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Master Course Outline
Introduction to Statistics

Course Number: MATH 201
Credits: 5
Hours per Quarter: 50

Lecture Hours:50


Description
Study of both descriptive and inferential statistics. Topics include data presentation, and analysis, measures of central tendency and dispersion, sampling distributions, parameter estimation hypothesis testing, and linear regression. Prerequisite: Grade of C or higher in MATH 078E, appropriate score on placement test, or permission of the Mathematics Department.

Intended Skills and Attitude Outcomes
A. Describe the difference betwen descriptive statistics and inferential statistics.
B. Describe the difference betwen a population and sample.
C. Describe the difference between numerical and categorical data.
D. Describe the difference between discrete and continuous numerical data.
E. Construct a stem-and-leaf plot of numerical data.
F. Construct frequency distributions for discrete data.
G. Determine appropriate class limits to be used for the construction of a frequency distribution for data set and construct frequency distributions for continuous data.
H. Construct histograms for categorical data, discrete data, and continuous data.
I. Read and interpret histograms for centers, spread, and skewness of data sets.
J. Compute the mean for a data set.
K. Describe the difference between a sample mean and population mean.
L. Compute the median for a data set.
M. Compute the variance and standard deviation for a data set.
N. Determine quartiles for a data set.
O. Compute standard scores z-scores for observations.
P. Determine percentiles for data sets.
Q. Construct a boxplot for a data set.
R. Analyze relationships in bivariate data using a scatterplot.
S. Compute and interpret correlation coefficient for bivariate data.
T. Describe why strong association does not imply causation.
U. Compute the slope and y-intercept for the sample regression line.
V. Plot the sample regression line together with the data set.
W. Use the sample regression line for prediction.
X. Compute and interpret the coefficient of determination.
Y. Understand conceptually the meaning of probability.
Z. Know the definition of a random variable.
AA. Describe the difference beween a discrete and continuous random variable.
BB. Calculate probabilities for random variables taking on certain values.
CC. Given a scenario, determine a probability distribution for a random variable.
DD. Calculate the mean and standard deviation for a discrete random variable.
EE. Describe the properties of a binomial experiment.
FF. Define the binomial random variable.
GG. Use the binomial probability distribution to determine probabilities in binomial settings.
HH. Calculate the mean and standard deviation of a binomial random variable.
II. Describe the characteristics of a probability distribution for a continuous random variable.
JJ. Use the probability distribution to calculate probabilities for a random variable.
KK. Describe the characteristics of a normal distribution.
LL. Determine z critical values.
MM. Use a table to calculate probabilities for a normally distributed random variable.
NN. Know the definition of a statistic verses a parameter.
OO. Describe the sampling distribution of a statistic.
PP. Describe the characteristics of a random sample.
QQ. Describe the sampling distribution of a sample mean.
RR. Understand and apply the Central Limit Theorem as it applies to a sample means and probabilities.
SS. Use the sampling distribution of a sample mean to calculate probabilities.
TT. Describe the sampling distribution of a sample proportion.
UU. Use the sampling distribution of a sample proportion to calculate probabilities.
VV. Understand a point estimate for a population parameter.
WW. Describe the difference between an unbiased statistic and biased statistic.
XX. Understand the concept of a confidence interval estimate for a population parameter.
YY. Describe the difference between confidence level and margin of error in the estimate.
ZZ. Understand how the randomness of the sample affects the location of the confidence interval.
1. Calculate confidence intervals for a population mean from large sample data.
2. Determine appropriate sample sizes to meet specified margin of errors.
3. Calculate confidence intervals for the population proportion from large sample study.


