**1.** **Respond to the following in a minimum of 175 words:**

Let’s use the checkpoint. The 7,000 is in error. The correct chi square is 7.2.

Let’s discuss the checkpoint and Chi Square in general.

**2.** **Respond to classmate Lisa Raney to her DQ response. (90 words)**

Instructor and Class,

There are two types of chi-square tests and they are used for different purposes. A chi-square goodness of fit test determines if a sample data matches a population. A chi-square test for independence compares two variables in a contingency table to see if they are related. In general terms a chi-square tests to see whether distributions of categorical variables differ from each other. A very small chi-square test statistic means that your observation data fits your expected data extremely well and there is a relationship. A very large chi-square test statistic means that the data does not fit well and there is no relationship. A chi-square statistic is a way to show a relationship between two categorical variables. The two types of variables in statistics are numerical and non-numerical. The chi-square statistic is a single number that tells you how much difference exists between your observed counts and the counts you would expect if there were no relationship at all in the population. The chi-square statistic can only be used on numbers. For example if you have 10% of 200 before the chi-square test can be conducted the number has to be converted to the number 20. In short a chi-square statistic is used for testing hypotheses (Chi-Square Statistic, 2019).

I used the equation**: 16/10.0*6.0=9.6 and 4/10.0*-6.0=-2.4 9.6- -2.4=7.2 chi-square**

**Reference**

Chi-Square Statistic: How to Calculate It/Distribution(2019). Retrieved from http://www.statisticshowto.datasciencecentral.com/

**3.** **Respond to classmate ****Tammy Lives ****DQ response. (90 words)**

The point of the Chi-Square Test is to look at two variables and see if the variables are independent from each other or not. If they are independent from each other the factors do not correlate with each other. If they are not independent of one another than there is some correlation between the two. It can also be known as goodness of fit test that looks at the population of the data that was collected. A problem that can arise is the data itself and what two variables are being looked at it. Whether your sample population is big or small can alter even a little bit the results. The Chi-Square Test can tell us whether the two variables correlate with each other, not which variable can effect the other variable. Looking at the 7.2 chi square and an error of 7,000 we can tell that the degree of freedom is 1 and the significant value is 0.007. We know by this information that 0.007 is lower than the p value of .05 which means that we would reject the null hypothesis.

Reference

Jackson, S.L. (2017). Statistics Plain and Simple (4th ed.). Boston, MA. Cengage Learning