Fall 2008. Where I’m from: About Me… My “kids”… About Me…
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- Slide 1
- Fall 2008
- Slide 2
- Where Im from: About Me
- Slide 3
- My kids About Me
- Slide 4
- My personality About Me
- Slide 5
- 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
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- Some Basics Descriptive Statistics Allow us to get a sense of
things Inferential Tools Allow us to reach some conclusion
Estimation, Hypothesis Testing
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- 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?
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- 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
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- 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
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- What level are the data? Nominal Simplest form, no rank implied
Ordinal Rank data Interval Difference measure, no true zero Ratio
Consistent, true zero
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- 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)
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- Describing Data Grouped Frequency Distribution Classifies data
into groups Groups must be: Mutually Exclusive All-Inclusive
Equal-Width Free of empty classes (if possible)
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- 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
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- 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?
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- 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)