Webpage http://web.missouri.edu/~dls6w4 Syllabus Calendar
Practice Materials Homework Exam Information
Slide 6
Make sure you have access to Blackboard You must either:
Activate your stlcc email account Update Blackboard to different
email Otherwise, you will not receive emails You are still
responsible for all emails sent regardless of receipt When/if you
send me an email, please put Stats Night in the subject line If you
do not, I wont answer it Blackboard
Slide 7
Homework Long and painful Absences will not excuse you from
completing homework All will be posted on the webpage Youll need to
have a strong understanding of the material Group work I will take
your top 5 scores I do not know how many we will have
Slide 8
Exams 4 exams Final is cumulative I will drop your lowest exam
score of the first three The final exam counts You will be allowed
a notecard for formulas and a non- programmable calculator
Slide 9
Project Paper, no minimum page requirement Do something that
interests you Check webpage for details/deadlines Failure to
complete the paper as required will result in the loss of an
additional letter grade
Slide 10
Attendance Attendance includes being present, but it also
includes: Not disrupting class Being attentive Not excessively
talking Not doing anything I deem annoying This will cost you
attendance credit If you come in after roll call, it is your job to
notify me in person that day
Slide 11
Point Breakdown Exams: 60% Three Midterm exams: 100 points each
Final Exam (cumulative): 100 points Homework: 30% Each homework
worth fifty points each Ill count the top 5 Project: 10%
Attendance: Loss of 3%
Slide 12
Introductory Material
Slide 13
Some Basics Descriptive Statistics Allow us to get a sense of
things Inferential Tools Allow us to reach some conclusion
Estimation, Hypothesis Testing
Slide 14
Where does data come from? Experiments Process generating
outcomes Design is important Surveys Closed-end Questions Open-end
Questions Demographics Interviews/Observation
Slide 15
Stop and Think What kinds of things can go wrong with
surveys?
Slide 16
What can go wrong? Potential Problems Interviewer Bias
Non-response Bias Selection Bias Observer Bias Measurement Error
Validity Internal Eliminating useless info External Results beyond
original
Slide 17
Key Terms Population All possible observations Sample A portion
of the population Is error (sample) worth the lower cost
(population)?
Slide 18
Sampling Techniques Statistical Sampling Based on chance
Nonstatistical Sampling Not on chance Simple Random Sampling All
possible Stratified Random Sampling Into levels Systematic Random
Sampling Every kth Cluster Sampling Break into groups
Slide 19
Types of Data Quantitative v. Qualitative Quantitative
Numerical Qualitative Categorical Time-series v. Cross-Section
Time-series one value, many times Cross-section many values, one
time
Slide 20
What level are the data? Nominal Simplest form, no rank implied
Ordinal Rank data Interval Difference measure, no true zero Ratio
Consistent, true zero
Slide 21
Describing Data Frequency Distribution Reports how often values
occur Classifies observations by class Relative Frequency How often
one value occurs compared to sample Usually expressed in percentage
RF = (f i )/(n)
Slide 22
Describing Data Grouped Frequency Distribution Classifies data
into groups Groups must be: Mutually Exclusive All-Inclusive
Equal-Width Free of empty classes (if possible)
Slide 23
Describing Data Grouped Frequency Distribution How to determine
groups Determine number of groups (2 k n) Establish width of
classes Determine boundaries for classes Count values in each class
Both types can be built into a histogram Also can construct
Cumulative Frequency Distribution and build an ogive
Slide 24
Describing Data Other methods Bar Chart Pie Chart Stem-and-Leaf
Diagram Line Chart (Time graph) Scatter Plot Can see relationship
between X and Y Demand/Supply curves (Economics)
Slide 25
Describing Data May want to examine two variables Use Joint
Frequency Distribution How? Get data containing two responses Build
table Find joint occurrences Sum rows and columns for marginal
frequencies
Slide 26
Numerical Measures Weve done some simple measures Now lets
actually do some calculations Before we start: Parameter-based on
population Statistic-based on sample
Slide 27
Center and Location Population Mean () A.k.a. average For
population, sum of deviations=0 Sample Mean (x-bar) Based on a
selected sample All means subject to distortion by extrema
Slide 28
Center and Location Median Middle value of the data
Odd-numbered sample=find middle Even-numbered sample=find middle of
middle two
Slide 29
Center and Location Taken together, the mean and median show
skewness of data Median>Mean = Left Skewed Median
Slide 30
Center and Location Mode Value occuring most often
Occasionally, a set of data has no mode
Slide 31
Center and Location Weighted Mean Same idea as mean, just
unequal weights on observations Percentiles Describes where a
particular value is located in data i = (p/100)*(n) If i is integer
average (i, i + 1) If i is not integer round up Quartiles Dividing
the data into four equal parts Qua implies four (quarter, quart,
etc.)
Slide 32
Be careful! These not always useful for qualitative data
masquerading as quantitative Need further assumptions/theory to
hold
Slide 33
Measures of Variation Variation The spread of the data Range =
Maximum minimum Sensitive to extrema Considered weak Interquartile
Range = Third Q First Q Softens dependence on extrema
Slide 34
Measures of Variation Variance ( 2 ) Measure of dispersion or
spread Equation Shortcut Standard Deviation () VAR Sample (s 2, s)
and Population calculated in similar fashion Use n-1 instead of N
in denominator
Slide 35
Combining and Coefficient of Variation (CV) Relative variation
with different means (/)*(100%) for population Replace with sample
measures for sample CV Empirical Rule (with bell-shape) 68% within
95% within 2 All within 3
Slide 36
Standardizing Values Allows us to compare different data
effectively Z-value (population) = (x ) / X is value of interest
Based on a standard normal distribution Mean = 0, Variance = 1 This
will be important from now until the end
Slide 37
Probability The chance that something will happen Sample Space
all possible events Event Element(s) of sample space Mutually
Exclusive Independence v. Dependence Ways to determine Classical
Relative Frequency Subjective
Slide 38
Probability Some rules to know All probabilities are between 0
and 1 (incl.) The sum of all probabilities is 1 Complement Rule
Probability of X = 1 Probability of all others Addition Rule
Probability of X or Y = Pr(X) + Pr(Y) Pr(X and Y) If events
mutually exclusive = Pr(X) + Pr(Y)
Slide 39
Probability Some simple examples Probability of tails on fair
coin? Probability of rolling a 1 or 6 on fair die? Probability of
drawing a heart from standard deck?
Slide 40
Probability Conditional Probability The probability that one
event occurs when you know something else has happened Pr(X|Y) =
Pr(X and Y)/Pr(Y) If the events are independent, =Pr(X)
Multiplication Rule Pr(X and Y) = Pr(X)(Pr(Y|X)) Independent =
Pr(X)Pr(Y)