statistical analysis - lecture 10
TRANSCRIPT
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STATISTICAL INFERENCE
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Recap
Statistics
Descriptive Inferential
Estimation
Point Estimation
µ = ഥ𝒙 and p = ෝ𝒑
Interval
(Lower Limit, Upper Limit)
Hypothesis
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Statistical Inference
➢ The statistical inference is the process of making
judgment about a population based on the
properties of a random sample from the
population.
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Estimators
Estimator (Sample Statistic)
Population Parameter
ഥ𝒙 ESTIMATES µ
S2 ESTIMATES σ2
S ESTIMATES σ
ෝ𝒑 ESTIMATES p
ഥ𝒙𝟏- ഥ𝒙𝟐 ESTIMATES µ1 - µ2
ෝ𝒑𝟏 − ෝ𝒑𝟐 ESTIMATES p1 – p2
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Confidence Interval (CI)
Population Parameter = Estimator ± Margin of Error (d)
Where:
Margin of Error (d) = Critical Value of Z * Standard Error
Population Parameter ∈
[Estimator − Margin of Error (d) , Estimator + Margin of Error (d)]
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Confidence Interval (CI) for Single Mean
𝝁 = ഥ𝒙 ± 𝒛𝝈
𝒏
𝝁 ∈ [ഥ𝒙 − 𝒛𝝈
𝒏, ഥ𝒙 + 𝒛
𝝈
𝒏]
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Confidence Interval (CI) for Single Proportion
𝒑 = ෝ𝒑 ± 𝒛ෝ𝒑 𝟏 − ෝ𝒑
𝒏
𝒑 ∈ [ ෝ𝒑 − 𝒛ෝ𝒑 𝟏−ෝ𝒑
𝒏, ෝ𝒑 + 𝒛
ෝ𝒑 𝟏−ෝ𝒑
𝒏]
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Standard Error for Different Sample Statistics
Estimator (Sample Statistic)
Standard Error
ഥ𝒙
ෝ𝒑
ഥ𝒙𝟏- ഥ𝒙𝟐
ෝ𝒑𝟏 − ෝ𝒑𝟐
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Confidence Interval (CI)
(1-α) Confidence Level
α Confidence Coefficient
−𝒛(𝟏− Τ𝜶 𝟐) 𝒛(𝟏− Τ𝜶 𝟐)
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Critical Value for Z:
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Confidence Interval (CI) for the Difference Between
Two Population Means
𝝁𝟏− 𝝁𝟐 = ഥ𝒙𝟏− ഥ𝒙𝟐 ± 𝒛σ𝟏𝟐
𝒏𝟏
+σ𝟐𝟐
𝒏𝟐
𝝁𝟏− 𝝁𝟐 ∈
[ ഥ𝒙𝟏− ഥ𝒙𝟐 − 𝒛σ𝟏𝟐
𝒏𝟏
+σ𝟐𝟐
𝒏𝟐
, ഥ𝒙𝟏− ഥ𝒙𝟐 + 𝒛σ𝟏𝟐
𝒏𝟏
+σ𝟐𝟐
𝒏𝟐
]
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Confidence Interval (CI) for Difference Between
Two Population Proportions
(𝒑𝟏 − 𝒑𝟐) = (ෝ𝒑𝟏 − ෝ𝒑𝟐) ± 𝒛ෝ𝒑𝟏𝟏−ෝ𝒑
𝟏
𝒏𝟏
+ෝ𝒑𝟐𝟏−ෝ𝒑
𝟐
𝒏𝟐
(𝒑𝟏 − 𝒑𝟐) ∈
[ ෝ𝒑𝟏− ෝ𝒑𝟐 − 𝒛ෝ𝒑𝟏𝟏−ෝ𝒑
𝟏
𝒏𝟏
+ෝ𝒑𝟐𝟏−ෝ𝒑
𝟐
𝒏𝟐
, ෝ𝒑𝟏− ෝ𝒑𝟐 + 𝒛ෝ𝒑𝟏𝟏−ෝ𝒑
𝟏
𝒏𝟏
+ෝ𝒑𝟐𝟏−ෝ𝒑
𝟐
𝒏𝟐
]
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Hypothesis Tests
Statistics
Descriptive Inferential
Estimation
Point Estimation
µ = ഥ𝒙 and p = ෝ𝒑
Interval
(Lower Limit, Upper Limit)
Hypothesis
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Hypothesis
➢ It is a claim or statement about a population
parameter.
