ANALYZE 3 OF THESE COMMENTS ABOUT STATISTICS. WHAT CAN YOU ADD? DO YOU AGREE? WRITE ABOUT EACH ONE!

1) BERNIE

Interval scales are numerical scales in which intervals have the same interpretation.If we are using 0 to represent a male and 1 to represent a female then it can be misrepresented because interval scales are numeric scales. Ratio scales are very similar to interval scales and they both have measurements of data."Both interval and ratio scales allow us to do both inferential and descriptive analysis." (Tanner & Youssef-Morgan, 2013) At work we classify our contractors under labels to make sure they have all their proper paperwork and if things are missing we cannot use them.Contractors have to send us updated information and every year we have to update their license expiration dates, if missed labeled the contractor can miss out on a potential job.I would have to say that I use nominal date at work.

Descriptive statistics are just figures that are on the overall performance, so if it not correct it can give false information. "The standard deviation changes this awkward measure to one that makes more intuitive sense." (Tanner & Youssef-Morgan, 2013)If I had to use this information in my personal life it would have to be for school. On an average there is an overall GAP for all students that are attending college. When we break down how if a students is receiving A's, B's, C' D's and F's that data changes and it is more conclusive to that individual.

Considering the salary income in our sample can change completely because previously it was known that males were the bread winners. Now that more women are having bigger roles in the work place the salaries are changing.So the probability of males and females paid equally for equal work can actually be true. At work all the clerical positions have three week off of work and they are mostly women. The plant managers that have to work those three weeks get paid more than the office clerks. The district wants to take away the three weeks but would have to change the pay of all the office clerks and they are not willing to do this. In order to make everyone happy they would need to give the three weeks to the plant managers and decrease their pay or increase the clerical positions and take way the three weeks and no one is willing to do this.

2) SHERRY

If we are using 0 to represent a male and 1 to represent a female then it can become confusing because of the two different scales.Interval scales is the same thing as a numerical scale but are similar to ratio scales.Both interval and ratio scales allow us to do both inferential and descriptive analysis (Tanner & Youssef-Morgan, 2013). On my job, we track all kinds of data.I would say we mostly use nominal data, because we are always looking at the number for projects.We have to figure out the dollar amount and how long it will take to complete the project.We also look at percentages and how it will affect the community.One simple mistake can throw the whole project off and can cause millions of dollars if not caught in time.We have a team of people looking at the same data to make sure it is inputted properly.

Descriptive statistics describes the overall collection of data information that was entered.What you have to be careful about is making sure the data is entered correctly so not to cause any conflicts or confusion.Excel does all the calculations one just needs to make sure they are input correctly.I think I would use this information mainly on my job.When dealing with infrastructure there are several data sheets that are created to track everything from beginning to the end of a project.I think we use location measures the most because of the mean, median and mode modules are in it.

In the past, males have been known to be the breadwinners of a family.They are the ones that made the most money and took control of the household.Today, some females make just as much or even more than males do.So the probability is that males and females are on the same level on most jobs.On my job, there are two top positions under the Mayor and one of them is currently held by a female. We have female as well as male customer service representatives.It basically dependent on your level of education and not your sex.When creating budgets for my agencies, we always consider the probability that the funding could decrease for the said year.Understanding the distribution of the data is an important element of understanding what the data is trying to tell us. Probabilities can give us a sense of the data set and allow us to compare results across groups. (Tanner & Youssef-Morgan, 2013).

3) AMANDA

Each way of classifying the data values are correct just presented differently. Neither way should be considered incorrect. The presentment can be misconstrued as gender bias considering males are listed first. Presenting the data in a nominal way does help analysts to point out exactly how many should exist within each group. Classifying data at the interval level adds the element of constant differences between sequential data points while a ratio level adds a "meaningful" 0 - which means the absence of any characteristic. If data is classified in this manner I don't foresee a problem arising that would cause errors in the classification. Each data characteristic and levels have their strong point depending on how used. In my professional life I believe women are rated lower than men. Not only are the wages less, but the authority standpoints as well. I don't think this specifically is a result from being labeled incorrectly, it's just our society.

Statistics are placed providing a mean or average value measuring a data set containing all the information regarding values. The mean is a location measure that is used in many statistical tests. Conducting a mean measure is a must when attempting to find an average. Although it is a great way of measuring data, an average test alone cannot create all results needed. Researchers have the option to take advantages of other measures as well. Variation is a great example, helping to determine the range, standard deviation, and variance. Including these measures along with location measures will help make a more informed judgement. In my professional life a measure that I come to find most influential and helpful would be the range and standard deviation. Using these measures of variation I will be able to find the difference between salaries for males and females for the same type of work.

A probability measure might not be completely accurate estimate, but it does allow a more open-minded estimate. The measure looks at the likelihood that a specific outcome will take place. The salary outcomes within our sample are probabilistic rather than accurate since being assumptions on how much each gender receives for a salary. To find a more accurate statistical outcome using the probability measure is not the best solution. In my professional life the salaries of individuals could be reviewed measuring if equal pay exists. This would be a probability measure if not specifically stated facts of how much individuals made in pay and compare that against each other. This would cause a huge ruckus I believe, not only are we not supposed to know what each employee makes unless they are your subordinates, but finding out how wages relate compared to gender would be hard to swallow. There are many employees here who probably don't get paid for what they're worth, taking into account how much hard work of going above and beyond their regular jobs. In my place of work women are more caring and understanding when it comes to troubled customers, so sometimes putting a larger burden on females which doesn't get recognized when it comes to our paychecks.