ECON 3818

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# ECON 3818
## Chapter 21
### Kyle Butts
### 25 August 2021

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## Chapter 21: Comparing Two Means

---
# Two-Sample Framework

Comparing two populations is one of the most common situations in statistics. These are called .hi.kelly[two-sample problems].

Can divide into groups, A and B
      
- .ex[Example:] Women vs. Men; Econ Majors vs. Non-Econ Majors; Treated vs. Control
      
We want to know if they differ along some measurable margin
      
- .ex[Example:] salary, hours of homework per week, health

This is different from the matched pairs set up because:

- We have a separate sample for each group and we cannot match the observations

---
# Two-Sample Framework
Consider two groups, A and B. You have the following information for each group:

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<table class="gt_table">
  
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    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Population/Group</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Sample Mean</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Standard Deviation</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">A</td>
<td class="gt_row gt_left">$ \bar{X}_A $</td>
<td class="gt_row gt_left">$ \sigma_A $</td></tr>
    <tr><td class="gt_row gt_left">B</td>
<td class="gt_row gt_left">$ \bar{X}_B $</td>
<td class="gt_row gt_left">$ \sigma_B $</td></tr>
  </tbody>
  
  
</table>
</div>

We use `$\bar{X}_A$` and `$\bar{X}_B$` to say something about the difference in population means, `$\mu_A - \mu_B$`

- Construct a confidence interval for `$\mu_A-\mu_B$`

- Test the hypothesis `$H_0: \mu_A - \mu_B = 0$`

---
# Conditions for Two-Sample Inference

We use `$\bar{X}_A$` and `$\bar{X}_B$` to say something about `$\mu_A - \mu_B$`

- We have two SRS's from two distinct populations

- The two samples are independent of one another

- We measure the same response variable for both samples

- Both populations are normally distributed

- In practice, it is enough the distributions have similar shapes and that the data have no strong outliers.

---
# Distribution of \$ \bar{X}_A\$ and \$\bar{X}_B\$

If the sample mean, `$\bar{X}_i \sim N\left(\mu_i, \frac{\sigma^2_i}{n}\right)$` for `$i \in A, B$`, then:

1. `$\bar{X}_A$` and `$\bar{X}_B$` is normally distributed

2. `$E[\bar{X}_A - \bar{X}_B] = E[\bar{X}_A] - E[\bar{X}_B]$`

3. `$V[\bar{X}_A - \bar{X}_B] = V[\bar{X}_A] + V[\bar{X}_B]$` (by independence)

--
To summarize:
$$ 
\bar{X}_A - \bar{X}_B \sim N(\mu_A-\mu_B, \frac{\sigma^2_A}{n} + \frac{\sigma^2_B}{n})
$$

---
# Distribution of \$ \bar{X}_A\$ and \$\bar{X}_B\$

Therefore, when both `$\sigma^2$` are known:

$$
\frac{(\bar{X}_A-\bar{X}_B) - (\mu_A-\mu_B)}{\sqrt{\frac{\sigma^2_A}{n_A} + \frac{\sigma^2_B}{n_B}}} \sim N(0,1)
$$

---
# Distribution of \$ \bar{X}_A\$ and \$\bar{X}_B\$

As we mentioned, we don't always know the population variance, `$\sigma^2$`.

If we don't know these values, we can use the sample standard deviations `$s_A$` and `$s_B$` as estimators.

The standard error for the difference in sample means is:

$$
SE_{\bar{X}_A-\bar{X}_B}=\sqrt{\frac{s^2_A}{n_A}+\frac{s^2_B}{n_B}}
$$

---
# Distribution of \$ \bar{X}_A\$ and \$\bar{X}_B\$

Since we estimate the sample standard deviations, we should use the `$\daisy{t}$`-distribution

$$
\frac{\bar{X}_A-\bar{X}_B - (\mu_A-\mu_B)}{\sqrt{\frac{s^2_A}{n_A} + \frac{s^2_B}{n_B}}}
$$

can be approximated by the `$\daisy{t}$`-distribution, where the degrees of freedom is `$min\{n_A, n_B\}-1$`

- Statistical software can be more exact, but the formulas get complicated

---
# Two-Sample Confidence Interval

<div class="subheader alice">
$ \sigma^2 $ Known
</div>

A confidence interval for `$\mu_A-\mu_B$` with level of confidence `$C$`:

$$
(\bar{X}_A-\bar{X}_B) \pm Z^{(1-C)/2} \cdot \sqrt{\frac{\sigma^2_A}{n_A}+\frac{\sigma^2_B}{n_B}}
$$