➢ For example, the average age of college students
equals 23 years.
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Null Hypothesis (Ho)
➢ Usually, the null hypothesis represents a statement
of “no effect,” “no difference,” or, put another way,
“things haven’t changed.”
➢ For example, Ho: μ = 23
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Alternate Hypothesis (Ha) or (H1)
➢ This test is a statistical test is designed to assess the
strength of the evidence (data) against the null
hypothesis.
➢ For example, H1: μ ≠ 23
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Test Statistics
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The Critical Region
Hypothesis Tests
Two-Tailed
Ho: μ = 23
H1: μ ≠ 23
Left-Tailed
Ho: μ ≥ 23
H1: μ < 23
Right-Tailed
Ho: μ ≤ 23
H1: μ > 23
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The Critical Region
Fail to reject HoFail to reject Ho
Fail to reject Ho
α: Significance Level
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Steps for Hypothesis Test
➢ Step 1: State the null and alternative hypothesis (Ho and
H1).
➢ Step 2: Specify the suitable test statistic.
➢ Step 3: Compute the critical value for the statistic.
➢ Step 4: Make a decision.
Reject Ho or Fail to reject Ho
➢ Step 5: Make a conclusion
Accept H1 or we don’t have enough evidence to support H1
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In recent years, the mean age of all college students
in a city has been 23. A random sample of 42
students revealed a mean age of 23.8 suppose their
ages are normally distributed with a population
standard deviation of σ=2.4 can we infer at α=0.05
that the population mean has changed?
Example (1)
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Solution𝜇 = 23 𝑛 = 42 𝜎 = 2.4 ҧ𝑥 = 23.8
Step 1: State the null and alternative hypothesis (Ho and H1)
Null hypothesis (Ho) 𝜇 = 23
alternative hypothesis (H1) 𝜇 ≠ 23
Step 2: Specify the suitable test statistic
Since 𝑛 ≥ 30 and 𝜎 is known, we will use Z-statistic
𝛍 = 𝟐𝟑 𝛔 = 𝟐. 𝟒 𝐧 = 𝟒𝟐 ത𝐱 = 𝟐𝟑. 𝟖
𝛍ത𝐱 = 𝛍 = 𝟐𝟑
𝛔ത𝐱 =𝛔
𝐧
=𝟐. 𝟒
𝟒𝟐= 𝟎. 𝟑𝟕
𝐳ത𝐱 =ത𝐱 − 𝛍
𝛔ത𝐱
=𝟐𝟑. 𝟖 − 𝟐𝟑
𝟎. 𝟑𝟕= 𝟐. 𝟏𝟔
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SolutionStep 3: Compute the critical value for the statistic
𝟏 − 𝛂 = 𝟎. 𝟗𝟓 𝛂 = 𝟎. 𝟎𝟓𝛂
𝟐=. 𝟎𝟐𝟓
𝟏 −𝛂
𝟐= 𝟎. 𝟗𝟕𝟓 ±𝐳𝟏−𝛂/𝟐 = ±𝟏. 𝟗𝟔
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Solution
Step 5: Make a conclusion
We accept 𝐻1, there is enough evidence that
mean age has changed at 𝛼 = 0.05
Step 4: Make a decision
𝑧 ҧ𝑥 = 2.16 which is greater than 1.96
=> 𝑧 ҧ𝑥 = 2.16 > 1.96, so we reject 𝐻0
Fail to reject Ho
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In the previous example, if the confidence level (α)
= 0.02, what can we infer about the population
mean ?
