# AGES

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## AGES

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1. AGES By Clay Tattersall And Paul Podlas

2. For our project we had chosen to take a look at married couples ages, for the husband and women, and compare them • Our Hypothesis • Ho: Husbands age > Wives age • Ha: Husbands age </= Wives age What?

3. The information is relevant because it can help companies check out information on their target audience. • Also can be used to see how many sugar momma’s there are or how rich a man is. Why?

4. We can check the average age for our partner in marriage could be in the future • Companies can see the average age of people married if there selling a product for a married couple. Why? cont.

5. For our project we gathered data in a simple method. • Who ever walked into our place of work, we would ask them if they were married, if yes we asked them their age and the age of their spouse, if no, we asked them there parents ages. • This was a very good method until one lady thought I (Clay) was hitting on her and another lady said she probably won’t be coming back because it was rude of me to ask her age How?

6. Husband Wife

7. Husbands age Wives age 998875543 |2| 34445568 98865 |3| 0223689 985310 |4| 01155788 72210 |5| 35 Stem Plot

8. We gathered the data up and compiled it into a chart and this is what we came up with • After testing our hypothesis we got that p=.2462 so it wasn’t statistically significant at the 5% level

9. A=1.2725 • B=.9141 • R=.9601 • Sd1=10.26 • Sd2=9.76 Some random info

10. It seem that most husbands and wives ages are fairly close and generally the husband is older but not by a lot, usually a close couple of years. What we found out

11. The data could be totally different in certain situations. The location effects the data a lot because the richer the area, then the most likely the younger the wife, and the richer the guy. (Hugh Hefner and SEAL for example) • I work at a pool store and not many young people take care of pools so the majority of my customers were fairly old. Lurking Variables