Question 1
1 out of 1 points
A networked organizational structure feels both like a_ and a_
organizational structure because it supports a rigid structure with well-connected
communication networks.
Answer
Selected Answer:
flat, hierarchical
Correct Answ
VISIONMISSION
History: THE
BEGINNING
ABOUT
SMART
NEW
BEGINNINGS
ABOUT SMART
Smart Communications, Inc. (Smart) is the
Philippines' leading wireless services provider with
52.1 million subscribers on its GSM network as of
Industry
Communications Services
e
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics 6: Descriptive Statistics: Numerical Measures II
Variability
Suppose, you are the purchasing agent of a firm, and you regularly place orders with two different
suppliers. After several months o
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics 5: Descriptive Statistics : Numerical Measures
This lecture introduces different measures of location.
Measures computed for data from a sample are called sample statistics, and measures comput
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics 4:
Exploratory Data Analysis: The Stem and Leaf Display
The techniques of exploratory data analysis consist of simple arithmetic and easy to draw graphs that can
be used to summarize data quick
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics 3
Summarizing Qualitative Data
Frequency Distribution: A frequency distribution is a tabular summary of data showing the number (frequency) of
items in each of several non-overlapping classes.
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics: Lecture 2
Qualitative and Quantitative Data
Qualitative data include labels or names used to identify an attribute of each element. Qualitative data use either the
nominal or ordinal scale of
BUS 172: Introduction to Statisitics
ZbH
Fall 2009
Statistics: Lecture 1 INTRODUCTION
1.0 In everyday usage Statistics refers to numerical facts. As an academic discipline however Statistics
involves much more than numerical facts. In a broad sense, Stati
Statistics 21: Interval Estimation II
Population Proportion
The general form of an interval estimate of a population proportion p is
p bar Margin of error
The sampling distribution of p bar plays a key role in computing the margin of error of
this interva
BUS 172
ZbH
Statistics 20: Interval Estimation I
A point estimator is a sample statistic used to estimate a population parameter. As a point estimator may not
provide the exact value of the population parameter, an interval estimate is often computed by a
BUS 172
ZbH
Statistics 19: Sampling and Sampling Distribution III
Sampling Distribution of p bar
The sampling distribution of p bar is the probability distribution of all possible sample proportions p bar.
x
p bar = -n
where x = the number of elements in
BUS 172
ZbH
Statistics 18: Sampling and Sampling Distributions II
Introduction to Sampling Distributions
The sample mean x bar is the point estimator of population mean , and the sample
proportion p bar is the point estimator of population proportion p. D
BUS 172
ZbH
Statistics 17: Sampling and Sampling Distributions I
A population is the set of all elements of interest in a study, while a sample is a subset of
the population. Numerical measures of a population such as the mean and standard
deviation are c
BUS 172
ZbH
Statistics 16: Continuous Probability Distribution II
Computing Probabilities for any Normal Distribution
Probabilities for all normal distributions can be computed by using the standard normal
distribution. When we have a normal distribution
BUS 172
ZbH
Statistics 15: Continuous Probability Distributions I
In case of discrete random variables the probability function f(x) provides the probability
that the random variable x assumes a particular value. In case of continuous random
variables, th
Statistics 14: Discrete Probability Distribution III
Poisson Distribution
The Poisson distribution is used when it is desirable to determine the probability of obtaining x occurrences over an
interval of time or space. The following assumptions are necess
BUS 172
ZbH
Statistics 13: Discrete Probability Distribution II
Binomial Probability Distribution
Properties of a Binomial Experiment:
1. The experiment consists of a sequence of n identical trials.
2. Two outcomes are possible on each trial. One outcome
BUS 172
ZbH
Statistics 12: Discrete Probability Distributions I
Random Variable
A random variable is defined as a numerical description of the outcome of an experiment.
Each experimental outcome is associated with a specific numerical value which may be d
Statistics 11: Introduction to Probability IV
Bayes Theorem
Often we begin with prior probability estimates of specific events of interest. Then,
from sources such as a sample, a special report, or a product test, we obtain additional
information about th
Statistics 10: Introduction to Probability III
Conditional Probability
Often, the probability of an event is influenced by whether a related event has already
occurred. If A and B are related events, when we already know that the event B has
already occur
BUS 172
ZbH
Statistics 9: Introduction to Probability II
Events and their Probabilities
An event is a collection of sample points.
Example: Refer to the KP & L Project data. Let us define an event C as completion of the
project in 10 months or less.
C = c
BUS 172
ZbH
Lecture 8: Introduction to Probability
Probability is a numerical measure of the likelihood that an event will occur.
Probability values range from 0 to 1.
A probability near 0 indicates that the event is unlikely to occur.
A probability near
BUS 172
ZbH
Lecture 7: Descriptive Statistics: Numerical Measures III
EXPLORATORY DATA ANALYSIS
Five Number Summary
In a five number summary, the following five numbers are used to summarize the data.
1.
The smallest value
2. First quartile ( Q1 )
3. Medi
Statistics: Assignment 2
1.(a) Suppose that we have a sample space with five equally likely experimental outcomes: E 1, E2, E3, E4
and E5.
Let A= cfw_E1, E2, B = cfw_E3, E4, and C = cfw_E2, E3, E5
a. Find P(AUB). Are A and B mutually exclusive? Why?
b. Fi