Question

I want to investigate if aging has an effect on weight. I will use 10 people at between the ages of 40 and 80.Which method of statistical analysis would be appropriate to use to test the hypothesis? Give an example.

Answer 1

Sol:

build regression of weight on aging.

weight =a+b*age

perform Global F test

Ho;there is no effect of age on weight

Ha:There is an effect of age on weight,

Decsion rule

if p<0.05,reject Ho

if p>0.05,fail to reject Ho

From F statistic and p value ,

we can conlcude whether  aging has an effect on weight. or not

Answer 2

To investigate if aging has an effect on weight, you can use a statistical analysis known as correlation analysis. Specifically, you can perform a Pearson correlation analysis to examine the relationship between age and weight in your sample of 10 people.

The Pearson correlation coefficient measures the strength and direction of a linear relationship between two continuous variables, such as age and weight. The coefficient ranges from -1 to 1. A positive value indicates a positive correlation (as one variable increases, the other tends to increase), while a negative value indicates a negative correlation (as one variable increases, the other tends to decrease). A correlation coefficient close to 0 suggests no linear relationship between the two variables.

Here's an example of how you could conduct the analysis:

Let's say you have collected data on age and weight for 10 individuals between the ages of 40 and 80. The data looks like this:

Person	Age (years)	Weight (kg)
1	42	68
2	50	72
3	55	65
4	62	70
5	68	75
6	71	77
7	75	78
8	77	76
9	80	73
10	78	72

You can use statistical software, such as Python's scipy library or Microsoft Excel, to calculate the Pearson correlation coefficient between age and weight. The result will give you a correlation value between -1 and 1, which will indicate the strength and direction of the relationship between age and weight.

For example, if the calculated Pearson correlation coefficient is 0.8, it indicates a strong positive correlation, suggesting that as age increases, weight tends to increase. On the other hand, if the correlation coefficient is close to 0, it suggests that there is little to no linear relationship between age and weight.

Remember that correlation does not imply causation. Even if you find a significant correlation between age and weight in your sample, it does not necessarily mean that aging directly causes changes in weight. Correlation analysis only establishes the existence and strength of a relationship, but further research and experimentation would be needed to establish causation.