COURSE SYLLABUS
Ohio Northern University
College of Arts and Sciences
Department of Mathematics and Statistics
Effective Date: Fall 2011-12
Course: STAT 1761
Name: Statistics for Pharmacy
Credit hours: 3
Lecture hours/week: 3
Lab hours/week: 0
Instructor: Staff
Usual student level: P2
Course required of and limited to students in: Pharmacy
Course frequency per year: Offered Every Year, Fall Semester, Spring Semester
Average enrollment per year: 160
This course has a prerequisite: MATH 1401 or equivalent
This course is a satisfactory prerequisite for: STAT 2561
Catalog Description: Describing data graphically and numerically; Describing bivariate data;
Probability concepts; Random variables and probability distributions (both discrete and
continuous); Sampling distributions; Statistical inference (point estimation, confidence intervals,
hypothesis testing) for single means and proportions, and the difference between two means
and proportions; Statistical study designs.
Course Objectives: To introduce the students to data analysis, concepts of probability, and
fundamentals of statistical inference as used in Pharmacy.
Textbook: “Biostatistical Analysis” by Zar (Prentice Hall 5e)
Outline of content follows:
(see attached)
Course Outline
STAT 1761
Title: Statistics For Pharmacy
Introduction (2 hours)
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Population; sample
Descriptive vs. inferential
Types of data (ratio, etc.)
o continuous and discrete data
Simple random sample
Descriptive Statistics (4 hours)
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Understanding summarized data; histograms
Mean and median, 1st and 3rd quartiles
Understanding summarized data: box and whiskers
Variance, standard deviation, coefficient of variation
Bivariate quantitative data – scatter diagram
Probability Concepts (5 hours)
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Probability assignment through relative frequencies
Probability assignment through equally likely outcomes
Conditional probability
Independence
Ratio, proportion, and rate
Incidence and prevalence
Relative risk and odds ratio
Bayes' Theorem
o rate of infection if a group is exposed to a pathogen
o sensitivity of a test
o specificity of a test
o predictive value of a test
Probability Distributions (6 hours)
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Binomial distribution
o Identify setup resulting in binomial distribution
o binomial probability formula – how to use it
o using calculator for probabilities (instead of tables)
 probability function
o
 cumulative distribution function
expected value & variance formulas
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Standard normal distribution
o continuous distributions
o describe graphically; E(Z)=0, V(Z)=1
o probabilities using calculator
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Other normal distributions
o standardization
o probabilities using calculator
Sampling Distributions (5 hours)
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Distribution of sample mean X
o example of taking a random sample of size 2 with replacement from a numerical
pop.{ 80, 100, 100} of size 3 X
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 compute expected value, variance of X
 compare to pop mean, variance
formulas, sampling with/without replacement
sampling from normal population
sampling from a possible non-normal population: Central Limit Theorem
sampling from non-normal population (large sample size)
Distribution of sample mean X 1  X 2
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compute probability distribution of X
normal pops
non-normal, large samples
computing probabilities
Sample proportion P̂
o population proportion: p
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sample proportion: p̂
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distribution of random variable P̂ - relation to binomial
expectation; variance
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distribution of P̂ - large sample sizes
continuity correction
computing probabilities
Difference of sample proportion s Pˆ1  Pˆ2
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distribution - large sample sizes
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computing probabilities
Point and Confidence Interval Estimation (8 hours)
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Estimation of 
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know what a point estimate is
know what an unbiased estimate is
know what a consistent estimator is
interval estimation of µ
know what a confidence level represents
find critical value z1-/2
find confidence interval estimates of µ when σ known
when sampling from normal population
when sampling from non-normal population but with large sample size, 
unknown
know why central limit theorem used
for given confidence level and , and a desired error margin W, calculate the
necessary sample size n
find confidence interval estimates of µ when σ unknown
t distribution
 know properties
 use TI solver to find critical values
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know when
X 
S/ n
use it to estimate  when small size sample from normal distribution, 
unknown
find confidence interval estimates of µ when σ unknown
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Estimation of 1-2
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Estimate 1-2 in various situations
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when 1 and 2 known
when 1 and 2 unknown but assumed to be equal
 pooled variance s 2p
 use of TI's 2-SampTInt
when 1 and 2 unknown and possibly not equal: use of TI's 2-SampTInt
Estimation of p
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has a t-distribution with n-1 degrees of freedom
know formula and understand how to use
Estimation of p1-p2
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know formula and understand how to use
Hypotheses Tests (7 hours)
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Know what the null and alternative hypotheses are
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research hypothesis
know how to choose them
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Know what a type I error is
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Know what a type II error is
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Know what  and  represent
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Be able to identify one tail and two tail tests
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Know what p-value represents; how is p-value defined in terms of z statistic for
various one and two-tailed tests
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When  known: be able to perform hypothesis tests concerning  using decision rule in
terms of
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When  unknown: be able to perform hypothesis tests concerning  using decision rule
in terms of
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sample mean x
z-statistic
p value
sample mean x
t-statistic
p value
Know how to use TI calculator’s statistical testing functions for hypothesis tests
comparing 1 and 2
o when 1 and 2 known
o when 1 and 2 unknown but assumed to be equal
o when 1 and 2 unknown and possibly not equal
Simple Linear Regression (3 hours)
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Recognize bivariate data
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Use given formulas to compute
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The sample regression equation (least squares line)
The sample coefficient of determination and the correlation coefficient
Interpreting your results