Example (2)
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Solution𝜇 = 23 𝑛 = 42 𝜎 = 2.4 ҧ𝑥 = 23.8
Step 1: State the null and alternative hypothesis (Ho and H1)
Null hypothesis (Ho) 𝜇 = 23
alternative hypothesis (H1) 𝜇 ≠ 23
Step 2: Specify the suitable test statistic
Since 𝑛 ≥ 30 and 𝜎 is known, we will use Z-statistic
𝛍 = 𝟐𝟑 𝛔 = 𝟐. 𝟒 𝐧 = 𝟒𝟐 ത𝐱 = 𝟐𝟑. 𝟖
𝛍ത𝐱 = 𝛍 = 𝟐𝟑
𝛔ത𝐱 =𝛔
𝐧
=𝟐. 𝟒
𝟒𝟐= 𝟎. 𝟑𝟕
𝐳ത𝐱 =ത𝐱 − 𝛍
𝛔ത𝐱
=𝟐𝟑. 𝟖 − 𝟐𝟑
𝟎. 𝟑𝟕= 𝟐. 𝟏𝟔
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SolutionStep 3: Compute the critical value for the statistic
𝟏 − 𝛂 = 𝟎. 𝟗𝟖 𝛂 = 𝟎. 𝟎𝟐𝛂
𝟐=. 𝟎𝟏
𝟏 −𝛂
𝟐= 𝟎. 𝟗𝟗 ±𝐳𝟏−𝛂/𝟐 = ±𝟐. 𝟑𝟑
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Solution
Step 5: Make a conclusion
We don’t have enough evidence to accept 𝐻1, or
we don’t have enough evidence that mean age
has changed at 𝛼 = 0.02
Step 4: Make a decision
𝑧 ҧ𝑥 = 2.16 which is greater than 1.96
=> 𝑧 ҧ𝑥 = 2.16 < 1.96, so we fail to reject 𝐻0
Fail to reject Ho
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A random sample of 27 observations from a large
population has a mean of 22 and a standard
deviation of 4.8. can we conclude at α = 0.01 that a
population mean is significantly below 24?
Example (3)
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Solution𝜇 = 2𝟒 𝑛 = 𝟐𝟕 𝐬 = 𝟒. 𝟖 ҧ𝑥 = 𝟐𝟐
Step 1: State the null and alternative hypothesis (Ho and H1)
Null hypothesis (Ho) 𝜇 ≥ 23
alternative hypothesis (H1) 𝜇 < 23
Step 2: Specify the suitable test statistic
Since 𝑛 < 30 and 𝜎 is unknown, we will use t-statistic
𝝁 = 𝟐𝟒 𝒏 = 𝟐𝟕 𝐬 = 𝟒. 𝟖 ഥ𝒙 = 𝟐𝟐
𝛍ത𝐱 = 𝛍 = 𝟐𝟒
𝛔ത𝐱 =𝒔
𝐧
=𝟒. 𝟖
𝟐𝟕= 𝟎. 𝟗𝟐𝟒
𝒕 =ത𝐱 − 𝛍
𝛔ത𝐱
=𝟐𝟐 − 𝟐𝟒
𝟎. 𝟗𝟐𝟒= −𝟐. 𝟏𝟔𝟓
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SolutionStep 3: Compute the critical value for the statistic
𝟏 − 𝛂 = 𝟎. 𝟗𝟖 𝛂 = 𝟎. 𝟎𝟏
−𝒕𝛂 𝟐𝟕 − 𝟏 = −𝒕𝟎.𝟎𝟏 𝟐𝟔 = −𝟐. 𝟒𝟕𝟗
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Solution
Step 5: Make a conclusion
We don’t have enough evidence to accept 𝐻1, or
we fail to support 𝐻1
Step 4: Make a decision
𝑡 = 2.165 which is greater than -2.479 ,
So we fail to reject 𝐻0
Fail to reject Ho-tα