Statistics - standard deviations, etc

In summary: M_i) = avg(X_i) + avg(Ei) = avg(X_i) + 0 var(M_i) = var(X_i) + var(Ei) = var(X_i) + sigma^2so the things you need to know are the average and variance of the student heights, and the standard deviation of your ruler.In summary, the conversation discusses the topic of statistics and its application in measuring the height of students at a university. It explores the concept of uncertainty and standard deviation, and poses questions related to the expected range of values, distribution of measurements, and the fraction of students taller than a given height. A solution is proposed using the concepts of random variables and Gaussian distribution.
  • #1
joriarty
62
0
Statistics. The bane of my existence... and then a bit of statistics "revision" rears its (in my opinion) ugly head in my quantum physics course and I have absolutely no idea what to do.

Homework Statement



1. Suppose you measure the height of all students at the University. You use a ruler which is only accurate to ±0.5 cm (perhaps varies with temperature, stretches, numbers hard to read – whatever).

(a) On a given measurement you record 172.0cm. What range of values would you expect to get if you repeated this measurement several times?

(b) Make a sketch of the distribution of a sample of about 20 measurements of this particular student.

(c) Suppose your result after measuring all students is (height)=162.7±15.3cm. How much of the
15.3 cm uncertainty is due to your ruler?

(d) What fraction of students are taller than 162.7cm?

(e) What fraction of students are taller than 178.0cm?

Homework Equations



σ2 = ⟨x2⟩ - ⟨x⟩2

The Attempt at a Solution



I don't really know where to start. It's over 3 years since I did any stats at school. All I can understand is that standard deviation (σ) is some measure of average error over a range of samples. I haven't been given a range of samples, all I know is the uncertainty of ± 0.5 cm. For the first question, the student might be 171.5 cm tall, and the 172 cm measured is at the far end of this uncertainty. How can I come up with an expected range of values if this measurement was repeated?
 
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  • #2
you could write each measurement (Mi) as the sum of 2 random variables are error (Ei), and the heights (Xi) you :

[tex] M_i = X_i + Ei[/tex]
then calculate the average & variance of N measurements assuming everything is independent
 

Related to Statistics - standard deviations, etc

What is the purpose of calculating standard deviation?

The standard deviation is a measure of how spread out a set of data is from the mean. It tells us how much the data values deviate from the average, and helps us understand the variability or dispersion of the data. It is an important tool in statistical analysis as it allows for comparison of data sets and identification of outliers.

How is standard deviation calculated?

The standard deviation is calculated by taking the square root of the variance. The variance is found by taking the sum of the squared differences between each data point and the mean, divided by the total number of data points. This value is then squared to get the standard deviation.

What is a normal distribution and how is it related to standard deviation?

A normal distribution is a statistical concept in which the data points are symmetrically distributed around the mean, resulting in a bell-shaped curve. The standard deviation plays a key role in this distribution, as it is used to determine the spread of the data and the likelihood of a data point falling within a certain range of values.

Can standard deviation be negative?

No, standard deviation cannot be negative. It is a measure of variability or dispersion, and as such, it cannot have a negative value. If a calculation results in a negative standard deviation, it is likely due to an error in the calculation or the data itself.

What other measures of variability are commonly used besides standard deviation?

Other measures of variability include range, variance, and interquartile range. Range is the difference between the highest and lowest values in a data set. Variance is the average of the squared differences between each data point and the mean. Interquartile range is the difference between the 75th and 25th percentiles in a data set.

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