Question

project : analysis on the topic of use of statistics in real life based on real life examples

probability

Answer 1

Answer:

Project:

Analysis on the topic of the use of statistics in real life based on real-life examples.

Statistical analysis two examples:

(1).

Linear regression quantifies the relationship between one or more predictor variables and one outcome variable. Linear regression is used for predictive analysis and modeling.

The table below shows some data from the early days of the Italian clothing company Benetton. Each row in the table shows Benetton’s sales for a year and the amount spent on advertising that year. In this case, our outcome of interest is sales—it is what we want to predict. If we use advertising as the predictor variable, linear regression estimates that

Sales = 168 + 23 Advertising.

That is, if advertising expenditure is increased by one Euro, then sales will be expected to increase by 23 million Euros, and if there was no advertising we would expect sales of 168 million Euros.

Year	Sales (Million Euro)	Advertising (Million Euro)
1	651	23
2	762	26
3	856	30
4	1,063	34
5	1,190	43
6	1,298	48
7	1,421	52
8	1,440	57
9	1,518	58

(2).

t-test:

A t-test helps you compare whether two groups have different average values (for example, whether men and women have different average heights).

Example:

Let’s say you’re curious about whether New Yorkers and Kansans spend a different amount of money per month on movies. It’s impractical to ask every New Yorker and Kansan about their movie spending, so instead, you ask a sample of each—maybe 300 New Yorkers and 300 Kansans—and the averages are $14 and $18.

The t-test asks whether that difference is probably representative of a real difference between Kansans and New Yorkers generally or whether that is most likely a meaningless statistical fluke.

Technically, it asks the following:

If there were in fact no difference between Kansans and New Yorkers generally, what are the chances that randomly selected groups from those populations would be as different as these randomly selected groups are.

For example, if Kansans and New Yorkers as a whole actually spent the same amount of money on average, it’s very unlikely that 300 randomly selected Kansans each spends exactly $14 and 300 randomly selected New Yorkers each spend exactly $18.

So if you’re sampling yielded those results, you would conclude that the difference in the sample groups is most likely representative of a meaningful difference between the populations as a whole.

Thanks.