Syllabi Listing
Course
Year Quarter
Item
Instructor  
MATH 201
Winter 2015
6848
Michael Shively
MATH 201
Winter 2015
6847
Michael Shively
MATH 201
Fall 2014
6549
Michael Shively
MATH 201
Fall 2014
5777
Michael Shively
MATH 201
Fall 2014
1519
Eric Schulz
MATH 201
Fall 2014
1487
Shana Smith
MATH 201
Fall 2014
1486
Megan Schoessler
MATH 201
Spring 2014
6664
Michael Shively
MATH 201
Spring 2014
6563
Michael Shively
MATH 201
Spring 2014
5777
Shana Smith
MATH 201
Spring 2014
0844
Megan Schoessler
MATH 201
Spring 2014
0843
Benjamin Van Dyke
MATH 201
Spring 2014
0842
Shana Smith
MATH 201
Winter 2014
6848
Michael Shively
MATH 201
Winter 2014
6847
Michael Shively
MATH 201
Winter 2014
5777
Shana Smith
MATH 201
Winter 2014
0897
Shana Smith
MATH 201
Winter 2014
0895
Megan Schoessler
MATH 201
Fall 2013
6549
Michael Shively
MATH 201
Fall 2013
1817
Megan Schoessler
MATH 201
Fall 2013
1486
Megan Schoessler
MATH 201
Spring 2013
6562
Michael Shively
MATH 201
Spring 2013
1850
Mark Grimm
MATH 201
Spring 2013
1490
Megan Schoessler
MATH 201
Spring 2013
0445
Megan Schoessler
MATH 201
Winter 2013
6847
Michael Shively
MATH 201
Winter 2013
5777
Shana Smith
MATH 201
Winter 2013
0447
Shana Smith
MATH 201
Winter 2013
0445
Megan Schoessler
MATH 201
Fall 2012
6549
Michael Shively
MATH 201
Fall 2012
5777
Shana Smith
MATH 201
Fall 2012
1479
Shana Smith
MATH 201
Fall 2012
1476
Megan Schoessler
MATH 201
Spring 2012
6562
Barbara Blasey
MATH 201
Spring 2012
6559
Rochelle Dietz
MATH 201
Spring 2012
5777
Shana Smith
MATH 201
Spring 2012
1477
Shana Smith
MATH 201
Spring 2012
1476
Shana Smith
MATH 201
Winter 2012
6847
Barbara Blasey
MATH 201
Winter 2012
6560
Barbara Blasey
MATH 201
Fall 2011
5777
Shana Smith
MATH 201
Fall 2011
1476
Shana Smith
MATH 201
Winter 2011
6560
Barbara Blasey
MATH 201
Fall 2010
5777
Eric Schulz
MATH 201
Fall 2010
1476
Eric Schulz
MATH 201
Winter 2010
6560
Barbara Blasey
MATH 201
Winter 2010
5520
Julianne Sachs
MATH 201
Winter 2010
1487
Julianne Sachs
MATH 201
Fall 2009
5777
Eric Schulz
MATH 201
Fall 2009
1476
Eric Schulz
MATH 201
Summer 2009
6552
NATHAN READE
MATH 201
Spring 2009
5777
HEATHER VAN DYKE
MATH 201
Winter 2009
6560
Barbara Blasey
MATH 201
Winter 2009
5520
HEATHER VAN DYKE
MATH 201
Winter 2009
1487
Benjamin Van Dyke
MATH 201
Fall 2008
1476
HEATHER VAN DYKE
MATH 201
Spring 2008
1493
Benjamin Van Dyke
MATH 201
Spring 2007
1493
Benjamin Van Dyke
MATH 201
Winter 2007
5520
Eric Schulz
MATH 201
Fall 2006
5520
Eric Schulz
MATH 201
Fall 2006
1476
Eric Schulz
MATH 201
Spring 2006
1493
Benjamin Van Dyke
MATH 201
Winter 2006
5520
Eric Schulz
MATH 201
Winter 2006
1487
Eric Schulz
MATH 201
Fall 2005
5520
Eric Schulz
MATH 201
Fall 2005
1476
Eric Schulz
MATH 201
Summer 2005
1456
Benjamin Van dyke
MATH 201
Spring 2005
5520
Eric Schulz
MATH 201
Spring 2005
1476
Eric Schulz
MATH 201
Winter 2005
1487
Eric Schulz
MATH 201
Fall 2004
5520
Eric Schulz
MATH 201
Fall 2004
1476
Eric Schulz
MATH 201
Winter 2004
5520
Eric Schulz
MATH 201
Winter 2004
1487
Eric Schulz


Two Year Projected Schedule

Year One* Year Two**
Fall
Winter
Spring
Summer
Mini 
Fall
Winter
Spring
Summer
Mini
X
X
X
X
 
X
X
X
X
 

*Year one is all odd years (2003-2004)
**Year two is all even years (2004-2005)