve, and analyze information; and communicate
results to others in written, graphic, and pictorial format.
TEACHING STRATEGIES
This section details a strategy to help you run this course.
In-class time will be utilized as follows:
1. Review of concepts covered in previous unit: is included in all units in
order that there are periodic check points for all work to be completed and
instructors can track progress and follow up immediately with students who
are not getting assignments completed on-time. Review involves:
a. A discussion of homework assignments that students have completed
b. Graded quiz including a set of objective type questions (true/false and
MCQs)
2. Explanation and application of new concepts: EG381 has a dual focus—
building basic concepts/vocabulary and developing statistical skills. To
address these focal points, each unit involves:
a. Concept explanation: so that key concepts are covered in-class and
students are aware of the important formulas. To reinforce the
important concepts, concept explanation is followed by a review quiz
consisting of a set of 5–10 true/false statements.
b. Concept application: is done using the following tools:
i. A case study in most of the units. Each case study consists of
3–4 objective type questions, all based on a data set/scenario,
aimed at improving students’ calculation and interpretation
skills.
ii. Statlets from Course compass: provides about seven statlets
(java Applets) for this course. These JAVA applets consist of
a graphic display and corresponding input values that can be
changed in real time. Observing and commenting on the
changes enables development of students’ statistical analysis
skills. In most units, a statlet is included and students are
encouraged to their interpretation/analysis of the statlet. For
two units where statlets are not present, the discussion will be
based on a static graphic.
iii. Excel hands-On Sessions (when applicable): The purpose of
this component is to help students gain familiarity in the use
of MS Excel for solving certain statistical problems. These
sessions will utilize the student technology manual to
demonstrate application of built-in functionalities and
graphing utilities of MS Excel. Students will be provided with
a data set so that they can practice along with the excel
session.
Homework assignments in this course take the form of:
1. Writing Assignments: primarily consisting of 3–4 problems from the
textbook with an objective to provide more objective and mathematically
based assignments to students.
2. Preparing for the graded quiz
The following graphic illustrates instructional strategy for this course.
Note: Most units have been designed and developed consistently using a combination of
the components depicted above. A consistent organization is intended to help students
gain familiarity with content organization sooner and have clear expectations. Please feel
free to deviate from this strategy by using different examples or assignments based on
your teaching experiences and interactions with your class.
Discuss the homework assignment from the previous unit and
address students’ problems and concerns (15 minutes)
H
O
M
E
W
O
R
K
Conduct a graded quiz to assess students’ understanding of the
concepts covered in the previous unit (30 minutes)
Concept explanation with a focus on key concepts (1 hour)
Concept reinforcement using the review quiz (15-20 minutes)
Concept application and data interpretation using case study (1–
1.25 hours)
Excel hands-on session* (0.75 hours)
I
N
-
C
L
A
S
S
A
C
T
I
V
I
T
I
E
S
Writing assignment (2 hours)
Preparation for the graded quiz (0.5-0.75 hours)
Review
COURSE RESOURCES
Student Textbook Package
Textbook: Larson, Ron, and Betsy Farber. Elementary Statistics: Picturing the World.
Indianapolis: Pearson Custom Publishing, 2006.
Solutions and Technology Manual: Larson, Ron, and Betsy Farber. Student’s Solutions
& Technology Manual for Elementary Statistics: Picturing the World. Indianapolis:
Pearson Custom Publishing, 2006.
CD-ROMs:
o Chapter quiz prep, videos, and data files to accompany Elementary Statistics:
Picturing the World
o Data files for use with Student’s Solutions & Technology Manual for Elementary
Statistics: Picturing the World
References and Resources
ITT Tech Virtual Library
Log on to the ITT Tech Virtual Library at http://www.library.itt-tech.edu/ to access
online books, journals, and other reference resources selected to support ITT Tech
curricula.
General References
• Reference Resources > Statistics
• Program Links > General Education/Technical Basics > Link Library > EG381
Statistics
All links to web references outside of the ITT Tech Virtual Library are always subject to change
without prior notice.
EVALUATION & GRADING
COURSE REQUIREMENTS
1. Attendance and Participation
Regular attendance and participation are essential for satisfactory progress in this course.
2. Completed Assignments
Each student is responsible for completing all assignments on time.
3. Team Participation (if applicable)
Each student is responsible for participating in team assignments and for completing the
delegated task. Each team member must honestly evaluate the contributions by all
members of their respective teams.
Evaluation Criteria Table
The final grade will be based on the following weighted categories:
CATEGORY WEIGHT
Participation 10%
Writing Assignments 45%
Quizzes 30%
Final Exam 15%
Total 100%
Grade Conversion Table
Final grades will be calculated from the percentages earned in class as follows:
Grade Percentage Credit
A 90–100% 4.0
B+ 85–89% 3.5
B 80–84% 3.0
C+ 75–79% 2.5
C 70–74% 2.0
D+ 65–69% 1.5
D 60–64% 1.0
F <60% 0.0
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