In a village far away, a man appeared and announced to the villagers that he would buy monkeys for $10 each. The villagers seeing that there were many monkeys around, went out to the jungle and started catching them. The man bought thousands at $10

1
In a village far away, a man appeared and
announced to the villagers that he would buy
monkeys for 10 each.The villagers seeing that
there were many monkeys around, went out to the
jungle and started catching them.The man
bought thousands at 10 each But as supply
started to diminish, the villagers stopped their
effort.
2
The man then announced that he would pay 20 for
a monkey.This renewed the efforts of the
villagers and they started catching monkeys
again.The supply of monkey's diminished even
further and the villagers once again, stopped
their effort.
3
The offer was then increased to 25 per
monkey.The supply of monkeys became so depleted
that it was an effort to even see a monkey let
alone catch one!The man now announced that he
would buy monkeys at 50!However, since he had
to go to the city on business, and left his
assistant to buy on his behalf.
4
The assistant told the villagers 'Look at all
these monkeys that the man has collected. I will
sell them to you for 35 and when the man returns
from the city, you can sell them to him for 50
each.'The villagers rounded up all their
savings andbought back all the monkeys.
5
They never saw the man nor his assistant
again!Now you have a better understanding of
how the stock market works..
6
Chapter Twelve
Chapter 12
7
Figure 12.3 Sampling Design Process
8
Figure 12.7 Non-probability Sampling Techniques
Figure 12.7 Nonprobability Sampling Techniques
Nonprobability Sampling Techniques
Convenience Sampling
Judgmental Sampling
Quota Sampling
Snowball Sampling
Figure 12.9 Probability Sampling Techniques
Probability Sampling Techniques
Simple Random Sampling
Cluster Sampling
Stratified Sampling
Systematic Sampling
9
Chapter 13
10
Definitions and Symbols

• Parameter
• Statistic
• Finite Population Correction
• Precision level
• Confidence interval
• Confidence level

11
The Confidence Interval Approach

• The confidence interval is given by
•  
•  
• We can now set a 95 confidence interval around
  the sample mean of 182.
• The 95 confidence interval is given by
•  
• 1.96
• 182.00 1.96(3.18) 182.00 6.23
•  
• Thus the 95 confidence interval ranges from
  175.77 to 188.23.

12
Figure 13.4 95 Confidence Interval
13
Figure 13A.1 Finding Probabilities Corresponding
to Known Values
14
Figure 13A.3 Finding Values Corresponding to
Known Probabilities Confidence Interval
15
Chapter Sixteen
Chapter 16
16
Frequency Distribution

• In a frequency distribution, one variable is
  considered at a time.
• A frequency distribution for a variable produces
  a table of frequency counts, percentages, and
  cumulative percentages for all the values
  associated with that variable.

17
Statistics Associated with Frequency
DistributionMeasures of Location

• The mean,
• The mode
• Median
• Range
• Variance
• standard deviation
• Type I Error
• Type II Error
• Power of a Test

18
A General Procedure for Hypothesis TestingStep
1 Formulate the Hypothesis

• A null hypothesis is a statement of the status
  quo, one of no difference or no effect. If the
  null hypothesis is not rejected, no changes will
  be made.
• An alternative hypothesis is one in which some
  difference or effect is expected. Accepting the
  alternative hypothesis will lead to changes in
  opinions or actions.
• The null hypothesis refers to a specified value
  of the population parameter (e.g., ),
  not a sample statistic (e.g., ).

19
Cross-Tabulation

• While a frequency distribution describes one
  variable at a time, a cross-tabulation describes
  two or more variables simultaneously.
• Cross-tabulation results in tables that reflect
  the joint distribution of two or more variables
  with a limited number of categories or distinct
  values, e.g., Table 16.3.
• chi-square
• contingency coefficient
• Cramer's V

20
Chapter Seventeen
Chapter 17
21
Hypothesis Tests Related to Differences
Tests of Differences
More Than Two Samples
One Sample
Two Independent Samples
Paired Samples
Means
Means
Means
Means
Proportions
Proportions
Proportions
Proportions
22
Statistics Associated with One-way Analysis of
Variance

• eta2 (?2). The strength of the effects of X
  (independent variable or factor) on Y (dependent
  variable) is measured by eta2 (?2). The value of
  ?2 varies between 0 and 1.
• F statistic. The null hypothesis that the
  category means are equal in the population is
  tested by an F statistic based on the ratio of
  mean square related to X and mean square related
  to error.
• Mean square. This is the sum of squares divided
  by the appropriate degrees of freedom.

23
Hypothesis Testing Related to Differences

• Parametric tests assume that the variables of
  interest are measured on at least an interval
  scale.
• These tests can be further classified based on
  whether one or two or more samples are involved.
• The samples are independent if they are drawn
  randomly from different populations. For the
  purpose of analysis, data pertaining to different
  groups of respondents, e.g., males and females,
  are generally treated as independent samples.
• The samples are paired when the data for the two
  samples relate to the same group of respondents.

24
Chapter Eighteen
Chapter 18
25
Product Moment Correlation

• The product moment correlation, r, summarizes the
  strength of association between two metric
  (interval or ratio scaled) variables, say X and
  Y.
• It is an index used to determine whether a linear
  or straight-line relationship exists between X
  and Y.
• As it was originally proposed by Karl Pearson, it
  is also known as the Pearson correlation
  coefficient. It is also referred to as simple
  correlation, bivariate correlation, or merely the
  correlation coefficient.

26
Product Moment Correlation

27
Product Moment Correlation

• r varies between -1.0 and 1.0.
• The correlation coefficient between two variables
  will be the same regardless of their underlying
  units of measurement.

28
Conducting One-way Analysis of VarianceDecompose
the Total Variation

• The total variation in Y, denoted by SSy, can be
  decomposed into two components
•  
• SSy SSbetween SSwithin
•  
• where the subscripts between and within refer to
  the categories of X. SSbetween is the variation
  in Y related to the variation in the means of the
  categories of X. For this reason, SSbetween is
  also denoted as SSx. SSwithin is the variation
  in Y related to the variation within each
  category of X. SSwithin is not accounted for by
  X. Therefore it is referred to as SSerror.

29
Figure 18.4 A Nonlinear Relationship for Which r
0
.
.
6
.
.
5
4
.
.
3
2
.
1
0
-3
-2
-1
0
1
2
3
30
Regression Analysis

• Regression analysis is used in the following
  ways
• Determine whether the independent variables
  explain a significant variation in the dependent
  variable whether a relationship exists.
• Determine how much of the variation in the
  dependent variable can be explained by the
  independent variables strength of the
  relationship.
• Determine the structure or form of the
  relationship the mathematical equation relating
  the independent and dependent variables.
• Predict the values of the dependent variable.
• Control for other independent variables when
  evaluating the contributions of a specific
  variable or set of variables.
• Regression analysis is concerned with the nature
  and degree of association between variables and
  does not imply or assume any causality.

31
Conducting Bivariate Regression Analysis
Fig. 18.5
32
Plot of Attitude with Duration
9
Attitude
6
3
4.5
2.25
9
6.75
11.25
13.5
15.75
18
Duration of Car Ownership
Figure 18.3
33
Figure 18.6 Bivariate Regression
Figure 18.6 Bivariate Regression
Y
b0 b1 X
34
Table 18.2 Bivariate Regression