Introductory Statistics
Instructor
Dr. Jesse Crawford
Email: jcrawford@tarleton.edu
Website: faculty.tarleton.edu/crawford
Office phone: (254) 968-9536
Office: Math 332
Office Hours
MWF 1:00 – 2:00
TR 1:30 – 2:30
You are highly encouraged to visit my office for help.
Course Meeting Times
TR 10:50 – 12:05 in Math 333
Required Materials
Statistics, 11th ed., by McClave and Sincich, and an access code to MyMathLab. The
MyMathLab Course ID is crawford76979. You will also need a graphing calculator with
statistical capabilities, such as a TI-83 Plus or a TI-nspire CAS.
Exams
There will be four exams during the semester covering the following chapters:
Exam 1: Chapters 2 & 3
Exam 2: Chapters 4 & 5
Exam 3: Chapters 6 & 7
Exam 4: Chapter 8 & 11
There will also be a cumulative final exam.
Homework
Homework will be assigned almost every day, and it will usually be due two class periods later.
It is crucial to keep up with the homework to succeed in this course. A few homework
assignments will be dropped.
Missed Exams:
A student who misses an exam for a valid reason, such as serious illness or the death of a family
member will be allowed to make up the exam. Students who make up exams are required to
provide documentation confirming that the absence occurred for a legitimate reason.
Grades
The following tables show how your final grade will be calculated.
Homework
Exam 1
Exam 2
Exam 3
Exam 4
Final Exam
Percentage
of Grade
20%
15%
15%
15%
15%
20%
Average
Grade
90-100
80-89
70-79
60-69
0-59
A
B
C
D
F
Students with Disabilities: It is the policy of Tarleton State University to comply with the
Americans with Disabilities Act and other applicable laws. If you are a student with a disability
seeking accommodations for this course, please contact Trina Geye, Director of Student
Disability Services, at 254.968.9400 or geye@tarleton.edu. Student Disability Services is located
in Math 201. More information can be found at www.tarleton.edu/sds or in the University
Catalog.
Academic Integrity: The Tarleton University Mathematics Department takes academic
integrity very seriously. The usual penalty for a student caught cheating includes an F in the
course. Further penalties may be imposed, including expulsion from the university.
How to Succeed in This Course:
1)
2)
3)
4)
Attend Class. Only miss class when absolutely necessary.
Pay attention during class.
Start homework as soon as possible. Don’t procrastinate.
Get help on problems that you are struggling with, either from me, the math clinic, or
other students.
5) Study a moderate amount before exams.
Student Learning Outcomes
Students will
(a) demonstrate understanding of data organization by constructing appropriate
graphical representations of data sets and by calculating measures of central
tendency and variability for data sets.
(b) demonstrate understanding of discrete probability by calculating probabilities
(including conditional probabilities), expected values, and variances using
axioms, counting techniques, and specific distributions.
(c) demonstrate understanding of continuous probability by calculating probabilities
involving the normal distribution and the central limit theorem.
(d) demonstrate understanding of statistical decision making by finding confidence
intervals for population means and population proportions.
(e) demonstrate understanding of statistical decision making by performing
hypothesis tests involving population means and population proportions.
Sections of Primary Interest
2.2 Graphical Methods for Describing Quantitative Data
2.3 Summation Notation.
2.4 Numerical Measures of Central Tendency.
2.5 Numerical Measures of Variability.
2.10 Distorting the Truth with Descriptive Techniques.
3.1 Events, Sample Spaces, and Probability.
3.2 Unions and Intersections.
3.3 Complementary Events.
3.4 The Additive Rule and Mutually Exclusive Events.
3.5 Conditional Probability.
3.6 The Multiplication Rule and Independent Events.
3.8 Some Additional Counting Rules
3.9 Bayes’s Rule
4.1 Two Types of Random Variables.
4.2 Probability Distributions for Discrete Random Variables.
4.3 Expected Values of Discrete Random Variables.
4.4 The Binomial Random Variable.
5.1 Continuous Probability Distributions.
5.2 The Uniform Distribution.
5.3 The Normal Distribution.
5.4 Descriptive Methods for assessing Normality.
5.5 Approximating a Binomial Distribution with a Normal Distribution.
6.1 The Concept of a sampling Distribution.
6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance.
6.3 The Sampling Distribution of 𝑥̅ and the Central Limit Theorem.
7.1 Identifying the Target Parameter.
7.2 Large-Sample Confidence Interval for a Population Mean.
7.3 Small-Sample Confidence Interval for a Population Mean.
7.4 Large-Sample Confidence Interval for a Population Proportion.
7.5 Determining the Sample Size.
8.1 The Elements of a Test of Hypothesis.
8.2 Large-Sample Test of Hypothesis About a Population Mean.
8.3 Observed Significance Levels: p-Values.
8.4 Small-Sample Test of Hypothesis About a Population Mean.
8.5 Large-Sample Test of Hypothesis About a Population Proportion.
11.1 Probabilistic Models
11.2 Fitting the Model: The Least Squares Approach
11.3 Model Assumptions
11.4 Assessing the Utility of the Model
11.5 The Coefficients of Correlation and Determination
11.6 Using the Model for Estimation and Prediction
11.7 A Complete Example