11.1 - Reviews

11.1 - Reviews

In this lesson you may need to use Minitab Express to construct frequency tables or two-way contingency tables. We'll start by reviewing these procedures. You will need to construct probability distribution plots for chi-square distributions. In earlier lessons we constructed probability distribution plots for z, t, and F distributions; the procedure is similar for a chi-square distribution. We will also review conditional probabilities and the term independence in this section.

11.1.1 - Frequency Table

11.1.1 - Frequency Table

The following example was first presented in Lesson 2.1.1.2.1.

It uses following data set (from College Board):

MinitabExpress – Frequency Tables

To create a frequency table in Minitab Express:

1. Open the data set
2. On a PC: In the menu bar select STATISTICS > Describe > Tally
3. On a Mac: In the menu bar select Statistics > Summary Statistics > Tally
4. Double click the variable Region in the box on the left to insert the variable into the Variable box
5. Under Statistics, check Counts and Percents
6. Click OK

This should result in the following frequency table:

Tally
Region Count Percent
ENC 5 9.8039%
ESC 4 7.8431%
MA 3 5.8824%
MTN 8 15.6863%
NE 6 11.7647%
PAC 5 9.8039%
SA 9 17.6471%
WNC 7 13.7255%
WSC 4 7.8431%
N= 51
Video Walkthrough

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11.1.2 - Two-Way Contingency Table

11.1.2 - Two-Way Contingency Table

Recall from Lesson 2.1.2 that a two-way contingency table is a display of counts for two categorical variables in which the rows represented one variable and the columns represent a second variable. The starting point for analyzing the relationship between two categorical variables is to create a two-way contingency table. When one variable is obviously the explanatory variable, the convention is to use the explanatory variable to define the rows and the response variable to define the columns; this is not a hard and fast rule though.

MinitabExpress – Constructing a Two-Way Contingency Table

1. Open the data set:
2. On a PC: Select STATISTICS > Cross Tabulation and Chi-square
On a Mac: Select Statistics > Tables > Cross Tabulation and Chi-Square
3. Select Raw data (categorical variable) from the drop down menu
4. Double click the variable Smoke Cigarettes in the box on the left to insert the variable into the Rows box
5. Double click the variable Biological Sex in the box on the left to insert the variable into the Columns box
6. Click OK

This should result in the two-way table below:

Tabulated Statistics: Smoke Cigarettes, Biological Sex
Rows: Smokes Cigaretes | Columns: Biological Sex
Female Male All
No 120 89 209
Yes 7 10 17
All 127 99 226
Cell Contents: Count
Video Walkthrough

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11.1.3 - Probability Distribution Plots

11.1.3 - Probability Distribution Plots

In previous lessons you have constructed probabilities distribution plots for normal distributions, binomial distributions, and $t$ distributions. This week you will use the same procedure to construct a probability distribution plot for the chi-square distribution.

MinitabExpress – Constructing a Probability Distribution Plot

Chi-square tests of independence are always right-tailed tests. Let's find the area of a chi-square distribution with 1 degree of freedom to the right of $\chi^2 = 1.75$. In other words, we're looking up the $p$ value associated with a chi-square test statistic of 1.75.

1. On a PC: from the menu select STATISTICS > Distribution Plot
On a Mac: from the menu select Statistics > Probability Distributions > Distribution Plot
2. Select Display Probability
3. For Distribution select Chi-Square
4. For Degrees of freedom enter 1
5. Select A specified X value
6. Select Right tail
7. For X value enter 1.75

This should result in the following output:

Video Walkthrough

Select your operating system below to see a step-by-step guide for this example.

11.1.4 - Conditional Probabilities and Independence

11.1.4 - Conditional Probabilities and Independence

In Lesson 2 you were introduced to conditional probabilities and independent events. These definitions are reviewed below along with some examples.

Recall that if events A and B are independent then $P(A) = P(A \mid B)$. In other words, whether or not event B occurs does not change the probability of event A occurring.

Conditional Probability

The probability of one event occurring given that it is known that a second event has occurred. This is communicated using the symbol $\mid$ which is read as "given."

For example, $P(A\mid B)$ is read as "Probability of A given B."

Independent Events
Unrelated events. The outcome of one event does not impact the outcome of the other event.

Example: Queens & Hearts

If a card is randomly drawn from a standard 52-card deck, the probability of the card being a queen is independent from the probability of the card being a heart.  If I tell you that a randomly selected card is a queen, that does not change the likelihood of it being a heart, diamond, club, or spade.

Using a conditional probability to prove this:

$P(Queen) = \dfrac{4}{52}=0.077$

$P(Queen \mid Heart) = \dfrac {1}{13} = 0.077$

Example: Gender and Pass Rate

Data concerning two categorical variables can be displayed in a contingency table.

 Pass Did Not Pass Total Men 6 9 15 Women 10 15 25 Total 16 24 40

If gender and passing are independent, then the probability of passing will not change if a case's gender is known. This could be written as $P(Pass) = P(Pass \mid Man)$.

$P(Pass) = \dfrac{16}{40} = 0.4$

$P(Pass \mid Man) = \dfrac{6}{15}=0.4$

In this sample, gender and passing are independent.

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