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Did the student identify the correct population, given the dataset they selected?

Please create personalized and substantive responses to at least two student main posts. In your response, include the following.  

 

1.Did the student identify the correct population, given the dataset they selected? Why or why not? If the population is not correct, please note what you think it should be. 

 

2. Did the student choose and identify one quantitative and qualitative variable? How can you tell that that their selections are correct. 

 

3. Which variable did the student select to evaluate level of measurement and what was the level of measurement selected? Is this level correct? How can you tell? If not, what is the correct level for the variable and why?

 

Student 1 post

 

What is the name of the dataset?

            Candy

What does each observation (row) in the dataset represent?

            Color, weight, ranking, production

Each dataset is a sample from a population. What population does your dataset represent?

            The data set of CANDY is a sample of population.

What does each column in the dataset represent? (List and describe each variable)

            Color- the different colors of the candy

            Weight – the weight of the candy

            Ranking- How the colors are ranked (1-4)

            Production- which day the candy was made

Give the name of one qualitative variable and one quantitative variable from the data set. Explain how you can tell that a variable is qualitative or quantitative.

            The COLOR of the candy is a qualitative variable

            The WEIGHT of the candy is a quantitative variable

            A qualitative is not numerical

            A quantitative is numerical

Choose one of the variables from your dataset and classify it according to the “levels of measurement” (nominal, ordinal, interval, or ratio). Explain how you know.

            Color falls under the nominal level. The reason is it consists of non-numerical and they cannot be put in order or ranked.

            Weight falls under the Ratio level. The reason is the weight is able to measure.  

            Preference ranking falls under interval level. The reason is the intervals are meaningful but the ratio is not.

            Day of Production falls under ordinal level. The reason is the days can be put in order.

 

 

Student 2 post

 

I chose the Tobacco In Movies database.  I found this to be amazing!  I have seen most of these movies and have never noticed how much smoking is in them.  Each row represents the movie that is seen.  The population of this chart is movies.  The sample population is Disney Movies.  Each column represents the name of movie, length, how many times there is tobacco use, and how many times there is alcohol use.  Each of these are different variables as they describe different ideas of the movie.  The data varies in each movie,  Some have no drinking or drug use while others have a lot.  Qualitative is data that can be expressed.  An example would be the number of times there is alcohol use in the movie.  Quantitative data is measured by it's quality not it's quantity.  An example would be the name of the Disney Movie.  The title of the movie is "nominal" data.  It is only words and cannot be added together.  It is just data.  There is no way to analyze it. 

Jennie Laing

 

 

Discussion 2

 

Choose any Excel dataset from class to work with:

1. What is the name of your dataset?

2. Choose and write down the name of any quantitative and continuous variable from your dataset and use Excel to calculate the mean, median, mode, min, max, range, variance, and standard deviation for that variable. Paste the results into your main post (or use Add/Remove to attach them).

a. Which measure of center (mean, median, or mode) does the best job in describing your variable and why?

b. Which measure of center (mean, median, or mode) does the worst job in describing your variable and why?

c. Include (type in) the value 50000 into your selected variable data. Now, recalculate the mean, median, and the variance for your variable (now that it includes the extra value of 50000). How did this change the mean, median, and variance from their original values? Is the change what you expected?

3. Choose any two quantitative variables from your dataset. Use three numerical measures to compare these two variables. Next, create a bar graph that includes the mean and median for both variables (to visualize their comparison).

 

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