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Basic directions on how to use Google Spreadsheets and Excel to calculate and display Confidence Intervals.

Confidence intervals using spreadsheets

In this section we will discuss techniques using spreadsheet for creating confidence intervals for a one population mean, population standard deviation known; a one population mean, population standard deviation unknown: and for a population proportion,

Confidence intervals formulas

You can set up a worksheet in Excel to compute the confidence interval be using one of the following formulas in Excel or Google Spreadsheet

Function Excel Formula Google Spreadsheet formula
Confidence interval for one population mean, population standard deviation known. =confidence.norm(alpha, standard deviation, number in sample) =confidence(alpha, standard deviation, number in sample)
Confidence interval for one population mean, population standard deviation unknown. =confidence.t(alpha, standard deviation, number in sample) No built in formula
Confidence interval for one population proportion. There is no built in formula. . . we will calculate using model below = p-hat±(critical value z-score*sqrt((p-hat*(1-p-hat)/n) There is no built in formula. . . we will calculate using model below = p-hat±(critical value z-score*sqrt((p-hat*(1-p-hat)/n)

Displaying confidence intervals:

To graph confidence intervals the “Statistics Online Computational Resources (SOCR)” (just as in the previous chapter) at http://socr.ucla.edu/htmls/SOCR_Distributions.html has in the dropdown menu for SOCR distribution the normal distribution and for a Student’s t-distribution. For a normal distribution, you will need to have your mean and standard deviation and again your right and left cut off values (which in this case will be your critical values). For a Student’s t-distribution, you will need to have degrees of freedom and again your right and left cut off values (which in this case will be your critical values). Below is a graph of the Student’s t-distribution. We have used the example from 8.6 Two column Model step by step example for this demonstration. The degrees of freedom were 14 and a 95% confidence interval. We used the t-table to determine the left and right cut off values. In this instance two tailed confidence interval of 95% with 14 degrees for freedom is minus and plus 2.145.

This next example is using the normal distribution for determining the confidence interval for a population proportion. The normal density curve here has the population proportion as the mean (p or p-hat) and the standard deviation (the square root of (p(1-p)/n). We have demonstrated the example 8.8 to show you how this looks in SOCR. For this problem the mean proportion was .842 and the critical z-value for a 95% confidence interval was ± 0.032 or (0.81, 0.874).

Optional classroom exercise:

At your computer, try to use some of these tools to work out your homework problems or check homework that you have completed to see if the results are the same or similar.

Questions & Answers

how do they get the third part x = (32)5/4
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ninjadapaul
20/(×-6^2)
Salomon
okay, so you have 6 raised to the power of 2. what is that part of your answer
ninjadapaul
I don't understand what the A with approx sign and the boxed x mean
ninjadapaul
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
Salomon
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I got X =-6
Salomon
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
ninjadapaul
oops. ignore that.
ninjadapaul
so you not have an equal sign anywhere in the original equation?
ninjadapaul
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Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
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In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
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At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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Source:  OpenStax, Collaborative statistics using spreadsheets. OpenStax CNX. Jan 05, 2016 Download for free at http://legacy.cnx.org/content/col11521/1.23
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