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Model Building for Regression Analyses or Multiple Regression Analyses
In the simple regression EXAMPLE, one independent variable (number of truck transfers) explained
over 90% of the variation in Y (number of bottles broken). That was rather go
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QMETH 201, Introduction to Statistical Methods, Section 12
Testing Hypotheses about Differences between Multiple Means
Suppose we want to compare multiple (2, 3, 5, 10, .) populations, or groups, generally. Should we
conduct t-tests for all poss
MODEL
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QMETH 201, Introduction to Statistical Methods, Section 11
Testing Hypotheses and Model Building for Regression Analyses
Testing Hypotheses — now that we know about errors and sums of squares, we can test hypotheses for
regression analyses
x1
I!
COMPLe-ra)
CORRELAWU .‘ Y<——>< KEGKESSION: Y: RX?
Regression'Analysis — Regression Analysis is about explaining variation in outcomes by specifying and
testing statistical relationships among variables. In the two-variable case we usually specify Y
Corn/amp
QMETH 201, Introduction to Statistical Methods, Section 8
B. TESTING HYPOTHESES
YO! A BIG IDEA!
Some practical problems require us to use intervals to estimate values of population parameters —
common when we have little or no information about t
QMETH 201, Introduction to Statistical Methods, Section 9
Testing Hypotheses about Differences
We now examine approaches to test hypotheses about differences between two populations or groups.
Basically, we want to compare two populations or groups on som
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HOW to USE Sampling Distributions
A. INTERVAL ESTIMATION
Due to sampling (meaning, having incomplete information), values of individual sample statistics
seldom equal the value of a population parameter. As noted in the Coca Cola bottling plan
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QMETH 201, Introduction to Statistical Methods, Section 7
[From Descriptive Statistics and Probability to] Inferential Statistics
1. WHAT: Inferential Statistics — Due to limited resources (time, money, persons, space, .), we oﬁen
use data from
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Testing Hypotheses about Differences between Two Population Proportions
The logic for testing differences between two population proportions is similar to the logic for testing
differences between 0 ulation means. "
p p Two ’lTs
Stating Hypothe
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2. Exponential Probability Distribution
Recall, the Poisson distribution is useful for modeling the arrival, or occurrence, of events. For example,
the probability that x visits to our web-site occur within the next minute. The exponential mod
Part C
Probability COMPLETED
PLUS“. MANv Fumes Cauasss Mu. Use
QMETH 201, Introduction to Statistical Methods, Section 4 PROBAB! LITY IDeAs; PROBAGIUTY IDEAS
ARE A EH9 PART OF DATA SCIENCéJ' C FA
Probability Basics [CHARTERED FWANU A L ANA LVS 1.]
Ex Come
QMETH 201, Introduction to Statistical Methods, Section 5
PROBABILITY DISTRIBUTIONS
‘9:
Recall, a random variable is an uncertain numerical quantlty. We distinguish discrete and continuous
random variables.
A. DISCRETE Random Variable — can take on only c
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QMETH 201, Introduction to Statistical Methods, Section 6
B. Probability Distributions — Continuous Random Variables
GENERAL INFORMATION
poHP
Let X be a continuous random variable; f(X) is the associated probability density function, which
rep
Carving?
QMETH 201, Introduction to Statistical Methods, Section 2
THE DISTRIBUTION
— a collection of DATA elements
— the most basic way to organize or classify DATA
— oﬁen the ﬁrst step to transforming DATA into an understandable form
Types — frequency,
Com merge
QMETH 201, Introduction to Statistical Methods, Section 3
3. Variability — Spread or dispersion. Central tendencies are just that, tendencies. For example, a student
can have an overall GPA of 3.0 by earning all “B’s” or by earning no, or some,
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C. NUMBERS (or using numbers to Summarize or Describe distributions)
Recall: 1. Shape, 2. Central Tendency, and 3. Variability
1. Shape — Again, what does the distribution look like? Does it contain gaps? Is it tall or ﬂat? Is it
uniform? Is it
QMETH 201, Introduction to Statistical Methods, Section 1
BACKGROUND IDEAS and DEFINITIONS
1. The WHY:
Information Age, Information Society, Information Revolution OR the Digital Economy
All organizations collect and analyze, or use, INFORMATION; or in th