*Tuesday, November 18th, 2014*

The Chi square compares the observed data with the Null Hypothesis.

Chi square test looks at single set of data and Null Hypothesis.

- H
_{o}The*no difference*or*no association*hypothesis that shows no difference between observed and expected data. - H
_{1}This postulates that there is a difference between observed and experimental data.

Expected = row X col total / grand total

χ^{2} = sum ((observed – expected) /Expected)^{2}

χ^{2} and DF/degree of freedom gives the test statistic

A big difference between observed and expected results in a large test statistic (χ^{2}) and so leads to a rejection of the Null Hypothesis (H_{o})

**The greater the value of the test statistic, the greater the evidence against the Null hypothesis -leads to a smaller p -value**

“…The `p`-value is the area under the chi-square probability density function (pdf) curve to the right of the specified `χ`^{2} value…” http://www.di-mgt.com.au/chisquare-calculator.h

p value is the area to the right of the test statistic. The less the number (< 0.05) the more likely to reject the Null Hypothesis

http://www.stat.ucla.edu/~kcli/stat13/stat13-lecture14.pdf

Nice video explaining it all.

- Two sample t-test
- paired t test
- One sample t test
- chi square test statistic
- Derivation of the linear least squares

- Connecting to Google Analytics – Brighton PHP October 2013 on
- udacity Introduction to statistics on
- udacity Introduction to statistics on
- jasonbailey.net is up on
- jasonbailey.net is up on

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