Difference in CHISQTEST and CHITEST (GROUP 9)

According to the official site of Microsoft there is no difference in the definitions of CHITEST and CHISQ.TEST.

Definition:

Returns the test for independence. It returns the value from the chi-squared (χ²) distribution for the statistic and the appropriate degrees of freedom. You can use χ² tests to determine whether hypothesized results are verified by an experiment.

Syntax: (actual_range,expected_range)

Actual range is the range of data that contains observations to test against expected values.

Expected range is the range of data that contains the ratio of the product of row totals and column totals to the grand total.

Usage: Chi-square is commonly used to compare observed data with data expected to obtain according to a specific hypothesis.

What is Chi Test or Chi Square test: The Chi-Square distribution is the distribution of the sum of the squares of a set of normally distributed random variables. Its value stems from the fact that the sum of random variables from any distribution can be closely approximated by a normal distribution as the sum as samples size increases. Thus the test is widely applicable for all distributions.

A chi-squared test, also referred to as chi-square test or test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Also considered a chi-squared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-squared distribution as closely as desired by making the sample size large enough.


Sources:
http://office.microsoft.com/en-in/excel-help/chisq-test-function-HP010335674.aspx
http://en.wikipedia.org/wiki/Chi-squared_test
 

Z Score- Group 1 Arti Chandavarkar

Z-SCORE

In statistics, the standard score also called the z- score is the number of standard deviations an observation or datum is above the mean. Thus, a positive standard score represents a datum above the mean, while a negative standard score represents a datum below the mean. It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing.

The standard score of a raw score x is

where:

μ is the mean of the population;

σ is the standard deviation of the population.

Values of Z-scores:

   Has a value of 0, it is equal to the group mean.

    Is positive, it is above the group mean.

    Is negative, it is below the group mean.

    Is equal to +1, it is 1 Standard Deviation above the mean.

    Is equal to +2, it is 2 Standard Deviations above the mean.

    Is equal to -1, it is 1 Standard Deviation below the mean.

    Is equal to -2, it is 2 Standard Deviations below the mean.

    Is equal to +3, it is 3 Standard Deviations above the mean.

    Is equal to -3 it is 3 Standard Deviations below the mean.

Properties of Z-score:

1. Mean of Z-score is o and Standard deviation of Z-score is 1.

2. Z-score distribution for normal distribution is the same as the data distribution.

Practical applications of Z-score:

Z-scores enable us to compare scores on different kinds of measures of the same variable.

For example: Measurement of child nutrition

Nutritional deterioration and improvement of individuals can be monitored using Z-scores as long as the mean and standard deviation for a given height are known.

Sources:

http://fex.ennonline.net/1/practical

http://www.coedu.usf.edu/ychen/EDF6432/pdf/module11.pdf.

http://en.wikipedia.org/wiki/Standard_score.

http://statistics-help-for-students.com/What_are_Z_scores.htm#.UlI8GNJHKSo.