We will use the heart attack data as an example. If you have not worked with this data before you can find a description here. This is a very large data set and so is provided as zip files. (You may need a program such as winzip to unzip them). Available are plain text (with tabs separating entries), Excel. If possible, download and unzip in Excel of the heart attack data and open it in Excel.

We can find 2 by 2 table in Excel that gives a count of male and female who died in hospital or not.
For this select ** Data > PivotTable and PivotChart Report… > Next > Range:** select the columns ** SEX, DRG,
and DIED > Next> >** (tick on) **New worksheet > Layout… > **Drag the variable **DIED** and place it into **Row** >
Similarly drag the variable **SEX** and place it into **Column** > Now in data select one of the variable **DIED
or SEX** and place it into **Data > **Now double click on the button which immediately above the **Data > Count
> OK > Finish.** We get table as follow:

Count of DIED SEX DIED F M Grand Total 0 4298 7136 11434 1 767 643 1410 Grand Total 5065 7779 12844

We can not directly find proportion test in Excel. Here we want to test the proportions of male and female who died in hospital. From the above table we get the number of male and female. We will make one more table as follow:

X is number of patient who died in hospital & N is total number of patient.
First make a column **Sample proportion** next to N column. To find proportion of female died in hospital,
suppose we have number of female died in hospital 767 is in cell B2 and total number of
female 5065 in C2. First click on the first cell of **Sample proportion** and type
"=B2/C2" and press ‘Enter’. We get p_{0} the proportion of females died in hospital. Similarly find
the proportion of males (p_{1}).

A | B | C | D | |

1 | SEX |
X |
N |
Proportion |

2 | F | 767(X_{0}) |
5065 (N_{0}) |
0.1514 (p_{0}) |

3 | M | 643(X_{1}) |
7779 (N_{1}) |
0.0826 (p_{1}) |

**To calculate the test statistic we have formula:**

z = (p_{0}) - (p_{1}) / sqrt ( (P*(1-P)/(N_{0}))+ (P*(1-P)/(N_{1})))

where P is pooled proportion

P = [(X_{0}) + (X_{0})]/[(N_{0})+(N_{1})]

Now we have to find difference in the proportion (p_{0} - p_{1}),
for this we have to select one cell (say cell (D4)) and

type "=D2-D3" and press ‘Enter’. For pooled proportion P we have to write "=(B2+B3)/(C2+C3)" and press ‘Enter’.Suppose
we have this value in cell (D5).

Now we will find z-value in cell say D6, we have formula for z-value "=D4/SQRT((D5*(1-D5)/C2)+(D5*(1-D5)/C3))" and press ‘Enter’.

So finally we get :
Difference = 0.0688

P = 0.1097

Z = 12.1845

A | B | C | D | |

1 | SEX |
X |
N |
Proportion |

2 | F | 767(X_{0}) |
5065 (N_{0}) |
0.1514 (p_{0}) |

3 | M | 643(X_{1}) |
7779 (N_{1}) |
0.0826 (p_{1}) |

4 | 0.0688 | |||

5 | 0.1097 | |||

6 | 12.1845 |

**Next step is to calculate P-value:**

Note that using excel we can calculate left tailed P-value directly.

For that select **Insert > Functions > (Function category:) Statistical > (Function name:)
NORMSDIST >** select D6 in **z** > OK. The left tailed P-value is 1.

Thus, two tailed P-value = 2 * P(z > 12.1845) =2 * (1 - P (z < 12.1845)) = 0.0000

The small P-value indicates that, we cannot regard this as a random sample from a population with an average death rate of 10%.

P-Value = 0.0000Since p-value = 0.0000 which is less than 0.05 so we reject H_{0} at 5% level of
significance.

**This implies that there is difference between proportion of males and females
died in hospital.**