---
# Two-Sample Confidence Interval

<div class="subheader alice">
$ \sigma^2 $ Known
</div>

Say we have two groups -- athletes and non-athletes and we're asked to construct a 95\% confidence interval for the difference in GPA `$\mu_A-\mu_{NA}$`

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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Standard Deviation</th>
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    <tr><td class="gt_row gt_left">Athletes</td>
<td class="gt_row gt_left">$ \bar{X}=2.8 $</td>
<td class="gt_row gt_left">$ \sigma = 0.4 $</td>
<td class="gt_row gt_right">15</td></tr>
    <tr><td class="gt_row gt_left">Non-athletes</td>
<td class="gt_row gt_left">$ \bar{X}=2.9 $</td>
<td class="gt_row gt_left">$ \sigma = 0.5 $</td>
<td class="gt_row gt_right">25</td></tr>
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</div>

$$ 
CI= (2.8-2.9) \pm Z_{0.025}\cdot \sqrt{\frac{0.4^2}{15}+\frac{0.5^2}{25}} = [-0.38, 0.18]
$$

---
# Two-Sample Confidence Interval

<div class="subheader alice">
$ \sigma^2 $ Unknown
</div>

Since we have to estimate `$\sigma^2$` for both samples, we need to use the `$\daisy{t}$`-distribution to find the critical value:

$$ 
(\bar{X}_A-\bar{X}_B) \pm t^{n-1, \frac{1-C}{2}} \cdot \sqrt{\frac{s^2_A}{n_A}+\frac{s^2_B}{n_B}}
$$

---
# Two-Sample Confidence Interval

<div class="subheader alice">
$ \sigma^2 $ Unknown
</div>

We have 2 groups of students, and we're asked to construct 90\% confidence interval for difference in test scores, `$\mu_A-\mu_B$`

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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Group</th>
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Standard Deviation</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Sample Size</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
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<td class="gt_row gt_left">$ s = 9 $</td>
<td class="gt_row gt_right">60</td></tr>
    <tr><td class="gt_row gt_left">Control</td>
<td class="gt_row gt_left">$ \bar{X} = 73 $</td>
<td class="gt_row gt_left">$ s = 5 $</td>
<td class="gt_row gt_right">20</td></tr>
  </tbody>
  
  
</table>
</div>

$$
CI=(76-73) \pm t^{0.05}_{19} \cdot \sqrt{\frac{9^2}{60}+\frac{5^2}{20}} =[0.21, 5.79]
$$

---
# Two-Sample Hypothesis Testing

<div class="subheader alice">
$ \sigma^2 $ Known
</div>

Researchers are asking college graduates how old they were when they had their first job. Researchers are curious to see if students who attended state schools got jobs earlier in life than those who attended private colleges.

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<td class="gt_row gt_left">$ \bar{X}= 18.19 $</td>
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<td class="gt_row gt_right">20</td></tr>
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<td class="gt_row gt_left">$ \bar{X}= 20.98 $</td>
<td class="gt_row gt_left">$ \sigma = 4.2 $</td>
<td class="gt_row gt_right">20</td></tr>
  </tbody>
  
  
</table>
</div>

Test the following hypothesis at the `$\alpha=0.05$` significance level:

$$ 
H_0: \mu_A - \mu_B = 0 
$$

$$ 
H_1: \mu_A - \mu_B <0 
$$

---
# Two-Sample Hypothesis Testing

<div class="subheader alice">
$ \sigma^2 $ Known
</div>

Calculate p-value using:
$$
P(\bar{X}_A-\bar{X}_B \leq 18.19-20.98 \ \vert \ \mu_A - \mu_B = 0)
$$

$$ 
P\left(\frac{\bar{X}_A-\bar{X}_B-(\mu_A-\mu_B)}{\sqrt{\frac{\sigma_A^2}{n_A} + \frac{\sigma_B^2}{n_B}}} \leq \frac{18.19-20.98 - (0)}{\sqrt{\frac{3.8^2}{20}+\frac{4.2^2}{20}}} \right)
$$

<br/>

`$p$`-value `$= P(Z \leq -2.2) = 0.014 \implies \text{reject } H_0$` because p-value `$\leq \alpha=0.05$`

---
# Two-Sample Hypothesis Testing

<div class="subheader alice">
$ \sigma^2 $ Unknown
</div>

You want to test how attached individuals are to their friends, and whether that is different across people who volunteer for community service versus those who do not.

