Chapter 01 - An Introduction to Business Statistics
Chapter 01
An Introduction to Business Statistics
Multiple Choice Questions
10. Statistical methods help to:
A. Demonstrate the need for improvement
B. Identify ways to make improvements
C. Assess whethe
Chapter 03 - Descriptive Statistics: Numerical Methods
Chapter 03
Descriptive Statistics
Multiple Choice Questions Part 1
11. A(n) _ is a graph of a cumulative distribution.
A. Histogram
B. Scatter plot
C. Ogive plot
D. Pie Chart
AACSB: Reflective Thinkin
Chapter 05 - Discrete Random Variables
Chapter 05
Discrete Random Variables
Multiple Choice Questions
9. If p = .1 and n = 5, then the corresponding binomial distribution is
A. Right skewed
B. Left skewed
C. Symmetric
D. Bimodal
AACSB: Reflective Thinking
Chapter 08 - Confidence Intervals
Chapter 08
Confidence Intervals
Multiple Choice Questions
15. The t distribution approaches the _ as the sample size _.
A. Binomial, increases
B. Binomial, decreases
C. Z, decreases
D. Z, increases
AACSB: Reflective Think
574
Chapter 16
Response Surface Methodology
Bread our consists of wheat plus a small number of minor ingredients. Their fourth
experiment was concerned with the effects of three such ingredients (labeled design factors
B, C, and D) on loaf volume. An orth
572
Chapter 16
Response Surface Methodology
primary advantage of orthogonal blocking as compared with nonorthogonal blocking is that
an orthogonally blocked design gives the smallest values of Var(Y ), Var(i ), Var(ii ), and
Var(ij ). A second advantage i
16.4
571
Properties of Second-Order Designs: CCDs
where n is the total number of observations; that is, n
nf + 2p + n0 . So, a central
composite design with nf factorial points and 2p axial points can be made orthogonal by
appropriate choice of or n0 . Fo
562
Chapter 16
Response Surface Methodology
where hats on the parameters denote the least squares estimates. Although it is possible to
obtain explicit formulae for the least squares estimates for any specic design, the formulae
for the quadratic paramete
16.3
565
Second-Order Designs and Analysis
with the linear effects have been added (pooled) together, as have those of the quadratic
effects and those of the interaction (cross product) effects. Sequential, or Type I, sums of
squares are listed for each o
568
Chapter 16
Example 16.3.3
Response Surface Methodology
Acid copper pattern plating experiment, continued
In Example 16.3.2, page 563, a second-order model was tted to data collected from a central
composite design. The experiment was run in order to s
566
Chapter 16
Response Surface Methodology
Before settling on a nal model, we should check the lack of t of the second-order
model. The only replication consisted of two center-point observations with values 4.32 and
2
0.00245, so ssPE 0.00245
4.25. The
16.3
567
Second-Order Designs and Analysis
ii s are negative, then the tted model is concave down and has a maximum at the stationary
point. If all of the ii s are positive, then the tted model is concave up and has a minimum
at the stationary point. If s
16.4
Properties of Second-Order Designs: CCDs
569
so the w1 -axis has not been rotated very far from the A-axis (or x1 -axis). We can verify this
from Figure 16.4 on page 564, which shows the surface contours with axes almost parallel to
the A and B axes.
16.5
A Real Experiment: Flour Production Experiment, Continued
573
If the numbers of center points, n0a and n0f , in the blocks can be chosen to satisfy this
equation, then the design will be rotatable and can be orthogonally blocked. When this is
not pos
570
Chapter 16
Response Surface Methodology
of a design, since data are generally collected without knowing in which direction from the
design center the stationary point of the tted surface will be located.
Rotatable Central Composite Designs Suppose we
Estimating the Population Variance
(section 8.4)
In sections 8.1, and 8.2 we were interested
in estimating the mean, , of a population
However, sometimes in statistical analysis,
the researcher is more interested in the
population variance, 2
As an exampl
The Sampling Distribution of a Sample
Mean (section 7.2)
Suppose you take a random sample of size n
from a population that has mean and
standard deviation
x1 , x2 , x3 , , xn
Then calculate the sample mean:
x
x
i
n
x
is also a random variable
Page 1 of 1
Sampling from a Finite Population (section 7.2)
Examples 7.1, 7.2 and 7.3 were based on the
assumption that the population was infinite or
was extremely large
But if your sample (of size n) was taken from a
fairly small population (of size N), a statistic
Chapter 8 Statistical Inference: Estimation
for Single Populations
Confidence Intervals for (sections 8.1/8.2)
We now move into a statistical topic called
statistical inference
the goal of inference is to draw
conclusions (make inferences) about a
popula
Statistical Science 2035
Section 001
Review Class for Midterm1
Chapter 1:
Introduction to
Statistics
Statistics, application of statistics
Descriptive vs Inferential statistics.
Population, Census, Sample.
Parameter vs Statistics.
Variables and Data.
Uniform Distribution
Examples
The Uniform Probability Distribution
Bernd Schroder
Bernd Schroder
The Uniform Probability Distribution
Louisiana Tech University, College of Engineering and Science
Uniform Distribution
Examples
Definition.
Bernd Schroder
Th
Chapter 6:
Continuous
Distributions
Learning Objectives
LO1
LO2
LO3
LO4
Solve for probabilities in a continuous uniform
distribution.
Solve for probabilities in a normal distribution using z
scores and for the mean, the standard deviation, or a
value of x
Chapter 6:
Continuous
Distributions
Learning Objectives
LO1
LO2
LO3
LO4
Solve for probabilities in a continuous uniform
distribution.
Solve for probabilities in a normal distribution using z
scores and for the mean, the standard deviation, or a
value of x
Chapter 7:
Sampling and
Sampling
Distributions
Learning Objectives
LO1
Contrast sampling to census and differentiate among
different methods of sampling, which include simple,
stratified, systematic, and cluster random sampling; and
convenience, judgment,