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Master
Course Outline
Introduction to Statistics
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| Course
Number: MATH 201 |
| Credits: 5 |
Hours per Quarter: 50
Lecture Hours:50
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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 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.
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Syllabi
Listing
| Course |
Year
Quarter |
Item |
Instructor |
|
| 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 |
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| MATH 201 |
Fall 2009 |
5777 |
Eric Schulz |
|
| MATH 201 |
Fall 2009 |
1476 |
Eric Schulz |
|
| MATH 201 |
Summer 2009 |
6552 |
NATHAN READE |
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| 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 |
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| 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 |
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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
|
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*Year one is all odd years
(2003-2004)
**Year two is all even years (2004-2005)
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