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<td class="gt_row gt_left">$ \bar{X}= 105.32 $</td>
<td class="gt_row gt_left">$ s = 14.68 $</td>
<td class="gt_row gt_right">57</td></tr>
    <tr><td class="gt_row gt_left">No Service</td>
<td class="gt_row gt_left">$ \bar{X}= 96.82 $</td>
<td class="gt_row gt_left">$ s = 14.26 $</td>
<td class="gt_row gt_right">17</td></tr>
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</table>
</div>

Test the following hypothesis at `$\alpha=0.01$` level:

$$ 
H_0: \mu_A - \mu_B = 0
$$

$$ 
H_1: \mu_A - \mu_B \neq 0
$$

---
# Two-Sample Hypothesis Testing

<div class="subheader alice">
$ \sigma^2 $ Unknown
</div>

$$
t = \frac{\bar{X}_S-\bar{X}_N - (\mu_A - \mu_B)}{\sqrt{\frac{s^2_S}{n_S} + \frac{s^2_N}{n_N}}}  = \frac{105.32-96.82-(0)}{\sqrt{\frac{14.68^2}{57}+\frac{14.26^2}{17}}}
$$

$$ 
\implies t = \frac{8.5}{3.9677} = 2.142
$$

Look at t-table, row with degrees freedom = 16.

`$t_{16}^{0.025} = 2.12$` and `$t_{16}^{0.01} = 2.58$`, this means p-value is in between 0.025 and 0.01, .hi[BUT] it's a two-tailed test so we need to multiply these probabilities by 2:

`$0.02 < p$`-value `$< 0.05 \implies$` Do not reject null at `$\alpha = 0.01$`

---
# Review of Chapter 21

In this chapter, we focus on making inferences about the relationship between the means of two different samples

- Confidence intervals around the difference in means \$ \mu_A - \mu_B \pm \$ margin of error

- Generally testing \$ H_0: \mu_A-\mu_B = 0 \$
      
- You'll be given sample means (\$ \bar{X} \$), standard deviations (\$ \sigma \$ or \$ s \$) and population size (\$ n \$) of each sample.

<br/>      
If you're given `$\sigma$`, use Z-distribution
      
If you're given `$s$`, use t-distribution (unless .hi[both] samples are large enough)

---
# Calculating Margin of Error with Two Samples

Variances known:

$$ 
(\bar{X}_A-\bar{X}_B) \pm Z^{\frac{1-C}{2}} \cdot \sqrt{\frac{\sigma^2_A}{n_A}+\frac{\sigma^2_B}{n_B}}
$$

<br/>

Variances unknown:

$$ 
(\bar{X}_A-\bar{X}_B) \pm t^{n-1, \frac{1-C}{2}} \cdot \sqrt{\frac{s^2_A}{n_A}+\frac{s^2_B}{n_B}}
$$

---
# Calculating test-Statistic

Variances known:

$$ 
Z = \frac{\bar{X}_A-\bar{X}_B-(\mu_A-\mu_B)}{\sqrt{\frac{\sigma_A^2}{n_A} + \frac{\sigma_B^2}{n_B}}} 
$$

<br/>

Varainces unknown:

$$ 
t = \frac{\bar{X}_A - \bar{X}_B - (\mu_A - \mu_B)}{\sqrt{\frac{s^2_A}{n_A}+\frac{s^2_B}{n_B}}}
$$

---
# Clicker Question

You're given the following information about average length of careers in NFL versus MLB.

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    <tr><td class="gt_row gt_left">NFL</td>
<td class="gt_row gt_left">$ \bar{X}= 3.3 $</td>
<td class="gt_row gt_left">$ s = 2.1 $</td>
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<td class="gt_row gt_right">17</td></tr>
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</table>
</div>

You want to construct a 90% confidence interval. Given this information, calculate the margin of error:

---
# Midterm Example

New research has developed a new drug designed to reduce blood pressure. In an experiment, 21 subjects were assigned randomly to the treatment group and receive the experimental drug. The other 23 subjects were assigned to the control group and received a placebo treatment. A summary of these data is:

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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Group</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Sample Mean</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Standard Deviation</th>
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    <tr><td class="gt_row gt_left">Treatment</td>
<td class="gt_row gt_left">$ \bar{X} = 23.48 $</td>
<td class="gt_row gt_left">$ s = 8.01 $</td>
<td class="gt_row gt_right">21</td></tr>
    <tr><td class="gt_row gt_left">Placebo</td>
<td class="gt_row gt_left">$ \bar{X} = 18.52 $</td>
<td class="gt_row gt_left">$ s = 7.15 $</td>
<td class="gt_row gt_right">13</td></tr>
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</div>

We want to test whether there was any difference in means across these two groups:

<ol type = "a">
  <li>State the null and alternative hypothesis  </li>
  <li>Calculate p-value or range of p-values </li>
  <li>Do you reject at $ \alpha=0.05 $ level? </li>
  <li>If you were incorrect in part c, what kind of error did you make?  </li>
</ol>

---
class: clear