Exploring Marketing Research William G. Zikmund Chapter 21: Univariate Analysis.
Transcript of Exploring Marketing Research William G. Zikmund Chapter 21: Univariate Analysis.
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Exploring Marketing Research
William G. Zikmund
Chapter 21:
Univariate Analysis
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Univariate Statistics
• Test of statistical significance
• Hypothesis testing one variable at a time
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Hypothesis
• Unproven proposition
• Supposition that tentatively explains certain facts or phenomena
• Assumption about nature of the world
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Hypothesis
• An unproven proposition or supposition that tentatively explains certain facts or phenomena– Null hypothesis– Alternative hypothesis
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Null Hypothesis
• Statement about the status quo
• No difference
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Alternative Hypothesis
• Statement that indicates the opposite of the null hypothesis
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Significance Level
• Critical probability in choosing between the null hypothesis and the alternative hypothesis
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Significance Level
• Critical Probability
• Confidence Level
• Alpha
• Probability Level selected is typically .05 or .01
• Too low to warrant support for the null hypothesis
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0.3 : oH
The null hypothesis that the mean is equal to 3.0:
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0.3 :1 H
The alternative hypothesis that the mean does not equal to 3.0:
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A Sampling Distribution
x
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x
A Sampling Distribution
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LOWER LIMIT
UPPERLIMIT
A Sampling Distribution
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Critical values of
Critical value - upper limit
n
SZZS X or
225
5.196.1 0.3
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1.096.1 0.3
196. 0.3
196.3
Critical values of
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Critical value - lower limit
n
SZZS
X- or -
225
5.196.1- 0.3
Critical values of
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1.096.1 0.3
196. 0.3
804.2
Critical values of
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Region of Rejection
LOWER LIMIT
UPPERLIMIT
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Hypothesis Test
2.804 3.196 3.78
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Accept null Reject null
Null is true
Null is false
Correct-Correct-no errorno error
Type IType Ierrorerror
Type IIType IIerrorerror
Correct-Correct-no errorno error
Type I and Type II Errors
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Type I and Type II Errorsin Hypothesis Testing
State of Null Hypothesis Decisionin the Population Accept Ho Reject Ho
Ho is true Correct--no error Type I errorHo is false Type II error Correct--no error
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Calculating Zobs
xs
xz
obs
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X
obs S
XZ
Alternate Way of Testing the Hypothesis
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X
obs SZ
78.3
1.
0.378.3
1.
78.0 8.7
Alternate Way of Testing the Hypothesis
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Choosing the Appropriate Statistical Technique
• Type of question to be answered
• Number of variables– Univariate– Bivariate– Multivariate
• Scale of measurement
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PARAMETRICSTATISTICS
NONPARAMETRICSTATISTICS
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t-Distribution
• Symmetrical, bell-shaped distribution
• Mean of zero and a unit standard deviation
• Shape influenced by degrees of freedom
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Degrees of Freedom
• Abbreviated d.f.
• Number of observations
• Number of constraints
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or
Xlc StX ..
n
StX lc ..limitUpper
n
StX lc ..limitLower
Confidence Interval Estimate Using the t-distribution
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= population mean
= sample mean
= critical value of t at a specified confidence
level
= standard error of the mean
= sample standard deviation
= sample size
..lct
X
XSSn
Confidence Interval Estimate Using the t-distribution
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xcl stX
17
66.2
7.3
n
S
X
Confidence Interval Estimate Using the t-distribution
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07.5
)1766.2(12.27.3limitupper
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33.2
)1766.2(12.27.3limitLower
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Hypothesis Test Using the t-Distribution
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Suppose that a production manager believes the average number of defective assemblies each day to be 20. The factory records the number of defective assemblies for each of the 25 days it was opened in a given month. The mean was calculated to be 22, and the standard deviation, ,to be 5.
XS
Univariate Hypothesis Test Utilizing the t-Distribution
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20 :
20 :
1
0
H
H
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nSS X /25/5
1
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The researcher desired a 95 percent confidence, and the significance level becomes .05.The researcher must then find the upper and lower limits of the confidence interval to determine the region of rejection. Thus, the value of t is needed. For 24 degrees of freedom (n-1, 25-1), the t-value is 2.064.
Univariate Hypothesis Test Utilizing the t-Distribution
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:limitLower 25/5064.220 .. Xlc St 1064.220
936.17
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:limitUpper 25/5064.220 ..
Xlc St 1064.220
064.20
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X
obs S
Xt
1
2022
1
2
2
Univariate Hypothesis Test t-Test
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Testing a Hypothesis about a Distribution
• Chi-Square test
• Test for significance in the analysis of frequency distributions
• Compare observed frequencies with expected frequencies
• “Goodness of Fit”
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i
ii )²( ²
E
EOx
Chi-Square Test
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x² = chi-square statisticsOi = observed frequency in the ith cellEi = expected frequency on the ith cell
Chi-Square Test
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n
CRE ji
ij
Chi-Square Test Estimation for Expected Number
for Each Cell
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Chi-Square Test Estimation for Expected Number
for Each Cell
Ri = total observed frequency in the ith rowCj = total observed frequency in the jth columnn = sample size
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2
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Univariate Hypothesis Test Chi-square Example
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50
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Univariate Hypothesis Test Chi-square Example
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Hypothesis Test of a Proportion
is the population proportion
p is the sample proportion
is estimated with p
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5. :H
5. :H
1
0
Hypothesis Test of a Proportion
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Hypothesis Test of a Proportion: Another Example
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Hypothesis Test of a Proportion: Another Example
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