** This will Analyze any 10 Question Survey **
The Chi-Square 95% Point = 3.841
If the Chi-Square Number is
Less Than 3.841 => There is no Gender Influence For that Question! (No)
If the Chi-Square Number is
More Than 3.841 => There is Gender Influence For that Question! (Yes)
Of course you could choose any two things to analyze with your survey
Like Democrat vs Republican or Texan vs Floridian etc.

Random Numbers => ** Calculate Results => **

************ Male **** Female ****
** A1 => ** Chi-Square =>
** B1 => **** Yes or No =>

** A2 => ** Chi-Square =>
** B2 => **** Yes or No =>

** A3 => ** Chi-Square =>
** B3 => **** Yes or No =>

** A4 => ** Chi-Square =>
** B4 => **** Yes or No =>

** A5 => ** Chi-Square =>
** B5 => **** Yes or No =>

** A6 => ** Chi-Square =>
** B6 => **** Yes or No =>

** A7 => ** Chi-Square =>
** B7 => **** Yes or No =>

** A8 => ** Chi-Square =>
** B8 => **** Yes or No =>

** A9 => ** Chi-Square =>
** B9 => **** Yes or No =>

** A10 => ** Chi-Square =>
** B10 => **** Yes or No =>

Type in the Results of your Survey Above.
Then Click on the Analyze Button.
Save Your Results => Get Saved Results =>
Random Numbers => ** Calculate Results =>

A Yes on a Question indicates that Gender does influence the preference chosen for this item.!
(Example: Women prefer Coffee over Tea.! Or Men prefer Going Fishing over Going to a Movie.!)
(Example: Education influences How much a Person earns in a year.!)
(Example: Fertilizer A influences How many Tomatoes a Plant Produces.!)
The Chi-Square Statistic is one of the most useful Statistics for Surveys of any kind.!

** 10 Question Sample Survey **
The Initial Question Determines
What Your are Analyzing
(Male vs Female) or (Democrat vs Republican) or (Fertilizer A vs Fertilizer B) etc.

1) Which do you prefer:
A1) Coke
B1) Pepsi

2) Which do you prefer:
A2) Blue
B2) Green

3) Which do you prefer:
A3) A Hike thru the Woods
B3) A Boat Ride on a Lake

4) Which do you prefer:
A4) A Day at a Park with your kids
B4) A Day at the Mall with your kids

5) Which date do you prefer:
A5) Dinner and a Movie
B5) Hot-Dogs or Hamburgers at the Zoo

6) Which date do you prefer:
A6) Paddle Kayak up a River
B6) Ride a Speed Boat to a Fishing Area

7) Which do you prefer:
A7) Lasagna for Dinner
B7) Soup and Salad for Dinner

8) Which do you prefer:
A8) Coffee
B8) Tea

9) Which do you prefer:
A9) Popcorn
B9) Potato Chips

10) Which do you prefer:
A10) Chocolate Cake
B10) Lemon Meringue Pie


This is for taking surveys.
The Null hypothesis for the above
experiment is that the observed values
are close to the predicted values. The
alternative hypothesis is that they
are not close to the predicted values.
These hypotheses hold for all Chi-
square goodness of fit tests. Thus in
this case the null and alternative
hypotheses corresponds to:Null
hypothesis: The coin is fair
Alternative hypothesis: The coin is biased

The chi-square test for independence
is used to determine the relationship
between two variables of a sample. In
this context independence means that
the two factors are not related.
Typically in social science research,
we're interested in finding factors
which are related, e.g. education and
income, occupation and prestige, age
and voting behaviour.
Example: We want to know whether boys
or girls get into trouble more often
in school. Below is the table
documenting the frequency of boys and
girls who got into trouble in school
H1 = There is no relationship
Variables are Independent.
H2 = There is a relationship
Variables are dependent.
If Chi-Square is greater than
the 5% value in the table
=> H2 => 95% chance that there
is signaficant relationship.
Variables are dependent.
Degrees of Freedom = (Row - 1)*(Column -1)
This is a 2 by 2 Table for the Chi-Square Calculation!
Do you like Coke or Pepsi?

========> Coke*****Pepsi*****Totals
=> Male => === ====
***Female => === ====
**Totals ==> === ====

Chi-Square=> Degrees-of-Freedom =>
This is a 2 by 3 Table for the Chi-Square Calculation!
Do you like Coke or Pepsi or Orange-Crush?

========> Male*****Female*****Totals
**Coke => ****** ******
**Pepsi => ****** ******
Orange => ****** *****
**Totals ==> === ====

Chi-Square => Degrees-of-Freedom =>
This is a 3 by 3 Table for the Chi-Square Calculation.


Chi-Square => Degrees-of-Freedom =>
This is a 4 by 1 Table for the Chi-Square Goodness of Fit Calculation.
If you know what the percents are and type them in => Click gg4()
If each percent is calculated and equal Click gg5()

Observed ***
*Percents ***
Expected ***

Chi-Square => Degrees-of-Freedom =>

Hypothesis testing refers to the process of using statistical analysis to determine if the observed differences between two or more samples are due to random chance (as stated in the null hypothesis) or to true differences in the samples (as stated in the alternate hypothesis). A null hypothesis (H0) is a stated assumption that there is no difference in parameters (mean, variance, DPMO) for two or more populations. The alternate hypothesis (Ha) is a statement that the observed difference or relationship between two populations is real and not the result of chance or an error in sampling. Hypothesis testing is the process of using a variety of statistical tools to analyze data and, ultimately, to fail to reject or reject the null hypothesis. From a practical point of view, finding statistical evidence that the null hypothesis is false allows you to reject the null hypothesis and accept the alternate hypothesis.