Statistics - Normal distribution

AI Thread Summary
The discussion revolves around confusion regarding the use of normal distribution tables, specifically the normal table versus the percentage points table. The individual is struggling with discrepancies between their answers and textbook solutions when calculating probabilities related to a normally distributed variable. They highlight a specific problem involving the duration of pregnancy in cows, where they need to find the mean and standard deviation based on given percentages. The contributor seeks clarification on which table to use for accurate calculations. Understanding the correct application of these tables is crucial for resolving the discrepancies in statistical problems.
repugno
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Greetings to all,

I have run into some extreme difficulty with this straightforward topic. My problem is that I can’t work out which tables to use. There is the normal table and then the percentage points table. When the question states that the probably under the curve is a percentage I would naturally use the percentage points table. However, this is where the problems arise. My answers are different from the textbooks answers, and then when I recalculate using the normal table my answers are right. Then my understanding changes, so only the normal table gets used even with the percentages. But yet again my answers are wrong, because I had to use the percentage table. Please, anyone, shine some light onto this dilemma. Thank you in advance.
 
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Can you supply a sample problem?

Doug
 
Thanks for the reply Doug. Here is one question which has the probabilities as percentages. For some reason I am not suppose to use the percentage points table. Apart from that the question is straightforward.

The duration of pregnancy of a certian breed of cow is normally distributed with mean \mu and standard deviation \sigma days. Only 2.5% of all pregnancies are shorter than 235 days and 15% are longer than 286 days.

Find the value of \mu and \sigma

Any help would be much appreciated, thank you.
 
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